An Efficient Mobile Application for Real-time Detection and Classification of Skin Cancer

Skin cancer is one of the deadliest types of cancers that need to be detected at an early stage as it can widely spread affecting other organs of the body. Thus, our objective is to develop an efficient mobile application dedicated to the early detection of skin cancer in primary healthcare at a low cost. In the design of an Android-based mobile application, EfficientNetV2 deep learning model achieved the highest accuracy and feasibility of deployment as an offline model compared to other CNN pre-trained models. The designed deep learning system classifies three classes: healthy, benign, and malignant skin lesions, and further classification of malignant lesions into Basal Cell Carcinoma, Melanoma, or Nevus (the most common skin cancer malignancies) is performed. The developed two-stage classification system has achieved the highest accuracy with 91% and 90% using the EfficientNetV2-model in the designed application.Clinical relevance: Skin cancer is a severe disease that requires early detection, so we are proposing an efficient mobile application that would help dramatically in increasing survival rates by providing a free tool for real-time detection and classification of suspicious mole captured images.