Actinic Keratosis Prediction Based on Deep Learning Methods

Guan Yi He, Chi Ping Su, Chung Shuo Chen, Yao Sung Hsiang, Wei Huan Hu, Shin Jye Lee*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationProgress on Pattern Classification, Image Processing and Communications - Proceedings of the CORES and IP and C Conferences 2023
EditorsRobert Burduk, Paweł Ksieniewicz, Paweł Trajdos, Michał Choraś, Rafał Kozik, Tomasz Marciniak
PublisherSpringer Science and Business Media Deutschland GmbH
Pages111-120
Number of pages10
ISBN (Print)9783031416293
DOIs
StatePublished - 2023
Event13th International Conference on Image Processing and Communications, IP and C 2023, 13th International Conference on Computer Recognition Systems, CORES 2023 - Virtual, Online
Duration: 28 Jun 202329 Jun 2023

Publication series

NameLecture Notes in Networks and Systems
Volume766
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference13th International Conference on Image Processing and Communications, IP and C 2023, 13th International Conference on Computer Recognition Systems, CORES 2023
CityVirtual, Online
Period28/06/2329/06/23

Keywords

  • Actinic Keratosis prediction
  • Deep learning
  • Smart healthcare

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