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Life sciences · Journal article
International Journal of Drug Delivery Technology · October 22, 2026
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Two of the most severe forms of neurological issues, which can be treated only when diagnosed with a high degree of accuracy with the help of MRI, are brain tumors and Alzheimer disease (AD).The project under analysis considers the datasets of MRI that are not balanced and refers to the Synthetic Minority Oversampling Technique (SMOTE) to improve the classification process.They included several ML and DL models including the RF, SVM, Voting Classifier, CNN, VGG16, ResNet50, and Inception V3, as well as the AlexNet with LSTM, Xception, and the new Transformer and LSTM Enhanced Attention-Based Explainable CNN (TLEABLCNN).TLEABLCNN model consistently performed better than its baseline models with a classification accuracy of 96.1% in Scenario A, 97.4% in Scenario B and 98.2% in Scenario C. It also possessed superior macro-precision, recall and F1-scores.To detect abnormalities in the brain; the object recognition models of YOLOv5, YOLOv8, YOLOv9 and YOLOv11 were employed.YOLOv9 got the highest best mean average precision (mAP) of 91.5%.YOLOv8 achieved the best accuracy of 91.8%.Moreover, XAI techniques, such as heatmaps, were employed to generate the heatmaps that revealed the most significant portions of MRI scans that influenced model predictions.This simplified the results and enhanced professional trust.These findings indicate the strength of the framework in the discovery of brain disorders which can be explained.