{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T18:02:50Z","timestamp":1771956170861,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T00:00:00Z","timestamp":1678233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Automated deep learning is promising in artificial intelligence (AI). However, a few applications of automated deep learning networks have been made in the clinical medical fields. Therefore, we studied the application of an open-source automated deep learning framework, Autokeras, for detecting smear blood images infected with malaria parasites. Autokeras is able to identify the optimal neural network to perform the classification task. Hence, the robustness of the adopted model is due to it not needing any prior knowledge from deep learning. In contrast, the traditional deep neural network methods still require more construction to identify the best convolutional neural network (CNN). The dataset used in this study consisted of 27,558 blood smear images. A comparative process proved the superiority of our proposed approach over other traditional neural networks. The evaluation results of our proposed model achieved high efficiency with impressive accuracy, reaching 95.6% when compared with previous competitive models.<\/jats:p>","DOI":"10.3390\/jimaging9030064","type":"journal-article","created":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T03:59:32Z","timestamp":1678247972000},"page":"64","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images"],"prefix":"10.3390","volume":"9","author":[{"given":"Ahmad","family":"Alaiad","sequence":"first","affiliation":[{"name":"Department of Computer Information Systems, Jordan University of Science and Technology, Irbid 22110, Jordan"}]},{"given":"Aya","family":"Migdady","sequence":"additional","affiliation":[{"name":"Department of Computer Information Systems, Jordan University of Science and Technology, Irbid 22110, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0439-9210","authenticated-orcid":false,"given":"Ra\u2019ed M.","family":"Al-Khatib","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, Yarmouk University, Irbid 21163, Jordan"}]},{"given":"Omar","family":"Alzoubi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Jordan University of Science and Technology, Irbid 22110, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2693-2132","authenticated-orcid":false,"given":"Raed Abu","family":"Zitar","sequence":"additional","affiliation":[{"name":"Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, Abu Dhabi P.O. Box 32092, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2203-4549","authenticated-orcid":false,"given":"Laith","family":"Abualigah","sequence":"additional","affiliation":[{"name":"Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan"},{"name":"College of Engineering, Yuan Ze University, Taoyuan 320315, Taiwan"},{"name":"Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan"},{"name":"Faculty of Information Technology, Middle East University, Amman 11831, Jordan"},{"name":"Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan"},{"name":"School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,8]]},"reference":[{"key":"ref_1","unstructured":"WHO (2019). Global Perspectives on Assistive Technology: Proceedings of the GReAT Consultation 2019."},{"key":"ref_2","unstructured":"Rahman, A., Zunair, H., Rahman, M.S., Yuki, J.Q., Biswas, S., Alam, M.A., Alam, N.B., and Mahdy, M. (2019). Improving malaria parasite detection from red blood cell using deep convolutional neural networks. arXiv."},{"key":"ref_3","unstructured":"WHO (2019). World Malaria Report 2019."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"101669","DOI":"10.1016\/j.tmaid.2020.101669","article-title":"Online learning in the time of COVID-19","volume":"34","author":"Chiodini","year":"2020","journal-title":"Travel Med. Infect. Dis."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Osei-Yeboah, J., Kwame Norgbe, G., Yao Lokpo, S., Khadijah Kinansua, M., Nettey, L., and Allotey, E.A. (2016). Comparative performance evaluation of routine malaria diagnosis at Ho Municipal Hospital. J. Parasitol. Res., 2016.","DOI":"10.1155\/2016\/5837890"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ali, R., Hardie, R.C., Narayanan, B.N., and Kebede, T.M. (2022). IMNets: Deep learning using an incremental modular network synthesis approach for medical imaging applications. Appl. Sci., 12.","DOI":"10.3390\/app12115500"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Krishnadas, P., and Sampathila, N. (2021, January 2\u20134). Automated detection of malaria implemented by deep learning in PyTorch. Proceedings of the 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India.","DOI":"10.1109\/CONECCT52877.2021.9622608"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Abubakar, A., Ajuji, M., and Yahya, I.U. (2021). DeepFMD: Computational Analysis for Malaria Detection in Blood-Smear Images Using Deep-Learning Features. Appl. Syst. Innov., 4.","DOI":"10.3390\/asi4040082"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Uzun Ozsahin, D., Mustapha, M.T., Bartholomew Duwa, B., and Ozsahin, I. (2022). Evaluating the performance of deep learning frameworks for malaria parasite detection using microscopic images of peripheral blood smears. Diagnostics, 12.","DOI":"10.3390\/diagnostics12112702"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Thornton, C., Hutter, F., Hoos, H.H., and Leyton-Brown, K. (2013, January 11\u201313). Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA.","DOI":"10.1145\/2487575.2487629"},{"key":"ref_11","unstructured":"Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., and Hutter, F. (2015). Advances in Neural Information Processing Systems 28 (NIPS 2015), NeurIPS."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Jin, H., Song, Q., and Hu, X. (2019, January 4\u20138). Auto-keras: An efficient neural architecture search system. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330648"},{"key":"ref_13","unstructured":"Hibayesian (2021, January 01). GitHub\u2014Hibayesian\/Awesome-Automl-Papers: A Curated List of Automated Machine Learning Papers, Articles, Tutorials, Slides and Projects. Available online: https:\/\/github.com\/hibayesian\/awesome-automl-papers."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"054502","DOI":"10.1117\/1.JMI.8.5.054502","article-title":"Malaria detection through digital microscopic imaging using Deep Greedy Network with transfer learning","volume":"8","author":"Dey","year":"2021","journal-title":"J. Med. Imaging"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2033473","DOI":"10.1080\/08839514.2022.2033473","article-title":"A Novel Data Augmentation Convolutional Neural Network for Detecting Malaria Parasite in Blood Smear Images","volume":"36","author":"Oyewola","year":"2022","journal-title":"Appl. Artif. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kassim, Y.M., Yang, F., Yu, H., Maude, R.J., and Jaeger, S. (2021). Diagnosing Malaria Patients with Plasmodium falciparum and vivax Using Deep Learning for Thick Smear Images. Diagnostics, 11.","DOI":"10.3390\/diagnostics11111994"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"15297","DOI":"10.1007\/s11042-019-7162-y","article-title":"Deep learning approach to detect malaria from microscopic images","volume":"79","author":"Vijayalakshmi","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Diyasa, I.G.S.M., Fauzi, A., Setiawan, A., Idhom, M., Wahid, R.R., and Alhajir, A.D. (2021, January 13\u201316). Pre-trained deep convolutional neural network for detecting malaria on the human blood smear images. Proceedings of the 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Jeju, Republic of Korea.","DOI":"10.1109\/ICAIIC51459.2021.9415183"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9099","DOI":"10.1109\/ACCESS.2017.2705642","article-title":"Malaria parasite detection from peripheral blood smear images using deep belief networks","volume":"5","author":"Bibin","year":"2017","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s12639-019-01163-x","article-title":"Automatic detection of Plasmodium parasites from microscopic blood images","volume":"44","author":"Fatima","year":"2020","journal-title":"J. Parasit. Dis."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Maqsood, A., Farid, M.S., Khan, M.H., and Grzegorzek, M. (2021). Deep malaria parasite detection in thin blood smear microscopic images. Appl. Sci., 11.","DOI":"10.3390\/app11052284"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Islam, M.R., Nahiduzzaman, M., Goni, M.O.F., Sayeed, A., Anower, M.S., Ahsan, M., and Haider, J. (2022). Explainable Transformer-based deep learning model for the detection of malaria parasites from blood cell images. Sensors, 22.","DOI":"10.3390\/s22124358"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Shah, D., Kawale, K., Shah, M., Randive, S., and Mapari, R. (2020, January 13\u201315). Malaria parasite detection using deep learning:(Beneficial to humankind). Proceedings of the 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India.","DOI":"10.1109\/ICICCS48265.2020.9121073"},{"key":"ref_24","first-page":"147","article-title":"Empirical Analysis of a Fine-Tuned Deep Convolutional Model in Classifying and Detecting Malaria Parasites from Blood Smears","volume":"15","author":"Montalbo","year":"2021","journal-title":"KSII Trans. Internet Inf. Syst. (TIIS)"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"216","DOI":"10.5152\/electrica.2020.21004","article-title":"A novel implementation of deep-learning approach on malaria parasite detection from thin blood cell images","volume":"21","author":"Irmak","year":"2021","journal-title":"Electrica"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.dcan.2021.07.011","article-title":"Image analysis and machine learning based malaria assessment system","volume":"8","author":"Manning","year":"2021","journal-title":"Digit. Commun. Netw."},{"key":"ref_27","unstructured":"Perez, J.G.M. (2019). Autotext: AutoML for Text Classification. [Master\u2019s Thesis, National Institute of Astrophysics, Optics and Electronics]."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Olson, R.S., Bartley, N., Urbanowicz, R.J., and Moore, J.H. (2016, January 20\u201324). Evaluation of a tree-based pipeline optimization tool for automating data science. Proceedings of the Genetic And Evolutionary Computation Conference, Denver, CO, USA.","DOI":"10.1145\/2908812.2908918"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hutter, F., Hoos, H.H., and Leyton-Brown, K. (2011, January 17\u201321). Sequential model-based optimization for general algorithm configuration. Proceedings of the International Conference on Learning and Intelligent Optimization, Rome, Italy.","DOI":"10.1007\/978-3-642-25566-3_40"},{"key":"ref_30","unstructured":"LHNCBC (2021, January 01). LHNCBC Full Download List, Available online: https:\/\/lhncbc.nlm.nih.gov\/LHC-downloads\/downloads.html."},{"key":"ref_31","unstructured":"Ripley, B.D. (2007). Pattern Recognition and Neural Networks, Cambridge University Press."},{"key":"ref_32","unstructured":"Cho, J., Lee, K., Shin, E., Choy, G., and Do, S. (2015). How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?. arXiv."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/9\/3\/64\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:50:47Z","timestamp":1760122247000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/9\/3\/64"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,8]]},"references-count":32,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["jimaging9030064"],"URL":"https:\/\/doi.org\/10.3390\/jimaging9030064","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,8]]}}}