Version 1
: Received: 10 July 2024 / Approved: 10 July 2024 / Online: 10 July 2024 (13:33:07 CEST)
How to cite:
Yuan, C.; Zhao, D.; Agaian, S. S. UCM-NetV2 and BNN-UCM-NetV2: Efficient and Accurate Deep Learning Models for Skin Lesion Segmentation on Mobile Devices. Preprints2024, 2024070866. https://doi.org/10.20944/preprints202407.0866.v1
Yuan, C.; Zhao, D.; Agaian, S. S. UCM-NetV2 and BNN-UCM-NetV2: Efficient and Accurate Deep Learning Models for Skin Lesion Segmentation on Mobile Devices. Preprints 2024, 2024070866. https://doi.org/10.20944/preprints202407.0866.v1
Yuan, C.; Zhao, D.; Agaian, S. S. UCM-NetV2 and BNN-UCM-NetV2: Efficient and Accurate Deep Learning Models for Skin Lesion Segmentation on Mobile Devices. Preprints2024, 2024070866. https://doi.org/10.20944/preprints202407.0866.v1
APA Style
Yuan, C., Zhao, D., & Agaian, S. S. (2024). UCM-NetV2 and BNN-UCM-NetV2: Efficient and Accurate Deep Learning Models for Skin Lesion Segmentation on Mobile Devices. Preprints. https://doi.org/10.20944/preprints202407.0866.v1
Chicago/Turabian Style
Yuan, C., Dongfang Zhao and Sos S. Agaian. 2024 "UCM-NetV2 and BNN-UCM-NetV2: Efficient and Accurate Deep Learning Models for Skin Lesion Segmentation on Mobile Devices" Preprints. https://doi.org/10.20944/preprints202407.0866.v1
Abstract
Accurate segmentation of skin lesions from dermoscopic images is crucial for early skin cancer detection, yet variations in lesion appearance and image artifacts present challenges. This study proposes two deep learning models, UCM-NetV2 and BNN-UCM-NetV2, to improve accuracy and computational efficiency. UCM-NetV2 enhances the UCM-Net architecture with a novel "cyber-structure" combining Multilayer Perceptron and CNN layers, improving prediction accu-racy without increasing the model's parameter count. BNN-UCM-NetV2 further optimizes for mobile deployment by binarizing activations and weights into 1-bit values, reducing computa-tional complexity and model size. Evaluations on the ISIC2017 and ISIC2018 datasets demonstrate that UCM-NetV2 outperforms existing methods in both accuracy and computational efficiency, achieving this with less than 0.04 GFLOPs. These models have the potential to make skin lesion analysis accessible to a broader population, particularly in resource-limited settings, allowing for proactive skin health monitoring and facilitating teledermatology. To foster further innovation in mobile health diagnostics, the source code for UCM-NetV2 will be available on GitHub: https://github.com/chunyuyuan/UCMV2-Net.
Keywords
Medical image segmentation; Lightweight model; Mobile health; Binary neural network
Subject
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.
