A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks

TL;DR

This paper provides a comprehensive review of deep neural network-based medical image segmentation, emphasizing explainability, early diagnosis, and challenges to improve disease detection and patient outcomes.

Contribution

It offers an extensive analysis of current MIS techniques, discusses the role of XAI, and proposes solutions to enhance DNN efficiency in medical imaging.

Findings

XAI is crucial for transparent medical diagnoses.

Early prediction significantly improves patient survival rates.

Challenges in DNN implementation are identified and addressed.

Abstract

Over the past decade, Medical Image Segmentation (MIS) using Deep Neural Networks (DNNs) has achieved significant performance improvements and holds great promise for future developments. This paper presents a comprehensive study on MIS based on DNNs. Intelligent Vision Systems are often evaluated based on their output levels, such as Data, Information, Knowledge, Intelligence, and Wisdom (DIKIW),and the state-of-the-art solutions in MIS at these levels are the focus of research. Additionally, Explainable Artificial Intelligence (XAI) has become an important research direction, as it aims to uncover the "black box" nature of previous DNN architectures to meet the requirements of transparency and ethics. The study emphasizes the importance of MIS in disease diagnosis and early detection, particularly for increasing the survival rate of cancer patients through timely diagnosis. XAI and early prediction are considered two important steps in the journey from "intelligence" to "wisdom." Additionally, the paper addresses existing challenges and proposes potential solutions to enhance the efficiency of implementing DNN-based MIS.