Comparative analysis of Brain Tumor Categorization on MR Images using various Deep Learning models

Neural Network algorithms have demonstrated notable capability in effectively analyzing human body image datasets obtained from MRI or CT with sufficient efficiency to direct medical operations. Although clinicians and radiologists can adeptly detect the presence of tumors and general abnormalities in brain scans, differentiating between specific tumor types typically requires significant time and effort. This study introduces an approach employing Convolution Neural Network (CNN) algorithms for precise classification of brain tumor into four distinct classes, leveraging deep learning models such as VGG16, VGG19, Efficient Net B5, Customized CNN. Prior to classification, the dataset undergoes preprocessing using Adaptive Median Filter (AMF) and Contrast Limited Adaptive Histogram Equalization (CLAHE) for denoising and contrast enhancement. The classification task involves categorizing images into four classes namely Glioma, Meningioma, Pituitary and Tumorless. The proposed methodology aims to streamline the analysis process by minimizing complexity while maximizing accuracy rates when compared to previous developments.