Deep Neural Network with l2-norm Unit for Brain Lesions Detection

TL;DR

This paper introduces a novel deep CNN approach with an l2-norm unit for automated detection of brain lesions in MRI images, demonstrating superior performance across multiple brain diseases and datasets.

Contribution

The paper proposes a new l2-norm unit within CNNs for brain lesion detection, enhancing feature normalization and detection accuracy in medical imaging.

Findings

Superior detection accuracy on benchmark datasets

Effective across multiple brain diseases

Improved feature normalization with l2-norm unit

Abstract

Automated brain lesions detection is an important and very challenging clinical diagnostic task because the lesions have different sizes, shapes, contrasts, and locations. Deep Learning recently has shown promising progress in many application fields, which motivates us to apply this technology for such important problem. In this paper, we propose a novel and end-to-end trainable approach for brain lesions classification and detection by using deep Convolutional Neural Network (CNN). In order to investigate the applicability, we applied our approach on several brain diseases including high and low-grade glioma tumor, ischemic stroke, Alzheimer diseases, by which the brain Magnetic Resonance Images (MRI) have been applied as an input for the analysis. We proposed a new operating unit which receives features from several projections of a subset units of the bottom layer and computes a normalized l2-norm for next layer. We evaluated the proposed approach on two different CNN architectures and number of popular benchmark datasets. The experimental results demonstrate the superior ability of the proposed approach.