A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks
It tackles the problem of enhancing medical image segmentation for disease diagnosis, particularly cancer, but is incremental as it reviews and synthesizes existing methods rather than introducing new ones.
This paper conducts a comprehensive study on medical image segmentation using deep neural networks, focusing on state-of-the-art solutions across DIKIW levels and addressing challenges like explainable AI to improve efficiency in disease diagnosis.
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.