Abstract
There has been an unprecedented rise in the cases of skin diseases since past few decades owing to several factors. Among several skin diseases, skin cancer has also taken a steep rise and resultantly it becomes imperative to devise an efficient model to detect skin cancer. The requirement for automatic detection of skin cancer further grows owing to rise in rate of melanoma skin cancer, its expensive treatment, and its high fatality rate. Treatment of cancer cells frequently necessitates patience and manual inspection. Here, in this work authors propose an image processing and machine learning approach for skin cancer detection. It also uses a feature extraction technique to retrieve the features of the injured skin cells. The proposed model uses convolutional neural network classifier to stratify the extracted data. During the experimental evaluation, it is observed that the proposed system yields an accuracy of 77.03% and a training accuracy of 80% for the datasets available in public domain.
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Saini, V., Rai, N., Sharma, N., Shrivastava, V.K. (2023). A Convolutional Neural Network Based Prediction Model for Classification of Skin Cancer Images. 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_9
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