{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T16:10:15Z","timestamp":1774368615111,"version":"3.50.1"},"reference-count":141,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T00:00:00Z","timestamp":1737676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Alzheimer\u2019s disease (AD) is a progressive neurodegenerative disorder that significantly impairs cognitive functions, leading to memory loss and other behavioral changes. It is the seventh leading cause of death worldwide, with millions of people affected. Early and accurate detection of AD is critical for improving patient outcomes and slowing disease progression. Recent advancements in machine learning (ML) and deep learning (DL) models have demonstrated significant potential for detecting AD using patient\u2019s speech signals, as subtle changes in speech patterns, such as reduced fluency, pronunciation difficulties, and cognitive decline, can serve as early indicators of the disease, offering a non-invasive and cost-effective method for early diagnosis. This survey paper provides a comprehensive review of the current literature on the application of ML and DL techniques for AD detection through the analysis of a patient\u2019s speech signal, utilizing various acoustic and textual features. Moreover, it offers an overview of the changes in the brain caused by the disease, associated risk factors, publicly available datasets, and future directions for leveraging ML and DL in the detection of AD.<\/jats:p>","DOI":"10.3390\/computers14020036","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T05:24:31Z","timestamp":1737696271000},"page":"36","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Machine Learning Approaches for Speech-Based Alzheimer\u2019s Detection: A Comprehensive Survey"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6838-8211","authenticated-orcid":false,"given":"Ahmed","family":"Sharafeldeen","sequence":"first","affiliation":[{"name":"Computer Science and Engineering Department, University of Louisville, Louisville, KY 40292, USA"},{"name":"Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt"}]},{"given":"Justin","family":"Keowen","sequence":"additional","affiliation":[{"name":"Mathematics and Computer Science Department, Louisiana State University of Alexandria, Alexandria, LA 71302, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6557-6292","authenticated-orcid":false,"given":"Ahmed","family":"Shaffie","sequence":"additional","affiliation":[{"name":"Mathematics and Computer Science Department, Louisiana State University of Alexandria, Alexandria, LA 71302, USA"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"(2023). 2023 Alzheimer\u2019s disease facts and figures. Alzheimer\u2019s Dement., 19, 1598\u20131695.","DOI":"10.1002\/alz.13016"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1212\/WNL.0000000000001096","article-title":"Factors associated with cognitive evaluations in the United States","volume":"84","author":"Kotagal","year":"2015","journal-title":"Neurology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1356","DOI":"10.1212\/01.WNL.0000094327.68399.59","article-title":"Rate of cognitive decline and mortality in alzheimer\u2019s disease","volume":"61","author":"Hui","year":"2003","journal-title":"Neurology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"23780","DOI":"10.1039\/C8RA03620A","article-title":"Current progress, challenges and future prospects of Diagnostic and therapeutic interventions in alzheimer\u2019s disease","volume":"8","author":"Rajasekhar","year":"2018","journal-title":"RSC Adv."},{"key":"ref_5","unstructured":"Alzheimer\u2019s Asscociation (2024, August 22). 2024 Alzheimer\u2019s Disease Facts and Figures. Available online: https:\/\/www.alz.org\/media\/documents\/alzheimers-facts-and-figures.pdf."},{"key":"ref_6","unstructured":"Alzheimer\u2019s Disease International (2024, August 22). World Alzheimer Report 2023. Available online: https:\/\/www.alzint.org\/u\/World-Alzheimer-Report-2023.pdf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3141","DOI":"10.2174\/0929867323666161213101126","article-title":"Alzheimer\u2019s disease: A review from the pathophysiology to diagnosis, new perspectives for pharmacological treatment","volume":"25","author":"Ozela","year":"2018","journal-title":"Curr. Med. Chem."},{"key":"ref_8","first-page":"972685","article-title":"Diagnostic tests for alzheimer\u2019s disease: Rationale, methodology, and challenges","volume":"2010","author":"Mason","year":"2010","journal-title":"Int. J. Alzheimer\u2019s Dis."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Karimi, L., Mahboub-Ahari, A., Jahangiry, L., Sadeghi-Bazargani, H., and Farahbakhsh, M. (2022). A systematic review and meta-analysis of studies on screening for mild cognitive impairment in primary healthcare. BMC Psychiatry, 22.","DOI":"10.1186\/s12888-022-03730-8"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s13024-019-0333-5","article-title":"The neuropathological diagnosis of alzheimer\u2019s disease","volume":"14","author":"DeTure","year":"2019","journal-title":"Mol. Neurodegener."