Performance Evaluation of Pre-Trained Convolutional Neural Network Model for Skin Disease Classification

Abstract

Indonesia is a tropical country that has various skin diseases. Tinea versicolor, ringworm, and scabies are the most common types of skin diseases suffered by the people of Indonesia. The classification of the three skin diseases can be automatically completed by artificial intelligence and deep learning technology because the classification process using an expert will require a lot of money and time. The challenge in classifying skin diseases is in the process of collecting data. Because health data cannot be obtained freely, there must be approval from the patient or hospital. Therefore, to overcome the limited amount of data, Pre-Trained CNN is used. The Pre-Trained CNN model has many patterns from thousands of images, so we do not need many images to train the model. In this study, a comparison of five pre-trained CNN models was conducted, namely VGGNet16, MobileNetV2, InceptionResNetV2, ResNet152V2, and DenseNet201. The aim is to find out which CNN model can produce the best performance in classifying skin diseases with a limited amount of image data. The test results show that the ResNet152V2 model has the best classification ability with the highest accuracy, precision, recall, and F1 score values, namely 95.84%, 0.963, 0.96, and 0.956. As for the training execution time, the ResNet152V2 model has the fastest time to achieve 95% accuracy. That's happened because the addition of the dropout parameter is 20%.