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

TL;DR

This work tackles automated brain-tumor detection from MRI by introducing two novel self-attention–driven architectures: SAETCN for multiclass classification and SAS-Net for precise segmentation. SAETCN employs a Normalised Convolutional Activation Block and 16 Self-Attention Enhancement Blocks organized into four modules to produce a 2048-channel representation for 4-class outputs, while SAS-Net uses a parallel attention-based decoder with Segmental Feature Decoding Blocks to achieve high-accuracy tumor masks. Across three datasets, SAETCN achieves near-perfect classification performance (up to 99.38%), and SAS-Net achieves a segmentation Dice score of 0.9979 with strong specificity and sensitivity, surpassing prior methods. The authors also provide ablation evidence supporting the architectural choices and make all code and data publicly available for reproducibility and potential real-world deployment in mobile and web contexts.

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%.

Figures (16)

• Figure 1: MRI Images of Types of Brain Tumor and No Tumor
• Figure 2: Processflow Diagram of our Experiment
• Figure 3: Self-Attention Enhanced Tumor Classification Network (SAETCN)
• Figure 4: Self Attention Enhancement Block
• Figure 5: Normalised Convolutional Activation Block