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1038\/s41380-018-0067-8","article-title":"Changes in functional and structural brain connectome along the Alzheimer\u2019s Disease Continuum","volume":"25","author":"Filippi","year":"2018","journal-title":"Mol. Psychiatry"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"15056","DOI":"10.1038\/nrdp.2015.56","article-title":"Alzheimer\u2019s disease","volume":"1","author":"Masters","year":"2015","journal-title":"Nat. Rev. Dis. Prim."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1038\/aps.2017.28","article-title":"Amyloid beta: Structure, biology and structure-based therapeutic development","volume":"38","author":"Chen","year":"2017","journal-title":"Acta Pharmacol. Sin."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"313","DOI":"10.14283\/jpad.2021.15","article-title":"The epidemiology of alzheimer\u2019s disease modifiable risk factors and prevention","volume":"8","author":"Zhang","year":"2021","journal-title":"J. Prev. Alzheimer\u2019s Dis."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1016\/S0140-6736(10)61349-9","article-title":"Alzheimer\u2019s disease","volume":"377","author":"Ballard","year":"2011","journal-title":"Lancet"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"87","DOI":"10.5114\/fn.2019.85929","article-title":"Risk factors for alzheimer\u2019s disease","volume":"57","author":"Armstrong","year":"2019","journal-title":"Folia Neuropathol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Andrade-Guerrero, J., Santiago-Balmaseda, A., Jeronimo-Aguilar, P., Vargas-Rodr\u00edguez, I., Cadena-Su\u00e1rez, A.R., S\u00e1nchez-Garibay, C., Pozo-Molina, G., M\u00e9ndez-Catal\u00e1, C.F., Cardenas-Aguayo, M.d.C., and Diaz-Cintra, S. (2023). Alzheimer\u2019s disease: An updated overview of its genetics. Int. J. Mol. Sci., 24.","DOI":"10.3390\/ijms24043754"},{"key":"ref_18","unstructured":"Pitt, M. (2024, September 06). Pitt Corpus. Available online: https:\/\/dementia.talkbank.org\/access\/English\/Pitt.html."},{"key":"ref_19","unstructured":"Luz, S. (2024, September 06). ADReSS Challenge. Available online: https:\/\/luzs.gitlab.io\/adress."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Haulcy, R., and Glass, J. (2021). Classifying alzheimer\u2019s disease using audio and text-based representations of Speech. Front. Psychol., 11.","DOI":"10.3389\/fpsyg.2020.624137"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Luz, S., Haider, F., de la Fuente, S., Fromm, D., and MacWhinney, B. (2021). Detecting cognitive decline using speech only: The adresso challenge 2021. arXiv.","DOI":"10.21437\/Interspeech.2021-1220"},{"key":"ref_22","unstructured":"CCC (2024, September 07). Carolinas Conversations Collection. Available online: https:\/\/carolinaconversations.musc.edu\/ccc\/about\/."},{"key":"ref_23","first-page":"143","article-title":"Finding a balance: The carolinas conversation collection","volume":"7","author":"Pope","year":"2011","journal-title":"Corpus Linguist. Linguist. Theory"},{"key":"ref_24","unstructured":"Hauser, R.M., and Sewell, W. (2005). Wisconsin Longitudinal Study (WLS) [Graduates, Siblings, and Spouses], University of Wisconsin-Madison."},{"key":"ref_25","unstructured":"Karakostas, A., Briassouli, A., Avgerinakis, K., Kompatsiaris, I., and Tsolaki, M. (2016). The dem@ care experiments and datasets: A technical report. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1001\/archneur.1994.00540180063015","article-title":"The natural history of alzheimer\u2019s disease","volume":"51","author":"Becker","year":"1994","journal-title":"Arch. Neurol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ben Ammar, R., and Ben Ayed, Y. (November, January 28). Speech Processing for Early Alzheimer Disease Diagnosis: Machine Learning Based Approach. Proceedings of the 2018 IEEE\/ACS 15th International Conference on Computer Systems and Applications (AICCSA), Aqaba, Jordan.","DOI":"10.1109\/AICCSA.2018.8612831"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Orimaye, S.O., Wong, J.S.M., and Golden, K.J. (2014, January 27). Learning predictive linguistic features for Alzheimer\u2019s disease and related dementias using verbal utterances. Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, Baltimore, MD, USA.","DOI":"10.3115\/v1\/W14-3210"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1017\/S1041610210001092","article-title":"Microlinguistic aspects of the oral narrative in patients with Alzheimer\u2019s disease","volume":"23","author":"Ortiz","year":"2011","journal-title":"Int. Psychogeriatr."