Identification of Early Esophageal Cancer by Semantic Segmentation

Fang, Yu‐Jen; Mukundan, Arvind; Tsao, Yu-Ming; Huang, Chien‐Wei; Wang, Hsiang‐Chen

doi:10.3390/jpm12081204

Cited by 43 publications

(26 citation statements)

References 39 publications

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“…Goda et al also predicted that AI may be applied to improve diagnostic criteria for B2 vessels and modify the JES classification (152). At the same time, the combination of the emerging hyperspectral imaging (HSI) technology and AI in recent years has further improved the accuracy of early esophageal cancer detection (153)(154)(155). We believe that NBI combined with AI will be widely used in the diagnosis of diseases, and more simple classifications will be used in clinical practice to alleviate the workload of clinicians in the future.…”

Section: Future Prospect and Conclusionmentioning

confidence: 99%

A narrative review: narrow-band imaging endoscopic classifications

Yang¹,

Liu²,

Sun³

et al. 2023

Quant Imaging Med Surg

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Background and Objective: With the development of endoscopic techniques, narrow-band imaging (NBI) has been widely used in the diagnosis of various types of diseases. NBI can detect mucosal lesions at an early stage and different classification strategies have been established to help clinicians in disease diagnosis. However, there is currently no consensus for the classification criteria. This report summarizes the current classifications of diseases using NBI, so as to provide a comprehensive understanding of the various manifestations of mucosal lesions under NBI, and to promote the development of more practical NBI classifications.Methods: The PubMed database was searched for English language articles published between January 1994 and November 2021 using the keywords 'narrow band imaging', 'NBI', and 'classification'.

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Section: Future Prospect and Conclusionmentioning

confidence: 99%

A narrative review: narrow-band imaging endoscopic classifications

Yang¹,

Liu²,

Sun³

et al. 2023

Quant Imaging Med Surg

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“…There are also some other early prediction models based on segmentation results such as brain-related disease [130] - [132], evaluation of bone tumors [133], lung disease [134] - [136], breast cancer [137] - [138], tumor metastasis of ovarian cancer patients [139], stroke, ischemic coma [140] - [141], acute pancreatitis [142], and cancer radiation [143]- [144].…”

Section: A Some Typical Applicationsmentioning

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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“…HSI acquires the spectrum for each pixel in an image [ 18 , 19 , 20 , 21 ]. It has been used in many applications, such as cancer detection [ 22 , 23 , 24 , 25 ], air pollution monitoring [ 26 , 27 ], nanostructure identification [ 28 , 29 , 30 , 31 ], aerospace [ 32 , 33 , 34 ], food quality maintenance [ 35 ], verification [ 36 , 37 , 38 ], military [ 39 ], remote sensing [ 40 , 41 , 42 ], and agriculture [ 43 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Counterfeit Currency Detection Using a Novel Snapshot Hyperspectral Imaging Algorithm

Mukundan

Tsao

Cheng

et al. 2023

Sensors

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In this study, a snapshot-based hyperspectral imaging (HSI) algorithm that converts RGB images to HSI images is designed using the Raspberry Pi environment. A Windows-based Python application is also developed to control the Raspberry Pi camera and processor. The mean gray values (MGVs) of two distinct regions of interest (ROIs) are selected from three samples of 100 NTD Taiwanese currency notes and compared with three samples of counterfeit 100 NTD notes. Results suggest that the currency notes can be easily differentiated on the basis of MGV values within shorter wavelengths, between 400 nm and 500 nm. However, the MGV values are similar in longer wavelengths. Moreover, if an ROI has a security feature, then the classification method is considerably more efficient. The key features of the module include portability, lower cost, a lack of moving parts, and no processing of images required.

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Identification of Early Esophageal Cancer by Semantic Segmentation

Cited by 43 publications

References 39 publications

A narrative review: narrow-band imaging endoscopic classifications

A narrative review: narrow-band imaging endoscopic classifications

A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks

Automatic Counterfeit Currency Detection Using a Novel Snapshot Hyperspectral Imaging Algorithm

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