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      Computer-aided Diagnosis and Analysis of Skin Cancer from Dermoscopic Images in India

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          Abstract

          Background

          Researchers have made several advancements in this field, including automatic segmentation techniques, computer-aided diagnosis, mobile-based technology, deep learning methods, hybrid methods etc. All these techniques are beneficial in diagnosing melanoma or segregating skin lesions into different categories.

          Aim

          This paper aims to define different types of skin cancers, diagnosis procedures and statistics. This paper presents skin cancer statistics over a period of time in India. The increment in the number of skin carcinoma and melanoma cases from 1990 to 2020 as well as the mortality rates, has been presented in this paper. Also, this paper provides a review of different technologies used by researchers in detecting melanoma.

          Conclusion

          The rise in the number of cases by 2040 and mortality rates are compared. The statistics that are used in this paper are as per hospital-based cancer registries (HBCR) 2021 prepared by the Indian Council of Medical Research - National Centre for Disease Informatics and Research, Bengaluru (ICMR-NCDIR) and from World Health Organization (WHO).

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          Most cited references78

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          U-Net: Convolutional Networks for Biomedical Image Segmentation

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            Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet.

            There is a need for better standardization of the dermoscopic terminology in assessing pigmented skin lesions. The virtual Consensus Net Meeting on Dermoscopy was organized to investigate reproducibility and validity of the various features and diagnostic algorithms. Dermoscopic images of 108 lesions were evaluated via the Internet by 40 experienced dermoscopists using a 2-step diagnostic procedure. The first-step algorithm distinguished melanocytic versus nonmelanocytic lesions. The second step in the diagnostic procedure used 4 algorithms (pattern analysis, ABCD rule, Menzies method, and 7-point checklist) to distinguish melanoma versus benign melanocytic lesions. kappa Values, log odds ratios, sensitivity, specificity, and positive likelihood ratios were estimated for all diagnostic algorithms and dermoscopic features. Interobserver agreement was fair to good for all diagnostic methods, but it was poor for the majority of dermoscopic criteria. Intraobserver agreement was good to excellent for all algorithms and features considered. Pattern analysis allowed the best diagnostic performance (positive likelihood ratio: 5.1), whereas alternative algorithms revealed comparable sensitivity but less specificity. Interobserver agreement on management decisions made by dermoscopy was fairly good (mean kappa value: 0.53). The virtual Consensus Net Meeting on Dermoscopy represents a valid tool for better standardization of the dermoscopic terminology and, moreover, opens up a new territory for diagnosing and managing pigmented skin lesions.
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              Superior skin cancer classification by the combination of human and artificial intelligence

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                Author and article information

                Journal
                CMIR
                Curr Med Imaging
                Current Medical Imaging
                Curr. Med. Imaging
                Bentham Science Publishers
                1573-4056
                1875-6603
                2024
                : 20
                : E100423215589
                Affiliations
                [1 ]Department of Electronics and Communication Engineering, Chandigarh University, Punjab 140413, India
                Author notes
                [* ]Address correspondence to this author at the Department of Electronics and Communication Engineering, Chandigarh University, Punjab 140413, India; E-mail: khushmeenbrar4u@ 123456gmail.com
                Article
                CMIR-20-E100423215589
                10.2174/1573405620666230410092618
                79d2b279-966d-4955-9d5f-a929ce3c103e
                © 2024 Brar and Shiney

                © 2024 The Author(s). Published by Bentham Science Publisher. This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode

                History
                : 22 September 2022
                : 25 January 2023
                : 01 February 2023
                Categories
                Medicine, Imaging, Radiology, Nuclear Medicine

                Medicine,Chemistry,Life sciences
                Skin carcinoma,Mortality,Statistics,Computer-aided diagnosis,Melanoma,Non-melanoma

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