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Clarke, N., Barrick, T.R., and Garrard, P. (2021). A Comparison of Connected Speech Tasks for Detecting Early Alzheimer\u2019s Disease and Mild Cognitive Impairment Using Natural Language Processing and Machine Learning. Front. Comput. Sci., 3.","DOI":"10.3389\/fcomp.2021.634360"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e33460","DOI":"10.2196\/33460","article-title":"Evaluating Web-Based Automatic Transcription for Alzheimer Speech Data: Transcript Comparison and Machine Learning Analysis","volume":"5","author":"Soroski","year":"2022","journal-title":"JMIR Aging"},{"key":"ref_32","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102761","DOI":"10.1016\/j.ijhcs.2021.102761","article-title":"Exploiting linguistic information from Nepali transcripts for early detection of Alzheimer\u2019s disease using natural language processing and machine learning techniques","volume":"160","author":"Adhikari","year":"2022","journal-title":"Int. J. Hum.-Comput. Stud."},{"key":"ref_34","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., and Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_35","unstructured":"Grave, E., Bojanowski, P., Gupta, P., Joulin, A., and Mikolov, T. (2018). Learning word vectors for 157 languages. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics.","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","article-title":"Framewise phoneme classification with bidirectional LSTM and other neural network architectures","volume":"18","author":"Graves","year":"2005","journal-title":"Neural Netw."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"832","DOI":"10.3390\/make1030048","article-title":"A CNN-BiLSTM Model for Document-Level Sentiment Analysis","volume":"1","author":"Rhanoui","year":"2019","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Adhikari, S., Thapa, S., Singh, P., Huo, H., Bharathy, G., and Prasad, M. (2021, January 18\u201322). A Comparative Study of Machine Learning and NLP Techniques for Uses of Stop Words by Patients in Diagnosis of Alzheimer\u2019s Disease. Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN) IEEE, Shenzhen, China.","DOI":"10.1109\/IJCNN52387.2021.9534449"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"102023","DOI":"10.1016\/j.simpat.2019.102023","article-title":"A new machine learning method for identifying Alzheimer\u2019s disease","volume":"99","author":"Liu","year":"2020","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1007\/s10772-023-10056-7","article-title":"A speech based diagnostic method for Alzheimer disease using machine learning","volume":"26","author":"Begam","year":"2023","journal-title":"Int. J. Speech Technol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1186\/s13195-024-01394-y","article-title":"Unveiling the sound of the cognitive status: Machine Learning-based speech analysis in the Alzheimer\u2019s disease spectrum","volume":"16","author":"Alegret","year":"2024","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chen, X., Pu, Y., Li, J., and Zhang, W.Q. (2023, January 4\u201310). Cross-Lingual Alzheimer\u2019s Disease Detection Based on Paralinguistic and Pre-Trained Features. Proceedings of the ICASSP 2023\u20142023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece.","DOI":"10.1109\/ICASSP49357.2023.10095522"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Conneau, A., Baevski, A., Collobert, R., Mohamed, A., and Auli, M. (2020). Unsupervised Cross-lingual Representation Learning for Speech Recognition. arXiv.","DOI":"10.21437\/Interspeech.2021-329"},{"key":"ref_45","unstructured":"Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., and Sutskever, I. (2022). Robust Speech Recognition via Large-Scale Weak Supervision. arXiv."},{"key":"ref_46","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"88377","DOI":"10.1109\/ACCESS.2021.3090321","article-title":"Automated Recognition of Alzheimer\u2019s Dementia Using Bag-of-Deep-Features and Model Ensembling","volume":"9","author":"Syed","year":"2021","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Horwitz, R., Quatieri, T.F., Helfer, B.S., Yu, B., Williamson, J.R., and Mundt, J. (2013, January 6\u20139). On the relative importance of vocal source, system, and prosody in human depression. Proceedings of the 2013 IEEE International Conference on Body Sensor Networks, Cambridge, MA, USA.","DOI":"10.1109\/BSN.2013.6575522"},{"key":"ref_49","unstructured":"(2024, October 11). YAMNet. Available online: https:\/\/github.com\/tensorflow\/models\/tree\/master\/research\/audioset\/yamnet."