Regularized Transfer Learning in Brain Tumor Classification: Leveraging Pretrained Models for Improved Diagnostic Accuracy

Abstract

Classification of medical images for precise detection and treatment of diseases is very vital. This study focuses mainly on classifying MRI images of brain tumors for neurological disorders. This study introduces a Systematic deep learning framework based on an enhanced ResNet50 architecture to improve brain tumor classification. The proposed approach incorporates dropout regularization, Global Average PoolingGAP), additional dense layers, and leaky ReLU activation to boost feature extraction and reduce overfitting. This work uses a varied set of MRI images. The dataset has gone through a different enhancement and preprocessing pipeline that includes cropping, resizing, contrast enhancement, and smoothing. The model evaluation process incorporated stratified 5-fold cross-validation. Different performance Indicators were measured through accuracy, precision, recall, and F1-score. The proposed architecture attained an average testing accuracy of 99.62% (95% Confidence Interval (CI)). These measures surpass benchmark models such as VGG16, MobileNet, InceptionV3, and Xception, in addition to previously documented methodologies in the existing literature. The result shows the stability across the folds and was statistically corroborated by ablation. Its ablation studies revealed that although the architectural alterations yielded only marginal enhancements over the baseline.