Web Application Development Skin Lesion Classification Using Transfer Learning InceptionResNet-v2

Abstract

The development of machine learning continues from various domains where automation systems are needed. Advanced learning models, such as Convolutional Neural Networks (CNNs) in deep learning, can classify and identify objects even beyond human capabilities. One application is the classification of medical images skin cancer. Automatic diagnosis of skin cancer images is still challenging for CNNs. The use of transfer learning on classification has been leveraged for mobile, accurate, and fast automatic diagnosis. However, such models are imperfect in the categorization of skin lesions. Therefore, this study developed a web application for multiclass classification of 7 classes of disease through Streamlit and HuggingFace, with datasets from HAM10000 using TF Lite-conversion InceptionResNetV2. TF Lite-converted and the model’s classification reports were analyzed. The results on EarlyStopping overall accuracy were 87.56%, top-2 95.05%, and top-3 97.46%. Moreover, latency and classification duration were measured on Streamlit Share and HuggingFace Spaces. The findings are Streamlit has a faster average latency (1.17 ms) than HuggingFace (1.49 ms). The latency standard deviation on HuggingFace less consitent (0.49 ms) than Streamlit (0.10 ms). The HuggingFace classification average duration and standard deviation is 116 ms and 5 ms, while Streamlit is better at 97 ms and 2 ms respectively.