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DOI: 10.14569/IJACSA.2023.0140319
PDF

A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks

Author 1: Loan Dao
Author 2: Ngoc Quoc Ly

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 3, 2023.

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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.

Keywords: Medical image segmentation (MIS); SOTA solutions in MIS; XAI; early disease diagnosis

Loan Dao and Ngoc Quoc Ly, “A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks” International Journal of Advanced Computer Science and Applications(IJACSA), 14(3), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140319

@article{Dao2023,
title = {A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140319},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140319},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {3},
author = {Loan Dao and Ngoc Quoc Ly}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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