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Hershey, S., Chaudhuri, S., Ellis, D.P., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., and Seybold, B. (2017, January 5\u20139). CNN architectures for large-scale audio classification. Proceedings of the 2017 IEEE International Conference on aAcoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7952132"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Cramer, A.L., Wu, H.H., Salamon, J., and Bello, J.P. (2019, January 12\u201317). Look, Listen, and Learn More: Design Choices for Deep Audio Embeddings. Proceedings of the ICASSP 2019\u20142019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8682475"},{"key":"ref_52","unstructured":"Schmitt, M., Marchi, E., Ringeval, F., and Schuller, B. (2016, January 5\u20137). Towards Cross-lingual Automatic Diagnosis of Autism Spectrum Condition in Children\u2019s Voices. Proceedings of the Speech Communication, 12. ITG Symposium, Paderborn, Germany."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Perronnin, F., and Dance, C. (2007, January 17\u201322). Fisher Kernels on Visual Vocabularies for Image Categorization. Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383266"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Shah, Z., Sawalha, J., Tasnim, M., Qi, S.a., Stroulia, E., and Greiner, R. (2021). Learning Language and Acoustic Models for Identifying Alzheimer\u2019s Dementia From Speech. Front. Comput. Sci., 3.","DOI":"10.3389\/fcomp.2021.624659"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Shah, Z., Qi, S.A., Wang, F., Farrokh, M., Tasnim, M., Stroulia, E., Greiner, R., Plitsis, M., and Katsamanis, A. (2023, January 4\u201310). Exploring Language-Agnostic Speech Representations Using Domain Knowledge for Detecting Alzheimer\u2019s Dementia. Proceedings of the ICASSP 2023\u20142023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece.","DOI":"10.1109\/ICASSP49357.2023.10095593"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Parsapoor, M., Alam, M.R., and Mihailidis, A. (2023). Performance of machine learning algorithms for dementia assessment: Impacts of language tasks, recording media, and modalities. BMC Med. Inform. Decis. Mak., 23.","DOI":"10.1186\/s12911-023-02122-6"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Vats, N.A., Yadavalli, A., Gurugubelli, K., and Vuppala, A.K. (2021, January 5\u20137). Acoustic Features, Bert Model and Their Complementary Nature for Alzheimer\u2019s Dementia Detection. Proceedings of the 2021 Thirteenth International Conference on Contemporary Computing (IC3-2021), Noida, India. IC3 \u201921.","DOI":"10.1145\/3474124.3474162"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Ablimit, A., Botelho, C., Abad, A., Schultz, T., and Trancoso, I. (2022, January 23\u201327). Exploring Dementia Detection from Speech: Cross Corpus Analysis. Proceedings of the ICASSP 2022\u20142022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore.","DOI":"10.1109\/ICASSP43922.2022.9747167"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Weiner, J., Herff, C., and Schultz, T. (2016, January 8\u201312). Speech-Based Detection of Alzheimer\u2019s Disease in Conversational German. Proceedings of the Interspeech, San Francisco, CA, USA.","DOI":"10.21437\/Interspeech.2016-100"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Eyben, F., Weninger, F., Gross, F., and Schuller, B. (2013, January 21\u201325). Recent developments in openSMILE, the munich open-source multimedia feature extractor. Proceedings of the 21st ACM international conference on Multimedia, Barcelona, Spain. MM \u201913.","DOI":"10.1145\/2502081.2502224"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1109\/TASL.2010.2064307","article-title":"Front-End Factor Analysis for Speaker Verification","volume":"19","author":"Dehak","year":"2011","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Desplanques, B., Thienpondt, J., and Demuynck, K. (2020, January 25\u201329). ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification. Proceedings of the Interspeech 2020, Shanghai, China.","DOI":"10.21437\/Interspeech.2020-2650"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Sattler, C., Wahl, H.W., Schr\u00f6der, J., Kruse, A., Sch\u00f6nknecht, P., Kunzmann, U., and Zenth\u00f6fer, A. (2015). Interdisciplinary longitudinal study on adult development and aging (ILSE). Encyclopedia of Geropsychology, Springer.","DOI":"10.1007\/978-981-287-080-3_238-1"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Liu, N., Luo, K., Yuan, Z., and Chen, Y. (2022). A Transfer Learning Method for Detecting Alzheimer\u2019s Disease Based on Speech and Natural Language Processing. Front. Public Health, 10.","DOI":"10.3389\/fpubh.2022.772592"},{"key":"ref_65","unstructured":"Sanh, V., Debut, L., Chaumond, J., and Wolf, T. (2019). DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter. arXiv."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"4153","DOI":"10.1109\/JBHI.2022.3172479","article-title":"Explainable Identification of Dementia from Transcripts Using Transformer Networks","volume":"26","author":"Ilias","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_67","unstructured":"Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., and Soricut, R. (2019). ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. arXiv."