A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation

TL;DR

This paper introduces a momentum-enhanced deep learning method for improved medical image segmentation and classification, achieving state-of-the-art results on benchmark datasets.

Contribution

It presents a novel momentum-based technique within residual blocks that enhances training dynamics for medical image analysis tasks.

Findings

Achieved a 5.72% increase in dice score for lung segmentation.

Outperformed existing models with significant improvements in mIoU, recall, and precision.

Demonstrated state-of-the-art performance in both segmentation and classification tasks.

Abstract

Accurately segmenting different organs from medical images is a critical prerequisite for computer-assisted diagnosis and intervention planning. This study proposes a deep learning-based approach for segmenting various organs from CT and MRI scans and classifying diseases. Our study introduces a novel technique integrating momentum within residual blocks for enhanced training dynamics in medical image analysis. We applied our method in two distinct tasks: segmenting liver, lung, & colon data and classifying abdominal pelvic CT and MRI scans. The proposed approach has shown promising results, outperforming state-of-the-art methods on publicly available benchmarking datasets. For instance, in the lung segmentation dataset, our approach yielded significant enhancements over the TransNetR model, including a 5.72% increase in dice score, a 5.04% improvement in mean Intersection over Union (mIoU), an 8.02% improvement in recall, and a 4.42% improvement in precision. Hence, incorporating momentum led to state-of-the-art performance in both segmentation and classification tasks, representing a significant advancement in the field of medical imaging.