{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T20:12:01Z","timestamp":1774555921885,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T00:00:00Z","timestamp":1729209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Brain tumor detection is crucial for effective treatment planning and improved patient outcomes. However, existing methods often face challenges, such as limited interpretability and class imbalance in medical-imaging data. This study presents a novel, custom Convolutional Neural Network (CNN) architecture, specifically designed to address these issues by incorporating interpretability techniques and strategies to mitigate class imbalance. We trained and evaluated four CNN models (proposed CNN, ResNetV2, DenseNet201, and VGG16) using a brain tumor MRI dataset, with oversampling techniques and class weighting employed during training. Our proposed CNN achieved an accuracy of 94.51%, outperforming other models in regard to precision, recall, and F1-Score. Furthermore, interpretability was enhanced through gradient-based attribution methods and saliency maps, providing valuable insights into the model\u2019s decision-making process and fostering collaboration between AI systems and clinicians. This approach contributes a highly accurate and interpretable framework for brain tumor detection, with the potential to significantly enhance diagnostic accuracy and personalized treatment planning in neuro-oncology.<\/jats:p>","DOI":"10.3390\/info15100653","type":"journal-article","created":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T06:46:52Z","timestamp":1729234012000},"page":"653","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Enhancing Brain Tumor Detection Through Custom Convolutional Neural Networks and Interpretability-Driven Analysis"],"prefix":"10.3390","volume":"15","author":[{"given":"Kavinda Ashan Kulasinghe Wasalamuni","family":"Dewage","sequence":"first","affiliation":[{"name":"Department of Computer Science, Solent University, Southampton SO14 0YN, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8089-837X","authenticated-orcid":false,"given":"Raza","family":"Hasan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Solent University, Southampton SO14 0YN, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2081-5728","authenticated-orcid":false,"given":"Bacha","family":"Rehman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Solent University, Southampton SO14 0YN, UK"}]},{"given":"Salman","family":"Mahmood","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Nazeer Hussain University, ST-2, near Karimabad, Karachi 75950, Pakistan"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Khazaei, Z., Goodarzi, E., Borhaninejad, V., Iranmanesh, F., Mirshekarpour, H., Mirzaei, B., Naemi, H., Bechashk, S.M., Darvishi, I., and Ershad Sarabi, R. (2020). The association between incidence and mortality of brain cancer and human development index (HDI): An ecological study. BMC Public Health, 20.","DOI":"10.1186\/s12889-020-09838-4"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"vdac080","DOI":"10.1093\/noajnl\/vdac080","article-title":"Standard clinical approaches and emerging modalities for glioblastoma imaging","volume":"4","author":"Bernstock","year":"2022","journal-title":"Neuro-Oncol. Adv."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Sabeghi, P., Zarand, P., Zargham, S., Golestany, B., Shariat, A., Chang, M., Yang, E., Rajagopalan, P., Phung, D.C., and Gholamrezanezhad, A. (2024). Advances in Neuro-Oncological Imaging: An Update on Diagnostic Approach to Brain Tumors. Cancers, 16.","DOI":"10.3390\/cancers16030576"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1038\/s42256-021-00377-0","article-title":"Radiological tumour classification across imaging modality and histology","volume":"3","author":"Wu","year":"2021","journal-title":"Nat. Mach. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1111\/bpa.12837","article-title":"Pathology, diagnostics, and classification of medulloblastoma","volume":"30","author":"Orr","year":"2020","journal-title":"Brain Pathol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"ZainEldin, H., Gamel, S.A., El-Kenawy, E.M., Alharbi, A.H., Khafaga, D.S., Ibrahim, A., and Talaat, F.M. (2022). Brain Tumor Detection and Classification Using Deep Learning and Sine-Cosine Fitness Grey Wolf Optimization. Bioengineering, 10.","DOI":"10.3390\/bioengineering10010018"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5634","DOI":"10.22214\/ijraset.2023.52981","article-title":"Brain Tumor Detection","volume":"11","author":"Saraswat","year":"2023","journal-title":"IJRASET"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Rajeev, S.K., Rajasekaran, M.P., Ramaraj, K., Vishnuvarthanan, G., Arunprasath, T., and Muneeswaran, V. (2023, January 17\u201319). A Hybrid CNN-LSTM Network For Brain Tumor Classification Using Transfer Learning. Proceedings of the 2023 9th International Conference on Smart Computing and Communications (ICSCC), Kochi, Kerala, India.","DOI":"10.1109\/ICSCC59169.2023.10335082"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"490","DOI":"10.22214\/ijraset.2023.54665","article-title":"Brain Tumor Detection using Deep Learning","volume":"11","author":"Aakanksha","year":"2023","journal-title":"Int. J. Res. Appl. Sci. Eng. Technol."