},{"key":"ref_68","first-page":"12837","article-title":"Convbert: Improving bert with span-based dynamic convolution","volume":"33","author":"Jiang","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_69","unstructured":"Rumshisky, A., Roberts, K., Bethard, S., and Naumann, T. (2019, January 7). Publicly Available Clinical BERT Embeddings. Proceedings of the 2nd Clinical Natural Language Processing Workshop, Minneapolis, MN, USA."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","article-title":"BioBERT: A pre-trained biomedical language representation model for biomedical text mining","volume":"36","author":"Lee","year":"2019","journal-title":"Bioinformatics"},{"key":"ref_71","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., and Le, Q.V. (2019). XLNet: Generalized Autoregressive Pretraining for Language Understanding. arXiv."},{"key":"ref_72","unstructured":"Lu, J., Yang, J., Batra, D., and Parikh, D. (2016). Hierarchical question-image co-attention for visual question answering. Adv. Neural Inf. Process. Syst., 29."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016, January 13\u201317). \u201cWhy should I trust you?\u201d Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939778"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Nambiar, A.S., Likhita, K., Pujya, K.V.S.S., Gupta, D., Vekkot, S., and Lalitha, S. (2022, January 24\u201326). Comparative study of Deep Classifiers for Early Dementia Detection using Speech Transcripts. Proceedings of the 2022 IEEE 19th India Council International Conference (INDICON), Kochi, India.","DOI":"10.1109\/INDICON56171.2022.10039705"},{"key":"ref_75","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv."},{"key":"ref_76","unstructured":"Le, Q.V., and Mikolov, T. (2014). Distributed Representations of Sentences and Documents. arXiv."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., and Manning, C. (2014, January 25\u201329). GloVe: Global Vectors for Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1162"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"92294","DOI":"10.1109\/ACCESS.2022.3203068","article-title":"An Evaluation on Information Composition in Dementia Detection Based on Speech","volume":"10","author":"Zheng","year":"2022","journal-title":"IEEE Access"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Wankerl, S., N\u00f6th, E., and Evert, S. (2017, January 20\u201324). An N-Gram Based Approach to the Automatic Diagnosis of Alzheimer\u2019s Disease from Spoken Language. Proceedings of the Interspeech, Stockholm, Sweden.","DOI":"10.21437\/Interspeech.2017-1572"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Howard, J., and Ruder, S. (2018). Universal Language Model Fine-tuning for Text Classification. arXiv.","DOI":"10.18653\/v1\/P18-1031"},{"key":"ref_81","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). Attention is All you Need. Proceedings of the Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Ahn, K., Cho, M., Kim, S.W., Lee, K.E., Song, Y., Yoo, S., Jeon, S.Y., Kim, J.L., Yoon, D.H., and Kong, H.J. (2023). Deep Learning of Speech Data for Early Detection of Alzheimer\u2019s Disease in the Elderly. Bioengineering, 10.","DOI":"10.3390\/bioengineering10091093"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Kim, T.M., Son, J., Chun, J.W., Lee, Y., Kim, D.J., Choi, I.Y., Ko, T., and Choi, S. (2024). Comparison of AI with and without hand-crafted features to classify Alzheimer\u2019s disease in different languages. Comput. Biol. Med., 180.","DOI":"10.1016\/j.compbiomed.2024.108950"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation Applied to Handwritten Zip Code Recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_85","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1109\/TPAMI.2016.2599174","article-title":"Long-Term Recurrent Convolutional Networks for Visual Recognition and Description","volume":"39","author":"Donahue","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Snyder, D., Garcia-Romero, D., Sell, G., Povey, D., and Khudanpur, S. (2018, January 15\u201320). X-Vectors: Robust DNN Embeddings for Speaker Recognition. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8461375"},{"key":"ref_88","unstructured":"Ravanelli, M., Parcollet, T., Plantinga, P., Rouhe, A., Cornell, S., Lugosch, L., Subakan, C., Dawalatabad, N., Heba, A., and Zhong, J. (2021). SpeechBrain: A General-Purpose Speech Toolkit. arXiv."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Chau, H.H.H., Chau, Y., Wang, H.L., Chuang, Y.F., and Lee, C.C. (2022, January 27\u201329). MCI Detection Based on Deep Learning with Voice Spectrogram. Proceedings of the 2022 IEEE 4th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS), Tainan, Taiwan.","DOI":"10.1109\/ECBIOS54627.2022.9945032"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Vetrab, M., Egas-Lopez, J.V., Balogh, R., Imre, N., Hoffmann, I., Toth, L., Pakaski, M., Kalman, J., and Gosztolya, G. (2022, January 23\u201327). Using Spectral Sequence-to-Sequence Autoencoders to Assess Mild Cognitive Impairment. Proceedings of the ICASSP 2022\u20142022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore.","DOI":"10.1109\/ICASSP43922.2022.9746148"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3469089","article-title":"Automatic Speech Classifier for Mild Cognitive Impairment and Early Dementia","volume":"3","author":"Bertini","year":"2021","journal-title":"ACM Trans. Comput. Healthc."