Analysis Of Skin Cancer Detection Using Svm & Resnet-50


  • Rafik Ahmad, Kalyan Achariya


ABCD criteria, Melanoma, Skin cancer, CNN, ANN


The paper utilizes machine learning algorithms that incorporate Support Vector Machines (SVM) and Resnet-50, in detecting skin cancer from dermoscopy images. The study evaluates the performance of both models using accuracy, confusion matrix, graphs, and Receiver Operating Characteristics (ROC) to determine which model is more effective in skin cancer detection. Previous studies suggest that Resnet-50 outperforms SVM in terms of detection accuracy. Therefore, this paper also demonstrates the potential of combining both models to improve skin cancer detection accuracy. The outcomes of this study hold substantial inference for the field of clinical practice. By using computer-aided diagnosis (CAD) systems, clinicians can make more accurate diagnoses of skin cancer, reducing interobserver variability and improving objectivity. This research underscores the capacity of machine learning models to transform the aspect of skin cancer diagnosis and treatment, ultimately leading to enhanced patient outcomes. The abstract offers valuable perspectives on the efficiency of machine learning models in the realm of skin cancer detection, rendering it a valuable point of reference for researchers and clinicians exploring the usage of machine learning canon in this domain.


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How to Cite

Kalyan Achariya, R. A. . (2024). Analysis Of Skin Cancer Detection Using Svm & Resnet-50. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1267–1274. Retrieved from



Research Article