Towards Enhanced Melanoma Skin Cancer Detection Using Image Processing and Transfer Learning

Registration ID: IJNRD_212026

Published ID: IJNRD2401042

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Hussaini Aliyu Idris, Adamu Muhammad, Umar Abubakar Tsakuwa

Melanoma, a life-threatening form of skin cancer caused by DNA damage from ultraviolet radiation, poses a significant health risk. Early detection plays a critical role in successful treatment outcomes. In this paper, we present a novel approach for the detection of melanoma skin cancer using image processing techniques and transfer learning. Our proposed method aims to improve accuracy compared to existing state-of-the-art techniques. We conducted extensive experiments using the publicly available MED-NODE skin cancer dataset, which comprises high-resolution skin lesion images. Our approach leverages image processing algorithms to extract relevant features and employs transfer learning with pre-trained models to enhance classification performance. By fine-tuning a pre-trained model specifically VGG19, we capitalize on the learned representations from a large dataset like ImageNet. The results of our experiments demonstrate the superiority of the proposed approach, exhibiting an impressive improvement of approximately 10% in accuracy compared to existing methods. The validation of our approach using the MED-NODE skin cancer dataset further strengthens its effectiveness in melanoma detection.

Published Paper   E-Certificate

Hussaini Aliyu Idris, Adamu Muhammad, & Umar Abubakar Tsakuwa (January-2024). Towards Enhanced Melanoma Skin Cancer Detection Using Image Processing and Transfer Learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(1), a363-a368. https://ijnrd.org/papers/IJNRD2401042.pdf

Volume 9 Issue 1, January-2024

Pages : a363-a368

Paper Reg. ID: IJNRD_212026

Published Paper Id: IJNRD2401042

Downloads: 000122256

Research Area: Computer Science & Technology 

Author Type: Foreign Author

Country: Ringim, Jigawa, Nigeria

Published Paper PDF: https://ijnrd.org/papers/IJNRD2401042.pdf

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