Convolutional Neural Network (CNN) for Automatic Skin Cancer Classification System

Abstract

Skin cancer is a type of cancer that grows in the skin tissue, which can cause damage to the surrounding tissue, disability, and even death. In Indonesia, skin cancer is the third leading for most cancer cases after cervical and breast cancer. The accuracy of diagnosis and the early proper treatment can minimize and control the harmful effects of skin cancer. Due to the similar shape of the lesion between skin cancer and benign tumor lesions, physicians consuming much more time in diagnosing these lesions. The system was developed in this study could identify skin cancer and benign tumor lesions automatically using the Convolutional Neural Network (CNN). The proposed model consists of three hidden layers with an output channel of 16,32, and 64 for each layer respectively. The proposed model uses several optimizers such as SGD, RMSprop, Adam, and Nadam with a learning rate of 0.001. Adam optimizer provides the best performance with an accuracy value of 99% in identifying the skin lesions from the ISIC dataset into 4 classes, namely dermatofibroma, nevus pigmentosus, squamous cell carcinoma, and melanoma. The results obtained outperform the performance of the existing skin cancer classification system.

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“…In this paper [11], an automated system is proposed by the authors to identify the situations of di↵erent types of skin cancers from digital image processing. However the CNN model is adopted in this paper [11] containing 3 hidden layers which uses 3×3 filter sizes with 16, 32, and 64 channel outcomes in cycle and has a completely connected layer and softmax activation. They have conducted their optimization utilizing Adam, SGD, RMSprop, and Nadam optimizer on their proposed model and eventually based on their testing the authors found that the Adam optimizer gives the best interpretation of skin cancer lesions with 99% of accurateness from the dataset with the number of precision, recall and F1-is nearly 1.…”