INTERNATIONAL RESEARCH JOURNAL OF MODERNIZATION IN ENGINEERING TECHNOLOGY AND SCIENCE

Abstract

Melanoma, a type of skin cancer, is one of the deadliest forms of cancer if not detected early. In recent years, convolutional neural networks (CNNs) have shown promising results in medical image analysis tasks including skin cancer detection. This study proposes a CNN-based approach for melanoma detection using dermatoscopic images. The proposed algorithm preprocesses the images, extracts features using CNN architecture, and classifies the images as malignant or benign. We evaluate the performance of our method using standard metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Experimental results on benchmark datasets demonstrate the effectiveness of the proposed CNN algorithm in accurately identifying melanoma, thereby aiding in early diagnosis and timely treatment, ultimately improving patient outcomes and reducing mortalityrates.Keywords : Melanoma , CNN

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