Skin Lesion Classification with Deep Convolutional Neural Network: Process Development and Validation

Abstract

Background: Skin cancer is the most common cancer and is often ignored by people at an early stage. There are 5.4 million new cases of skin cancer worldwide every year. Deaths due to skin cancer could be prevented by early detection of the mole. Objective: We propose a skin lesion classification system that has the ability to detect such moles at an early stage and is able to easily differentiate between a cancerous and noncancerous mole. Using this system, we would be able to save time and resources for both patients and practitioners. Methods: We created a deep convolutional neural network using an Inceptionv3 and DenseNet-201 pretrained model. Results: We found that using the concepts of fine-tuning and the ensemble learning model yielded superior results. Furthermore, fine-tuning the whole model helped models converge faster compared to fine-tuning only the top layers, giving better accuracy overall. Conclusions: Based on our research, we conclude that deep learning algorithms are highly suitable for classifying skin cancer images.