},{"key":"ref_10","unstructured":"Singh, A. (2016, January 16\u201318). Review of Brain Tumor Detection from MRI Images. Proceedings of the 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tambe, U.Y., and Shanthini, A. (2023, January 6\u20137). Brain Tumor Detection & Classification into Different Categories using Deep Learning Model. Proceedings of the 2023 International Conference on Advanced Computing Technologies and Applications (ICACTA), Mumbai, India.","DOI":"10.1109\/ICACTA58201.2023.10393430"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"332","DOI":"10.5755\/j01.itc.51.2.30835","article-title":"A Deep Transfer Learning Based Architecture for Brain Tumor Classification Using MR Images","volume":"51","author":"Badjie","year":"2022","journal-title":"Inf. Technol. Control"},{"key":"ref_13","first-page":"816","article-title":"Brain Tumour Detection and Classification Using U-Net Deep Neural Network","volume":"10","author":"Banu","year":"2022","journal-title":"Int. J. Creat. Res. Thoughts (IJCRT)"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C., Feichtenhofer, C., Darrell, T., and Xie, S. (2022, January 18\u201324). A ConvNet for the 2020s. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"ref_15","unstructured":"Dai, Z., Liu, H., Le, Q.V., and Tan, M. (2021). CoAtNet: Marrying Convolution and Attention for All Data Sizes. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tajane, K., Rathkanthiwar, V., Chava, G., Dhavale, S., Chawda, G., and Pitale, R. (2023, January 18\u201319). EffiConvRes: An Efficient Convolutional Neural Network with Residual Connections and Depthwise Convolutions. Proceedings of the 2023 7th International Conference on Computing, Communication, Control And Automation (ICCUBEA 2023), Pune, India.","DOI":"10.1109\/ICCUBEA58933.2023.10392177"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Todi, A., Narula, N., Sharma, M., and Gupta, U. (2023, January 8\u20139). ConvNext: A Contemporary Architecture for Convolutional Neural Networks for Image Classification. Proceedings of the 3rd International Conference on Innovative Sustainable Computational Technologies, Graphic Era Deemed to Be University, Dehradun, India.","DOI":"10.1109\/CISCT57197.2023.10351320"},{"key":"ref_18","first-page":"100498","article-title":"Multigrade brain tumor classification in MRI images using Fine tuned efficientnet","volume":"8","author":"Priyadarshini","year":"2024","journal-title":"e-Prime"},{"key":"ref_19","unstructured":"Abu-Taieh, E., El Mouatasim, A., and Al Hadid, I.H. (2019). Research Design and Methodology, IntechOpen. Chapter 3."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"417","DOI":"10.22214\/ijraset.2021.39280","article-title":"Brain Tumor Classification using Deep Learning Algorithms","volume":"9","author":"Kadam","year":"2021","journal-title":"Int. J. Res. Appl. Sci. Eng. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Razzaq, M., Cl\u00e9ment, F., and Yvinec, R. (2022). An overview of deep learning applications in precocious puberty and thyroid dysfunction. Front. Endocrinol., 13.","DOI":"10.3389\/fendo.2022.959546"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Aliferis, C., and Simon, G. (2024). Overfitting, Underfitting and General Model Overconfidence and Under-Performance Pitfalls and Best Practices in Machine Learning and AI, Springer.","DOI":"10.1007\/978-3-031-39355-6_10"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Salehin, I., and Kang, D. (2023). A Review on Dropout Regularization Approaches for Deep Neural Networks within the Scholarly Domain. Electronics, 12.","DOI":"10.3390\/electronics12143106"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, X., Yan, L., and Zhang, Q. (2021, January 24\u201326). Research on the Application of Gradient Descent Algorithm in Machine Learning. Proceedings of the 2021 International Conference on Computer Network, Electronic and Automation (ICCNEA), Xi\u2019an, China.","DOI":"10.1109\/ICCNEA53019.2021.00014"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4236\/jbise.2024.171001","article-title":"Using Cross Entropy as a Performance Metric for Quantifying Uncertainty in DNN Image Classifiers: An Application to Classification of Lung Cancer on CT Images","volume":"17","author":"Matsuyama","year":"2024","journal-title":"J. Biomed. Sci. