Brain tumor malignancy classification using improved VGG16 based on MRI images

Abstract

This study introduces a method based on Magnetic Resonance Imaging (MRI) images and Convolutional Neural Networks (CNN) to categorize the malignancy of brain tumors. The major goal of this research is to develop an accurate and reliable model that aids in tumor early identification, leading to better patient outcomes. Specifically, first, the MRI images undergo preprocessing, which includes intensity normalization and random brightness and contrast adjustments for data augmentation. Next, the Visual Geometry Group (VGG16) model serves as a baseline, enhanced with flatten, dense, and dropout layers. Transfer learning is applied to leverage pre-trained features from VGG16, improving the generalization capability of model. A sizable collection of brain MRI pictures is used to train the model. The experimental outcomes on the MRI dataset illustrate the efficacy of the suggested model. The CNN-based approach achieves high accuracy in classifying brain tumor malignancy based on MRI images, indicating its potential in medical image analysis and its significance in early tumor detection and diagnosis. The proposed model serves as a valuable tool for healthcare professionals, aiding in well-informed decisions and providing an ideal medical approach for early detection of brain tumors. This research opens avenues for future work in medical imaging and deep learning, paving the way for improved treatment and diagnosis of brain tumors.