Segmentation of Medical X-ray Bone Image Using Different Image Processing Techniques

Isinkaye, Folasade Olubusola; Aluko, Abiodun Gabriel; Jongbo, Olayinka Ayodele

doi:10.5815/ijigsp.2021.05.03

Cited by 4 publications

(1 citation statement)

References 40 publications

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“…TABLE V. compares these four imaging modalities in terms of their advantages, disadvantages, SOTA applications, and health effects [60]. [61], Lung [2], [62], Caries lesion [63] Lung [42], [64] - [66], Proximal femur segmentation [67], Kidney tumor segmentation [68], tooth and alveolar bone segmentation [69] Brain [66], [70] - [72], Retinal Vessel [73] - [75], Cardiac [66], [76] - [78], prostate [66], [39], [78], Osteosarcoma [79] Breast [80], kidney [81], prostate [82], multi-organs [83] Health effects Biological effect, need protection against unnecessary does.…”

Section: B Data 1) Medical Image Modalitiesmentioning

confidence: 99%

A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks

Dao¹,

Ly²

2023

IJACSA

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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.

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