Taslima Ferdaus Shuva

Brain tumors are among the most severe health challenges, necessitating early and precise diagnosis for effective treatment planning. This study introduces an optimized hybrid transfer learning (TL) framework for brain tumor classification using magnetic resonance imaging images. The proposed system integrates advanced preprocessing techniques, an ensemble of pretrained deep learning models, and explainable artificial intelligence (XAI) methods to achieve high accuracy and reliability. The methodology enhances image quality through noise reduction and contrast enhancement, facilitating robust feature extraction. The ensemble model combines VGG16 and ResNet152V2 architectures, achieving a classification accuracy of 99.47% on a challenging four‐class dataset. Additionally, gradient‐weighted class activation mapping and SHapley Additive exPlanations (SHAP)‐based XAI techniques provide visual and quantitative insights into model predictions, improving interpretability and clinical trust. This comprehensive framework demonstrates the potential of hybrid TL and XAI in advancing diagnostic accuracy and supporting clinical decision‐making for brain tumor detection. The results underscore its applicability in clinical settings, particularly in resource‐constrained environments.

Eye diseases are defined as disorders or diseases that damage the tissue and related parts of the eyes. They appear in various types and can be either minor, meaning that they do not last long, or permanent blindness. Cataracts, glaucoma, and diabetic retinopathy are all eye illnesses that can cause vision loss if not discovered and treated early on. Automated classification of these diseases from fundus images can empower quicker diagnoses and interventions. Our research aims to create a robust model, BayeSVM500, for eye disease classification to enhance medical technology and improve patient outcomes. In this study, we develop models to classify images accurately. We start by preprocessing fundus images using contrast enhancement, normalization, and resizing. We then leverage several state-of-the-art deep convolutional neural network pre-trained models, including VGG16, VGG19, ResNet50, EfficientNet, and DenseNet, to extract deep features. To reduce feature dimensionality, we employ techniques such as principal component analysis, feature agglomeration, correlation analysis, variance thresholding, and feature importance rankings. Using these refined features, we train various traditional machine learning models as well as ensemble methods. Our best model, named BayeSVM500, is a Support Vector Machine classifier trained on EfficientNet features reduced to 500 dimensions via PCA, achieving 93.65 ± 1.05% accuracy. Bayesian hyperparameter optimization further improved performance to 95.33 ± 0.60%. Through comprehensive feature engineering and model optimization, we demonstrate highly accurate eye disease classification from fundus images, comparable to or superior to previous benchmarks.