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e604","DOI":"10.7717\/peerj-cs.604","article-title":"Selective oversampling approach for strongly imbalanced data","volume":"7","author":"Gnip","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1007\/s10462-023-10652-8","article-title":"Cost-sensitive learning for imbalanced medical data: A review","volume":"57","author":"Araf","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7120","DOI":"10.5688\/ajpe7120","article-title":"A Review of the Quality Indicators of Rigor in Qualitative Research","volume":"84","author":"Johnson","year":"2020","journal-title":"Am. J. Pharm. Educ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"211","DOI":"10.2147\/JMDH.S104807","article-title":"Information bias in health research: Definition, pitfalls, and adjustment methods","volume":"9","author":"Althubaiti","year":"2016","journal-title":"J. Multidiscip. Healthc."},{"key":"ref_31","first-page":"2245","article-title":"The Impact of Feature Importance Methods on the Interpretation of Defect Classifiers","volume":"48","author":"Rajbahadur","year":"2022","journal-title":"TSE"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Deepak, S., and Ameer, P.M. (2020). Retrieval of brain MRI with tumor using contrastive loss based similarity on GoogLeNet encodings. Comput. Biol. Med., 125.","DOI":"10.1016\/j.compbiomed.2020.103993"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"109684","DOI":"10.1016\/j.mehy.2020.109684","article-title":"Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture","volume":"139","author":"Yildirim","year":"2020","journal-title":"Med. Hypotheses"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Khare, N., Devan, P., Chowdhary, C., Bhattacharya, S., Singh, G., Singh, S., and Yoon, B. (2020). SMO-DNN: Spider Monkey Optimization and Deep Neural Network Hybrid Classifier Model for Intrusion Detection. Electronics, 9.","DOI":"10.3390\/electronics9040692"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"19","DOI":"10.4018\/jssci.2011040102","article-title":"Medical Image Classification Using an Optimal Feature Extraction Algorithm and a Supervised Classifier Technique","volume":"3","author":"Kharrat","year":"2011","journal-title":"Int. J. Softw. Sci. Comput. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"D\u00edaz-Pernas, F.J., Mart\u00ednez-Zarzuela, M., Ant\u00f3n-Rodr\u00edguez, M., and Gonz\u00e1lez-Ortega, D. (2021). A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network. Healthcare, 9.","DOI":"10.3390\/healthcare9020153"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4275","DOI":"10.1109\/ACCESS.2018.2885639","article-title":"A Modified Deep Convolutional Neural Network for Abnormal Brain Image Classification","volume":"7","author":"Hemanth","year":"2019","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Paul, J.S., Plassard, A., Landman, B., and Fabbri, D. (2017, January 11\u201316). Deep Learning for Brain Tumor Classification. Proceedings of the Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, Orlando, FL, USA.","DOI":"10.1117\/12.2254195"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3551636","article-title":"A Comprehensive Survey on Poisoning Attacks and Countermeasures in Machine Learning","volume":"55","author":"Tian","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_40","unstructured":"Wu, B., Wei, S., Zhu, M., Zheng, M., Zhu, Z., Zhang, M., Chen, H., Yuan, D., Liu, L., and Liu, Q. (2023). Defenses in Adversarial Machine Learning: A Survey. arXiv."},{"key":"ref_41","first-page":"1","article-title":"Adversarial Attacks and Defenses in Deep Learning: From a Perspective of Cybersecurity","volume":"55","author":"Zhou","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"104750","DOI":"10.1016\/j.trc.2024.104750","article-title":"Data poisoning attacks in intelligent transportation systems: A survey","volume":"165","author":"Wang","year":"2024","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Comput. Biol. Med., 158.","DOI":"10.1016\/j.compbiomed.2023.106848"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2058","DOI":"10.1002\/aenm.202002646","article-title":"How Far Are We from Achieving Self-Powered Flexible Health Monitoring Systems: An Energy Perspective","volume":"11","author":"Yu","year":"2021","journal-title":"Adv. Energy Mater."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/10\/653\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:15:58Z","timestamp":1760112958000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/10\/653"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,18]]},"references-count":44,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["info15100653"],"URL":"https:\/\/doi.org\/10.3390\/info15100653","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,18]]}}}