Improved YOLO V3 Network for Basal Cell Carcinomas and Bowen’s Disease Detection

Abstract The pathologists’ workload is very heavy because they have to identify and diagnose a large number of histological images in daily work. So, there is an urgent need to develop a method for detecting and identifying skin cancer. Basal cell carcinomas (BCC) and Bowen’s disease (squamous cell carcinoma in situ) are common malignant skin cancers. In this paper, we focus on the study of YOLO detection model in object detection, and use the YOLOv3 model to detect and identify BCC and Bowen disease. The results show that the model can effectively detect most lesions. Through the structural analysis of the YOLOv3 model algorithm, it is found that the number of convolutional feature maps in some layers’ changes drastically. So, a new convolutional layer is added to gradually reduce the number of feature maps, and then an improved YOLOv3 algorithm is proposed. The improved algorithm is used to detect the lesions of the BCC and the Bowen’s disease. The results show that the improved model algorithm has better detection performance. Under the condition of threshold value of 0.7, the recognition accuracy of BCC and Bowen's disease is 91.3% and 90.9% respectively.