Detection and optimization of skin cancer using deep learning
S Balambigai, K Elavarasi, M Abarna, R Abinaya, N Arun Vignesh
Abstract
Abstract Convolutional Neural Network (CNN) is a branch of deep learning which has been one of a popular methods in different applications, especially in medical field. In this study, an optimized CNN model is built using the random search optimization to classify seven types of skin cancer, namely, basal cell carcinoma, melanoma, dermatofibroma, vascular lesion, melanocytic nevus, actinic keratosis and benign keratosis. Total of 10,015 images were collected from the Human Against Machine dataset (HAM10000) which is available in Kaggle, Even though CNN has shown best results in many applications, the hyper-parameters that are required to build CNN model is difficult to choose. If the chosen hyper-parameters doesn’t show good results, the model should be trained again with other set of hyper-parameter values. To avoid this circumstance, the hyper-parameter optimization is required and in this study, it is done using random search optimization. A base CNN model is initially created without using any optimization technique, so that the performance of the CNN model which is optimized by the random search method can be compared and analysed. The first model provided an accuracy of 73.34%, whereas the optimized model shown an improvement in accuracy of 77.17%.