Novel Deep Learning Architectures for Classification and Segmentation of Brain Tumors from MRI Images

TL;DR

This paper introduces two novel deep learning architectures, SAETCN and SAS-Net, achieving high accuracy in classifying and segmenting brain tumors from MRI images, thereby enhancing early diagnosis and treatment.

Contribution

The paper presents new deep learning models specifically designed for brain tumor classification and segmentation, outperforming existing models in accuracy and generalization.

Findings

SAETCN achieved 99.38% validation accuracy in tumor classification.

SAS-Net achieved 99.23% pixel accuracy in tumor segmentation.

Models demonstrate improved generalization over previous approaches.

Abstract

Brain tumors pose a significant threat to human life, therefore it is very much necessary to detect them accurately in the early stages for better diagnosis and treatment. Brain tumors can be detected by the radiologist manually from the MRI scan images of the patients. However, the incidence of brain tumors has risen amongst children and adolescents in recent years, resulting in a substantial volume of data, as a result, it is time-consuming and difficult to detect manually. With the emergence of Artificial intelligence in the modern world and its vast application in the medical field, we can make an approach to the CAD (Computer Aided Diagnosis) system for the early detection of Brain tumors automatically. All the existing models for this task are not completely generalized and perform poorly on the validation data. So, we have proposed two novel Deep Learning Architectures - (a) SAETCN (Self-Attention Enhancement Tumor Classification Network) for the classification of different kinds of brain tumors. We have achieved an accuracy of 99.38% on the validation dataset making it one of the few Novel Deep learning-based architecture that is capable of detecting brain tumors accurately. We have trained the model on the dataset, which contains images of 3 types of tumors (glioma, meningioma, and pituitary tumors) and non-tumor cases. and (b) SAS-Net (Self-Attentive Segmentation Network) for the accurate segmentation of brain tumors. We have achieved an overall pixel accuracy of 99.23%.