Actinic Keratosis Prediction Based on Deep Learning Methods

Actinic Keratosis (AK) is a type of skin lesion that typically appears on skin areas that are exposed to the sun. It is considered a precursor to squamous cell carcinoma (SCC), which is a common form of skin cancer worldwide. Early detection and treatment of AK are essential for effective management of SCC. Recent developments in deep learning (DL) have shown significant promise in improving the detection and diagnosis of AK and SCC. This study aimed to evaluate the use of YOLOv7, a state-of-the-art object detection model, in identifying AK lesions in skin images. We compared the accuracy and efficiency of YOLOv7 and YOLOv7-tiny models to determine the most suitable model for AK lesion detection. The results of the experiment were promising and showed that YOLOv7 can effectively identify AK lesions in skin images with high accuracy and speed. Furthermore, the study utilized the Grad-CAM technique to gain a deeper understanding of how the DL models detect AK lesions in skin images. We hope that this technology can assist dermatologists in making clinical decisions, leading to early treatment and prevention of AK, and ultimately preventing the development of skin cancer. Overall, the findings of this study highlight the potential of DL models in dermatology and their usefulness in improving clinical practice. With the increasing prevalence of skin cancer worldwide, the use of DL models may play a critical role in the early detection and prevention of skin lesions.