Leveraging SeNet and ResNet Synergy within an Encoder-Decoder Architecture for Glioma Detection

TL;DR

This paper introduces a novel encoder-decoder model combining SeNet and ResNet architectures for improved glioma detection and segmentation in MRI scans, significantly enhancing accuracy and reducing manual effort.

Contribution

It proposes a new deep learning framework that leverages SeResNet-152 within an encoder-decoder architecture specifically for brain tumor segmentation.

Findings

Achieved 87% Dice Coefficient in tumor segmentation

Attained 89.12% accuracy in glioma detection

Demonstrated improved segmentation performance over existing methods

Abstract

Brain tumors are abnormalities that can severely impact a patient's health, leading to life-threatening conditions such as cancer. These can result in various debilitating effects, including neurological issues, cognitive impairment, motor and sensory deficits, as well as emotional and behavioral changes. These symptoms significantly affect a patient's quality of life, making early diagnosis and timely treatment essential to prevent further deterioration. However, accurately segmenting the tumor region from medical images, particularly MRI scans, is a challenging and time-consuming task that requires the expertise of radiologists. Manual segmentation can also be prone to human errors. To address these challenges, this research leverages the synergy of SeNet and ResNet architectures within an encoder-decoder framework, designed specifically for glioma detection and segmentation. The proposed model incorporates the power of SeResNet-152 as the backbone, integrated into a robust encoder-decoder structure to enhance feature extraction and improve segmentation accuracy. This novel approach significantly reduces the dependency on manual tasks and improves the precision of tumor identification. Evaluation of the model demonstrates strong performance, achieving 87% in Dice Coefficient, 89.12% in accuracy, 88% in IoU score, and 82% in mean IoU score, showcasing its effectiveness in tackling the complex problem of brain tumor segmentation.