Deep Learning Based Brain Tumor Diagnosis

Abstract

Among all oncology disorders, brain tumors are among the most complex and lethal, necessitating rapid and accurate differential diagnosis. Traditional manual examination of Magnetic Resonance Imaging (MRI) scans is time-consuming and subject to significant variability among radiologists. This study evaluates five deep learning architectures — InceptionV3, ResNet50, VGG16, InceptionResNetV2, and EfficientNetV2L — for the automated classification of brain tumors into glioma, meningioma, and pituitary tumor types using the Figshare Brain Tumor Classification dataset. The dataset was split into 60% training, 20% validation and 20% testing sets with class weights addressing class imbalance. Models were initialized with ImageNet pre-trained weights, with custom layers (GlobalAveragePooling2D, dense layers with 1024–4096 units, and dropout) added for feature extraction and classification. They were trained for 30 epochs using the Adam optimizer. The experimental results show that the InceptionV3 model achieved an accuracy of 92.10% and also demonstrated better Grad-CAM performance for the effective localization of tumor regions than the other four algorithms. These results highlight the potential of deep learning, particularly InceptionV3, to enhance diagnostic accuracy and efficiency in brain tumor classification. Future research should focus on refining model architectures and optimizing computations for real-time clinical applications.