@inproceedings{a92346851f3749e9bb0d0fa0f5baa363,
title = "Actinic Keratosis Prediction Based on Deep Learning Methods",
abstract = "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.",
keywords = "Actinic Keratosis prediction, Deep learning, Smart healthcare",
author = "He, {Guan Yi} and Su, {Chi Ping} and Chen, {Chung Shuo} and Hsiang, {Yao Sung} and Hu, {Wei Huan} and Lee, {Shin Jye}",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 13th International Conference on Image Processing and Communications, IP and C 2023, 13th International Conference on Computer Recognition Systems, CORES 2023 ; Conference date: 28-06-2023 Through 29-06-2023",
year = "2023",
doi = "10.1007/978-3-031-41630-9_12",
language = "English",
isbn = "9783031416293",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "111--120",
editor = "Robert Burduk and Pawe{\l} Ksieniewicz and Pawe{\l} Trajdos and Micha{\l} Chora{\'s} and Rafa{\l} Kozik and Tomasz Marciniak",
booktitle = "Progress on Pattern Classification, Image Processing and Communications - Proceedings of the CORES and IP and C Conferences 2023",
address = "Germany",
}