A HYBRID DEEP CNN MODEL FOR BRAIN TUMOR IMAGE MULTI-CLASSIFICATION

Abstract

The current approach to diagnosing and classifying brain tumors relies on the
histological evaluation of biopsy samples, which is invasive, time-consuming, and susceptible
to manual errors. These limitations underscore the pressing need for a fully automated, deeplearning- based multi-classification system for brain malignancies. This article aims to
leverage a deep convolutional neural network (CNN) to enhance early detection and presents
three distinct CNN models designed for different types of classification tasks. The first CNN
model achieves an impressive detection accuracy of 99.53% for brain tumors. The second
CNN model, with an accuracy of 93.81%, proficiently categorizes brain tumors into five
distinct types: normal, glioma, meningioma, pituitary, and metastatic. Furthermore, the third
CNN model demonstrates an accuracy of 98.56% in accurately classifying brain tumors into
their different grades. To ensure optimal performance, a grid search optimization approach is
employed to automatically fine-tune all the relevant hyperparameters of the CNN models. The
utilization of large, publicly accessible clinical datasets results in robust and reliable
classification outcomes. This article conducts a comprehensive comparison of the proposed
models against classical models, such as AlexNet, DenseNet121, ResNet-101, VGG-19, and
GoogleNet, reaffirming the superiority of the deep CNN-based approach in advancing the
field of brain tumor classification and early detection