MELANOMA SKIN CANCER DETECTION AND ANALYSIS

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Yashkumar Wankhede; Sonakshi Singh; Omkar Chaskar; Aditya Chaudhary

MELANOMA SKIN CANCER DETECTION AND ANALYSIS

Authors: Yashkumar Wankhede, Sonakshi Singh, Omkar Chaskar, Aditya Chaudhary

Unique Paper ID: 174545
Volume: 11
Issue: 11
PageNo: 285-290

Keywords: No Keywords Found

Abstract:
Early detection of melanoma skin cancer is crucial for effective treatment, as it is the most aggressive type and can spread rapidly if not diagnosed in time. Computer vision and medical image processing play a vital role in non-invasive diagnosis, enabling fast and accurate lesion evaluation through automated analysis. This study follows a systematic approach, starting with the collection of dermoscopic images, followed by preprocessing, segmentation, and feature extraction using techniques like Gray Level Co-occurrence Matrix (GLCM) and Asymmetry, Border, Color, Diameter (ABCD) analysis. Principal Component Analysis (PCA) is applied for feature selection, and the Dermoscopy Score is calculated to aid in classification. A Convolutional Neural Network (CNN) is then used to classify skin cancer, achieving an accuracy of 92.1%. The results highlight the effectiveness of AI-driven methods in improving early melanoma detection, assisting clinicians in diagnosis, and enhancing overall patient care.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 285-290

MELANOMA SKIN CANCER DETECTION AND ANALYSIS

Available:https://ijirt.org/article?manuscript=174545

03 Jun, 2025

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08 Nov, 2024

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Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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