Derekas, P.; Spyridonos, P.; Likas, A.; Zampeta, A.; Gaitanis, G.; Bassukas, I. The Promise of Semantic Segmentation in Detecting Actinic Keratosis Using Clinical Photography in the Wild. Cancers2023, 15, 4861.
Derekas, P.; Spyridonos, P.; Likas, A.; Zampeta, A.; Gaitanis, G.; Bassukas, I. The Promise of Semantic Segmentation in Detecting Actinic Keratosis Using Clinical Photography in the Wild. Cancers 2023, 15, 4861.
Derekas, P.; Spyridonos, P.; Likas, A.; Zampeta, A.; Gaitanis, G.; Bassukas, I. The Promise of Semantic Segmentation in Detecting Actinic Keratosis Using Clinical Photography in the Wild. Cancers2023, 15, 4861.
Derekas, P.; Spyridonos, P.; Likas, A.; Zampeta, A.; Gaitanis, G.; Bassukas, I. The Promise of Semantic Segmentation in Detecting Actinic Keratosis Using Clinical Photography in the Wild. Cancers 2023, 15, 4861.
Abstract
AK is a common precancerous skin condition that requires effective detection and treatment monitoring. To improve the monitoring of AK burden in clinical settings with enhanced automation and precision, the present study evaluates the application of semantic segmentation based on the U-Net architecture (AKU-Net). AKU-Net employs transfer learning to compensate for the relatively small dataset of annotated images and integrates a recurrent process based on convLSTM to exploit contextual information and address the challenges related to low contrast and ambiguous boundaries of AK-affected skin regions. We used an annotated dataset of 569 clinical photographs from 115 patients with actinic keratosis to train and evaluate the model. From each photograph, patches of 512x512 pixels were extracted using translation lesion boxes that encompass lesions in different positions and capture different contexts of perilesional skin. In total, 16488 translation augmented crops were used for training the model, and 403 lesion center crops were used for testing. To demonstrate the improvements in AK detection, AKU-net was compared with plain U-Net and U-Net++ architectures. The experimental results highlighted the effectiveness of AKU-Net, improving both automation and precision over existing approaches, paving the way for more effective and reliable evaluation of actinic keratosis in clinical settings.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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