Version 1
: Received: 16 July 2024 / Approved: 16 July 2024 / Online: 16 July 2024 (11:01:23 CEST)
How to cite:
Manole, I.; Butacu, A.-. I.; Bejan, R. N.; Tiplica, G. S. Enhancing Dermatological Diagnostics with EfficientNet: A Deep Learning Approach. Preprints2024, 2024071322. https://doi.org/10.20944/preprints202407.1322.v1
Manole, I.; Butacu, A.-. I.; Bejan, R. N.; Tiplica, G. S. Enhancing Dermatological Diagnostics with EfficientNet: A Deep Learning Approach. Preprints 2024, 2024071322. https://doi.org/10.20944/preprints202407.1322.v1
Manole, I.; Butacu, A.-. I.; Bejan, R. N.; Tiplica, G. S. Enhancing Dermatological Diagnostics with EfficientNet: A Deep Learning Approach. Preprints2024, 2024071322. https://doi.org/10.20944/preprints202407.1322.v1
APA Style
Manole, I., Butacu, A. . I., Bejan, R. N., & Tiplica, G. S. (2024). Enhancing Dermatological Diagnostics with EfficientNet: A Deep Learning Approach. Preprints. https://doi.org/10.20944/preprints202407.1322.v1
Chicago/Turabian Style
Manole, I., Raluca Nicoleta Bejan and George Sorin Tiplica. 2024 "Enhancing Dermatological Diagnostics with EfficientNet: A Deep Learning Approach" Preprints. https://doi.org/10.20944/preprints202407.1322.v1
Abstract
Background: Despite recent advancements, medical technology has not yet reached its peak. Precision medicine is growing rapidly, thanks to machine learning breakthroughs powered by increased computational capabilities. This article explores a deep-learning application for computer-aided diagnosis in dermatology. Methods: Using a custom model based on EfficientNetB3 and deep learning, we propose an approach for skin lesion classification that offers superior results with smaller, cheaper, and faster inference times compared to other models. The skin images dataset used for this research includes 8,222 files selected from the authors' collection and the ISIC2019 archive, covering six dermatological conditions. Results: The model achieved 95.4% validation accuracy on four categories—melanoma, basal cell carcinoma, benign keratosis-like lesions, and melanocytic nevi—using an average of 1,600 images per category. Adding two categories with fewer images (about 700 each)—squamous cell carcinoma and actinic keratoses—reduced the validation accuracy to 88.8%. The model maintained accuracy on new clinical test images taken under the same conditions as the training dataset. Conclusions: The custom model demonstrated excellent performance on the diverse skin lesions dataset, with significant potential for further enhancements.
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.
