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AI-Enabled Support System for Melanoma Detection and Classification

AI-Enabled Support System for Melanoma Detection and Classification

Vivek Sen Saxena, Prashant Johri, Avneesh Kumar
Copyright: © 2021 |Volume: 10 |Issue: 4 |Pages: 18
ISSN: 2160-9551|EISSN: 2160-956X|EISBN13: 9781799862512|DOI: 10.4018/IJRQEH.2021100104
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MLA

Sen Saxena, Vivek, et al. "AI-Enabled Support System for Melanoma Detection and Classification." IJRQEH vol.10, no.4 2021: pp.58-75. http://doi.org/10.4018/IJRQEH.2021100104

APA

Sen Saxena, V., Johri, P., & Kumar, A. (2021). AI-Enabled Support System for Melanoma Detection and Classification. International Journal of Reliable and Quality E-Healthcare (IJRQEH), 10(4), 58-75. http://doi.org/10.4018/IJRQEH.2021100104

Chicago

Sen Saxena, Vivek, Prashant Johri, and Avneesh Kumar. "AI-Enabled Support System for Melanoma Detection and Classification," International Journal of Reliable and Quality E-Healthcare (IJRQEH) 10, no.4: 58-75. http://doi.org/10.4018/IJRQEH.2021100104

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Abstract

Skin lesion melanoma is the deadliest type of cancer. Artificial intelligence provides the power to classify skin lesions as melanoma and non-melanoma. The proposed system for melanoma detection and classification involves four steps: pre-processing, resizing all the images, removing noise and hair from dermoscopic images; image segmentation, identifying the lesion area; feature extraction, extracting features from segmented lesion and classification; and categorizing lesion as malignant (melanoma) and benign (non-melanoma). Modified GrabCut algorithm is employed to generate skin lesion. Segmented lesions are classified using machine learning algorithms such as SVM, k-NN, ANN, and logistic regression and evaluated on performance metrics like accuracy, sensitivity, and specificity. Results are compared with existing systems and achieved higher similarity index and accuracy.

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