A Deep Learning Approach to Diagnose Skin Cancer Using Image Processing

Abstract

Skin cancer is the most commonly diagnosed cancer in the United States with over a million cases being detected each year. Fortunately, early detection provides high odds of recovery. The traditional method of detection involves clinical screening, which is prone to false positives, followed by an invasive biopsy. While this provides for a high rate of detection, it is intrusive and costly. Artificial Intelligence for medical image analysis has proved effective in assisting in the diagnosis of many medical maladies, yet fine variations in the appearance of skin lesions has made applications to skin cancer detection difficult. We report that a deep convolutional neural network (CNN) trained over clinically labeled images (pixels) can accurately assist in the diagnosis of early-stage skin cancers. Specifically, we analyze skin lesions using CNN and evaluate its performance on seven dermatologist-certified clinical image types: Actinic keratoses and intraepithelial carcinoma (Bowen’s disease), basal cell carcinoma, benign keratosis-like lesions (solar lentigines, seborrheic keratoses, and lichen-planus-like keratoses), dermatofibroma, melanoma, melanocytic nevi, and vascular lesions (angiomas, angiokeratomas, pyogenic granulomas, and hemorrhage). The model provides significantly high levels of average accuracy, specificity, and sensitivity across these types.