Abstract
Skin cancer is a common disease that affects mankind significantly every year there are more new cases of skin cancer than the combined incidence of cancers of the breast, prostate, lung, and colon. With over 5,000,000 new cases every year skin cancer is a concerning public health predicament. Melanoma and non-melanoma are the two main kinds of skin cancer, respectively. Melanoma is a malignant tumor. The 19th most common malignancy in both men and women is melanoma. The deadliest types of skin cancer are melanoma, which can spread quickly. The crucial factor in Melanoma cancer treatment is early diagnosis. Doctors usually prefer the biopsy method for skin cancer detection. During a biopsy, a sample from a suspected skin lesion is removed for medical examination to determine if it is cancerous or not a biopsy is a painful, slow, and time-consuming method. This study proposes an end-to-end decision-based system classifiers for example like neural networks. Convolutional Neural Networks (CNN) will be used to classify melanoma or benign. CNN architectures are appropriate classifiers to distinguish between the images of moles on the skin. This study has used images from both clinical and dermoscopic images Med-node and ISIC. The procedure advised in Melanoma detection shall capture images and preprocess. Segment the acquired preprocessed image and extract the desired feature and classify them as Melanoma or benign. The model has given an accuracy of 94%, and Sensitivity and Specificity are at 0.87 and 0.89 respectively.
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Shashidhara, G.M., Agarwal, R., Suryavamshi, J. (2023). Identify Melanoma Using CNN. In: Nandan Mohanty, S., Garcia Diaz, V., Satish Kumar, G.A.E. (eds) Intelligent Systems and Machine Learning. ICISML 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 470. Springer, Cham. https://doi.org/10.1007/978-3-031-35078-8_14
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