},{"key":"ref_92","unstructured":"Freitag, M., Amiriparian, S., Pugachevskiy, S., Cummins, N., and Schuller, B. (2017). auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks. arXiv."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Lin, S.Y., Chang, H.L., Hwang, J.J., Wai, T., Chang, Y.L., and Fu, L.C. (2022, January 9\u201312). Automatic Audio-based Screening System for Alzheimer\u2019s Disease Detection. Proceedings of the 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Prague, Czech Republic.","DOI":"10.1109\/SMC53654.2022.9945127"},{"key":"ref_94","unstructured":"Dauphin, Y.N., Fan, A., Auli, M., and Grangier, D. (2017, January 6\u201311). Language modeling with gated convolutional networks. Proceedings of the International Conference on Machine Learning, PMLR, Sydney, NSW, Australia."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Kumar, M.R., Vekkot, S., Lalitha, S., Gupta, D., Govindraj, V.J., Shaukat, K., Alotaibi, Y.A., and Zakariah, M. (2022). Dementia Detection from Speech Using Machine Learning and Deep Learning Architectures. Sensors, 22.","DOI":"10.3390\/s22239311"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"107519","DOI":"10.1016\/j.apacoust.2020.107519","article-title":"Investigation of multilingual and mixed-lingual emotion recognition using enhanced cues with data augmentation","volume":"170","author":"Lalitha","year":"2020","journal-title":"Appl. Acoust."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"19629","DOI":"10.1109\/ACCESS.2020.2968170","article-title":"Parallel Recurrent Convolutional Neural Networks-Based Music Genre Classification Method for Mobile Devices","volume":"8","author":"Yang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Chlasta, K., and Wo\u0142k, K. (2021). Towards Computer-Based Automated Screening of Dementia Through Spontaneous Speech. Front. Psychol., 11.","DOI":"10.3389\/fpsyg.2020.623237"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Chatzianastasis, M., Ilias, L., Askounis, D., and Vazirgiannis, M. (2023, January 4\u201310). Neural Architecture Search with Multimodal Fusion Methods for Diagnosing Dementia. Proceedings of the ICASSP 2023\u20142023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece.","DOI":"10.1109\/ICASSP49357.2023.10096579"},{"key":"ref_100","unstructured":"Liu, H., Simonyan, K., and Yang, Y. (2019, January 6\u20139). DARTS: Differentiable Architecture Search. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Ben-Younes, H., Cadene, R., Cord, M., and Thome, N. (2017, January 22\u201329). Mutan: Multimodal tucker fusion for visual question answering. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.285"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"5947","DOI":"10.1109\/TNNLS.2018.2817340","article-title":"Beyond bilinear: Generalized multimodal factorized high-order pooling for visual question answering","volume":"29","author":"Yu","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_103","unstructured":"Ben-Younes, H., Cadene, R., Thome, N., and Cord, M. (February, January 27). Block: Bilinear superdiagonal fusion for visual question answering and visual relationship detection. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Priyadarshinee, P., Clarke, C.J., Melechovsky, J., Lin, C.M.Y., B. T., B., and Chen, J.M. (2023). Alzheimer\u2019s Dementia Speech (Audio vs. Text): Multi-Modal Machine Learning at High vs. Low Resolution. Appl. Sci., 13.","DOI":"10.3390\/app13074244"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1109\/TAFFC.2015.2457417","article-title":"The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing","volume":"7","author":"Eyben","year":"2016","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Agbavor, F., and Liang, H. (2022). Predicting dementia from spontaneous speech using large language models. PLoS Digit. Health, 1.","DOI":"10.1371\/journal.pdig.0000168"},{"key":"ref_107","first-page":"12449","article-title":"wav2vec 2.0: A framework for self-supervised learning of speech representations","volume":"33","author":"Baevski","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"16029","DOI":"10.1007\/s10489-022-04255-z","article-title":"Multimodal fusion for alzheimer\u2019s disease recognition","volume":"53","author":"Ying","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Eyben, F., W\u00f6llmer, M., and Schuller, B. (2010, January 25\u201329). Opensmile: The munich versatile and fast open-source audio