Enhancing Brain Tumor Classification: Leveraging Pre-trained CNN Models for Efficient MRI Image Analysis

Abstract

A "brain tumor" is a form of malignant growth that can emerge in the tissues that surround the brain. It can be classified as either malignant or noncancerous. Brain tumors are classified into two types: primary tumors and secondary tumors. The former refers to brain tumors that can spread to other areas of the body, whereas the latter can spread to other parts of the body. The indications and symptoms of a brain tumor can differ based on its size, location, and type. Vision impairments, hearing problems, and convulsions are just a few examples. Different types of treatment methods are available for brain tumors, such as surgery, radiation therapy, targeted therapy, and chemotherapy. The patient's health and the grade and size of the tumor are some of the factors that are considered when choosing a course of action. The correct classification of brain tumors is critical in the development of successful treatments. Around the world, individuals are dying from these diseases. Recent developments in deep learning (DL) have led to the development of models that can accurately identify brain tumors using MRI scans. This study presents a method that uses two advanced DL models InceptionV3 and EfficientNet-B7 for the purpose of improving the classification of brain tumors. The proposed method performed better than the current techniques when compared to a public dataset. According to the results of the study, the two models EfficientNet-B7 and InceptionV3, were able to accurately classify brain tumors. The proposed method could be used to increase the accuracy of brain tumor diagnosis and planning. It can also be applied to other imaging classification tasks. The study demonstrates the application of DL methods used in the analysis of medical images. It shows the efficiency of the EfficientNet-B7 and the InceptionV3 in distinguishing brain tumors on MRI scans.