DEVELOPING NOVEL DEEP NEURAL NETWORKS FOR MEDICAL IMAGE ANALYSIS

Abstract

Advances in artificial intelligence (AI), powered by machine learning (ML) and deep learning (DL), are driving significant advancements in medical image analysis. These techniques facilitate the automated, precise interpretation of medical images for crucial tasks such as localization, classification, and segmentation. This capability holds immense potential for improving diagnostic accuracy, streamlining clinical workflows, and ultimately enhancing patient outcomes across diverse healthcare settings. The field of medical image analysis is experiencing rapid advancements fueled by AI-driven methodologies. These innovative techniques empower clinicians to detect subtle abnormalities, quantify anatomical structures, and monitor disease progression with enhanced accuracy. This research delves into the development of innovative deep neural networks tailored for medical image analysis. Prior work explored the use of YOLOv3 for accurate wound localization and the creation of multi-modal networks for enhanced wound classification, leveraging both image data and wound location information. Additionally, in breast cancer image classification, advanced architectures like MultiNet, incorporating transformers, and BCCNet, utilizing attention mechanisms and feature optimization, were investigated. Current research centers on a semi-supervised model (CustomNet) for 2D breast cell binary segmentation, designed to address the challenge of limited labeled datasets. This work demonstrates the transformative impact of AI in medical image analysis, with a potential to significantly improve wound care and breast cancer diagnostics.