feature extractor. Proceedings of the 18th ACM International Conference on Multimedia, Firenze, Italy. MM \u201910.","DOI":"10.1145\/1873951.1874246"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Schuller, B., Steidl, S., Batliner, A., Burkhardt, F., Devillers, L., M\u00fcller, C., and Narayanan, S. (2010, January 26\u201330). The INTERSPEECH 2010 paralinguistic challenge. Proceedings of the INTERSPEECH 2010, Makuhari, Japan.","DOI":"10.21437\/Interspeech.2010-739"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Lenain, R., Weston, J., Shivkumar, A., and Fristed, E. (2020). Surfboard: Audio Feature Extraction for Modern Machine Learning. arXiv.","DOI":"10.21437\/Interspeech.2020-2879"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Torfi, A. (2018). SpeechPy-A Library for Speech Processing and Recognition. arXiv.","DOI":"10.21105\/joss.00749"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/j.neunet.2023.10.040","article-title":"A comparative study of GNN and MLP based machine learning for the diagnosis of Alzheimer\u2019s Disease involving data synthesis","volume":"169","author":"Chen","year":"2024","journal-title":"Neural Netw."},{"key":"ref_115","first-page":"101940","article-title":"Classification of Alzheimer\u2019s disease using MRI data based on Deep Learning Techniques","volume":"36","author":"Sorour","year":"2024","journal-title":"J. King Saud Univ.\u2014Comput. Inf. Sci."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-023-02461-1","article-title":"Brain MRI Image Analysis for Alzheimer\u2019s Disease (AD) Prediction Using Deep Learning Approaches","volume":"5","author":"Singh","year":"2024","journal-title":"SN Comput. Sci."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"3767","DOI":"10.1007\/s11042-023-15738-7","article-title":"A deep learning framework for early diagnosis of Alzheimer\u2019s disease on MRI images","volume":"83","author":"Arafa","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"2079","DOI":"10.1109\/JBHI.2024.3355329","article-title":"A Clinically Explainable AI-Based Grading System for Age-Related Macular Degeneration Using Optical Coherence Tomography","volume":"28","author":"Elsharkawy","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Sharafeldeen, A., Elgafi, M., Elnakib, A., Mahmoud, A., Elgarayhi, A., Alghamdi, N.S., Sallah, M., and El-Baz, A. (2023, January 18\u201321). Diabetic Retinopathy Detection Using 3D OCT Features. Proceedings of the 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), Cartagena, Colombia.","DOI":"10.1109\/ISBI53787.2023.10230785"},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Elsharkawy, M., Sharafeldeen, A., Soliman, A., Khalifa, F., Ghazal, M., El-Daydamony, E., Atwan, A., Sandhu, H.S., and El-Baz, A. (2022, January 28\u201331). Diabetic Retinopathy Diagnostic CAD System Using 3D-Oct Higher Order Spatial Appearance Model. Proceedings of the 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), Kolkata, India.","DOI":"10.1109\/ISBI52829.2022.9761508"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Haggag, S., Elnakib, A., Sharafeldeen, A., Elsharkawy, M., Khalifa, F., Farag, R.K., Mohamed, M.A., Sandhu, H.S., Mansoor, W., and Sewelam, A. (2022). A Computer-Aided Diagnostic System for Diabetic Retinopathy Based on Local and Global Extracted Features. Appl. Sci., 12.","DOI":"10.3390\/app12168326"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Elgafi, M., Sharafeldeen, A., Elnakib, A., Elgarayhi, A., Alghamdi, N.S., Sallah, M., and El-Baz, A. (2022). Detection of Diabetic Retinopathy Using Extracted 3D Features from OCT Images. Sensors, 22.","DOI":"10.3390\/s22207833"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Farahat, I.S., Sharafeldeen, A., Ghazal, M., Alghamdi, N.S., Mahmoud, A., Connelly, J., van Bogaert, E., Zia, H., Tahtouh, T., and Aladrousy, W. (2024). An AI-based novel system for predicting respiratory support in COVID-19 patients through CT imaging analysis. Sci. Rep., 14.","DOI":"10.1038\/s41598-023-51053-9"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Fahmy, D., Kandil, H., Khelifi, A., Yaghi, M., Ghazal, M., Sharafeldeen, A., Mahmoud, A., and El-Baz, A. (2022). How AI Can Help in the Diagnostic Dilemma of Pulmonary Nodules. Cancers, 14.","DOI":"10.3390\/cancers14071840"},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Safta, W., and Shaffie, A. (2024). Advancing pulmonary nodule diagnosis by integrating Engineered and Deep features extracted from CT scans. Algorithms, 17.","DOI":"10.3390\/a17040161"},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Farahat, I.S., Sharafeldeen, A., Elsharkawy, M., Soliman, A., Mahmoud, A., Ghazal, M., Taher, F., Bilal, M., Abdel Razek, A.A.K., and Aladrousy, W. (2022). The Role of 3D CT Imaging in the Accurate Diagnosis of Lung Function in Coronavirus Patients. Diagnostics, 12.","DOI":"10.3390\/diagnostics12030696"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Elsharkawy, M., Sharafeldeen, A., Taher, F., Shalaby, A., Soliman, A., Mahmoud, A., Ghazal, M., Khalil, A., Alghamdi, N.S., and Razek, A.A.K.A. (2021). Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-91305-0"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Saleh, G.A., Batouty, N.M., Gamal, A., Elnakib, A., Hamdy, O., Sharafeldeen, A., Mahmoud, A., Ghazal, M., Yousaf, J., and Alhalabi, M. (2023). Impact of Imaging Biomarkers and AI on Breast Cancer Management: A Brief Review. Cancers, 15.","DOI":"10.3390\/cancers15215216"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"104983","DOI":"10.1109\/ACCESS.2024.3432459","article-title":"A Novel Machine Learning Approach for Predicting Neoadjuvant Chemotherapy Response in Breast Cancer: Integration of Multimodal Radiomics With Clinical and Molecular Subtype Markers","volume":"12","author":"Gamal","year":"2024","journal-title":"IEEE Access"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Sharafeldeen, A., Elsharkawy, M., Shaffie, A., Khalifa, F., Soliman, A., Naglah, A., Khaled, R., Hussein, M.M., Alrahmawy, M., and Elmougy, S. (2022, January 21\u201325). Thyroid Cancer Diagnostic System using Magnetic Resonance Imaging. Proceedings of the 2022 26th International Conference on Pattern Recognition (ICPR), Montreal, QC, Canada.","DOI":"10.1109\/ICPR56361.2022.9956125"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1002\/mp.15399","article-title":"Texture and shape analysis of diffusion-weighted imaging for thyroid nodules classification using machine learning","volume":"49","author":"Sharafeldeen","year":"2021","journal-title":"Med. Phys."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1002\/jmri.28904","article-title":"Deep Learning for Discrimination of Hypertrophic Cardiomyopathy and Hypertensive Heart Disease on MRI Native T1 Maps","volume":"59","author":"Wang","year":"2023","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Jacob, A.J., Chitiboi, T., Schoepf, U.J., Sharma, P., Aldinger, J., Baker, C., Lautenschlager, C., Emrich, T., and Varga-Szemes, A. (J. Magn. Reson. Imaging, 2024). Deep-Learning-Based Disease Classification in Patients Undergoing Cine Cardiac MRI, J. Magn. Reson. Imaging, online version of record.","DOI":"10.1002\/jmri.29619"},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Amini, M., Pursamimi, M., Hajianfar, G., Salimi, Y., Saberi, A., Mehri-Kakavand, G., Nazari, M., Ghorbani, M., Shalbaf, A., and Shiri, I. (2023). Machine learning-based diagnosis and risk classification of coronary artery disease using myocardial perfusion imaging SPECT: A radiomics study. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-42142-w"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"101026","DOI":"10.1016\/j.measen.2024.101026","article-title":"MRI brain tumor detection using deep learning and machine learning approaches","volume":"31","author":"Anantharajan","year":"2024","journal-title":"Meas. Sens."},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Saeedi, S., Rezayi, S., Keshavarz, H., and Niakan Kalhori, S.R. (2023). MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques. BMC Med. Inform. Decis. Mak., 23.","DOI":"10.1186\/s12911-023-02114-6"},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Abdusalomov, A.B., Mukhiddinov, M., and Whangbo, T.K. (2023). Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging. Cancers, 15.","DOI":"10.3390\/cancers15164172"},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Garnier, C., Ferrer, L., Vargas, J., Gallinato, O., Jambon, E., Le Bras, Y., Bernhard, J.C., Colin, T., Grenier, N., and Marcelin, C. (2023). A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75). Diagnostics, 13.","DOI":"10.3390\/diagnostics13152548"},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Mahmud, S., Abbas, T.O., Mushtak, A., Prithula, J., and Chowdhury, M.E.H. (2023). Kidney Cancer Diagnosis and Surgery Selection by Machine Learning from CT Scans Combined with Clinical Metadata. Cancers, 15.","DOI":"10.3390\/cancers15123189"},{"key":"ref_140","first-page":"2023","article-title":"Enhanced transfer learning strategies for effective kidney tumor classification with CT imaging","volume":"14","author":"Majid","year":"2023","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_141","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image is Worth 16 \u00d7 16 Words: Transformers for Image Recognition at Scale. arXiv."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/2\/36\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:35:22Z","timestamp":1759919722000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/2\/36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,24]]},"references-count":141,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["computers14020036"],"URL":"https:\/\/doi.org\/10.3390\/computers14020036","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,24]]}}}