Summary of Enhancing Skin Cancer Diagnosis (scd) Using Late Discrete Wavelet Transform (dwt) and New Swarm-based Optimizers, by Ramin Mousa et al.
Enhancing Skin Cancer Diagnosis (SCD) Using Late Discrete Wavelet Transform (DWT) and New Swarm-Based Optimizers
by Ramin Mousa, Saeed Chamani, Mohammad Morsali, Mohammad Kazzazi, Parsa Hatami, Soroush Sarabi
First submitted to arxiv on: 30 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach for skin cancer detection utilizes a novel combination of pre-trained networks, wavelet transformations, and self-attention modules. By optimizing weight vectors using three new swarm-based optimization techniques, the model achieves diagnostic accuracy rates of up to 98.11% on the ISIC-2016 dataset and 97.95% on the ISIC-2017 dataset, outperforming other methods by at least 1%. The approach is based on extracting hierarchical features from input images using pre-trained networks such as Densenet-121, Inception, Xception, and MobileNet, followed by wavelet transformations to capture low and high-frequency components. Self-attention modules are then used to learn global dependencies between features and focus on the most relevant parts of the feature maps. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The proposed approach uses a combination of deep learning techniques to improve skin cancer detection accuracy. The method involves using pre-trained networks, wavelet transformations, and self-attention modules to extract and process image features. By optimizing weight vectors using new optimization algorithms, the model can achieve high diagnostic accuracy rates. This approach shows promise for improving early intervention and treatment options for skin cancer patients. |
Keywords
» