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[2602.17797] Deep Learning for Dermatology: An Innovative Framework for Approaching Precise Skin Cancer Detection
Summary
This article presents a deep learning framework for improving skin cancer detection using VGG16 and DenseNet201 models, achieving an accuracy of 93.79% with DenseNet201 on a dataset of 3,297 images.
Why It Matters
Skin cancer is a leading health concern globally, and early detection is crucial for effective treatment. This research highlights the potential of advanced deep learning techniques to enhance diagnostic accuracy, which could significantly impact patient outcomes and streamline dermatological workflows.
Key Takeaways
- Deep learning models VGG16 and DenseNet201 were evaluated for skin cancer detection.
- DenseNet201 achieved the highest accuracy of 93.79% on the dataset.
- The study emphasizes the importance of early detection in skin cancer treatment.
- Both models showed excellent performance, but further improvements are possible.
- Future work will involve testing with new datasets to enhance accuracy.
Electrical Engineering and Systems Science > Image and Video Processing arXiv:2602.17797 (eess) [Submitted on 19 Feb 2026] Title:Deep Learning for Dermatology: An Innovative Framework for Approaching Precise Skin Cancer Detection Authors:Mohammad Tahmid Noor, B. M. Shahria Alam, Tasmiah Rahman Orpa, Shaila Afroz Anika, Mahjabin Tasnim Samiha, Fahad Ahammed View a PDF of the paper titled Deep Learning for Dermatology: An Innovative Framework for Approaching Precise Skin Cancer Detection, by Mohammad Tahmid Noor and 5 other authors View PDF Abstract:Skin cancer can be life-threatening if not diagnosed early, a prevalent yet preventable disease. Globally, skin cancer is perceived among the finest prevailing cancers and millions of people are diagnosed each year. For the allotment of benign and malignant skin spots, an area of critical importance in dermatological diagnostics, the application of two prominent deep learning models, VGG16 and DenseNet201 are investigated by this paper. We evaluate these CNN architectures for their efficacy in differentiating benign from malignant skin lesions leveraging enhancements in deep learning enforced to skin cancer spotting. Our objective is to assess model accuracy and computational efficiency, offering insights into how these models could assist in early detection, diagnosis, and streamlined workflows in dermatology. We used two deep learning methods DenseNet201 and VGG16 model on a binary class dataset containing 3297 images. The best...