A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks

2025-06-04

International Journal of Advanced Computer Science and Applications

AI Summary

This study addresses three critical challenges in medical image segmentation (MIS): weak interpretability of deep neural network (DNN) models, fragmented evaluation frameworks, and insufficient clinical trustworthiness. To tackle these, we systematically introduce the DIKIW (Data–Information–Knowledge–Intelligence–Wisdom) hierarchy into MIS evaluation for the first time. We propose an eXplainable AI (XAI)-driven “Intelligence-to-Wisdom” evolution pathway, integrating multi-level semantic modeling with clinically grounded assessment to establish a comprehensive DIKIW-aligned method taxonomy. Key bottlenecks—including black-box decision-making and inadequate early-lesion representation—are identified, and deployable transparency-enhancing solutions are provided. Our framework significantly improves both interpretability and early detection rates for cancerous lesions, offering theoretical foundations and practical guidelines for high-assurance clinical AI systems.

Technology Category

• Computer Vision: Segmentation
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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.

Problem

• Improving medical image segmentation using deep neural networks
• Enhancing explainability in AI for transparency and ethics
• Addressing challenges in DNN-based MIS for disease diagnosis

Innovation

• Deep Neural Networks for medical image segmentation
• Explainable AI to enhance transparency in DNNs
• Early disease detection using intelligent vision systems