Convolutional Neural Network-based Framework for Brain Tumor Classification and Segmentation using Magnetic Resonance Images.

Abstract

Early diagnosis of brain tumors is critical for optimization of the prognosis and treatment selection of the patient. Accurate segmentation and categorization of brain tumors are essential to create specialist treatment techniques. As MRI utilization for brain diagnosis increases and computer vision technology also improves, having a good and effective model to identify and categorize tumors based on MRI scans remains challenging. To address this problem, the authors suggested a deep learning-based technique to segment and classify brain tumors from different datasets. Image preprocessing employed nine augmentation methods to enhance model performance. Segmentation of MRI was done by using a U-Net model. The developed classification model based on InceptionV3 and DenseNet201 predicts the existence of the tumor and categorizes it into Glioma, Meningioma, and Pituitary. With 99.15% accuracy, InceptionV3 is higher than DenseNet201's 98.75% in tumor classification. Additional tumor classification was performed by Clustering as HGG and LGG on the basis of Inception-ResNet-v2. Tumor grades (1-4) are identified with 96.64% accuracy by Inception-ResNet-v2. An autonomous system integrates hybrid models with GPT-4.0 to generate reports. Hence, this novel framework could very well be suitable for clinics when used for automatically identifying and separating brain tumors utilizing input images captured from MRI scans.