| Issue |
MATEC Web Conf.
Volume 392, 2024
International Conference on Multidisciplinary Research and Sustainable Development (ICMED 2024)
|
|
|---|---|---|
| Article Number | 01159 | |
| Number of page(s) | 12 | |
| DOI | https://doi.org/10.1051/matecconf/202439201159 | |
| Published online | 18 March 2024 | |
Adaptive dermascopy application using machine learning
Department of Computer Science and Engineering, Institute of Aeronautical Engineering, Hyderabad.
* Corresponding author: maneesha2720@gmail.com
Skin cancer is the most lethal because skin cells develop abnormally. Finding skin cancer early is very important and may help stop some kinds of skin cancer, like melanoma and focal cell carcinoma. Early detection and classification of skin cancer are difficult and costly. Recurrent networks and ConvNets can automatically extract complex data. This paper proposes to use a handmade features-based multi-layer perceptron and a cascaded ensembled network to upgrade ConvNet models. This convolutional neural network model detects non-handmade picture qualities and generates features like color moments and material properties. With ensembled DL, accuracy increased from 85.3% with convolutional neural networks to 98.3%.
Key words: Dermatology / skin lesion classification / color moments / texture features / deep learning / convolution neural network.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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