Version 1
: Received: 12 December 2023 / Approved: 13 December 2023 / Online: 13 December 2023 (04:50:43 CET)
How to cite:
Halabi, D. Enhancing Skin Cancer Detection and Classification: Exploring the Impact of Attention Mechanisms in Transfer Learning Models. Preprints2023, 2023120943. https://doi.org/10.20944/preprints202312.0943.v1
Halabi, D. Enhancing Skin Cancer Detection and Classification: Exploring the Impact of Attention Mechanisms in Transfer Learning Models. Preprints 2023, 2023120943. https://doi.org/10.20944/preprints202312.0943.v1
Halabi, D. Enhancing Skin Cancer Detection and Classification: Exploring the Impact of Attention Mechanisms in Transfer Learning Models. Preprints2023, 2023120943. https://doi.org/10.20944/preprints202312.0943.v1
APA Style
Halabi, D. (2023). Enhancing Skin Cancer Detection and Classification: Exploring the Impact of Attention Mechanisms in Transfer Learning Models. Preprints. https://doi.org/10.20944/preprints202312.0943.v1
Chicago/Turabian Style
Halabi, D. 2023 "Enhancing Skin Cancer Detection and Classification: Exploring the Impact of Attention Mechanisms in Transfer Learning Models" Preprints. https://doi.org/10.20944/preprints202312.0943.v1
Abstract
Skin cancer is a global health concern with increasing prevalence, necessitating effective early detection and classification systems. Transfer learning has emerged as a powerful tool in this domain, enhancing diagnostic accuracy. Attention mechanisms, which selectively focus on pertinent image features, play a pivotal role in transfer learning. While previous studies have highlighted their effectiveness, their applicability across different models and datasets remains understudied. This empirical study explores the impact of various attention mechanisms on the performance of transfer learning models for skin cancer detection and classification. Using five transfer learning models (DenseNet121, InceptionV3, MobileNet, VGG16, and Xception) and six attention mechanisms (Channel Attention, Global Context Attention, Guided Attention, Nonlocal Attention, Positional Attention, and Spatial Attention), we conducted 105 experiments across three datasets. Traditional metrics (accuracy, precision, recall, and f1-score) were employed for empirical validation. The results reveal a nuanced relationship between attention mechanisms, transfer learning models, and datasets. Overall, attention mechanisms exhibit the potential to enhance skin cancer classification. Spatial and channel attention mechanisms consistently outperform others, offering simplicity and effectiveness. Model-specific selection of attention mechanisms is crucial, with a trade-off between model complexity and performance evident. This study provides insights into developing efficient skin cancer classification models utilizing attention mechanisms.
Keywords
Skin Cancer; transfer learning; attention; computer vision
Subject
Medicine and Pharmacology, Other
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.
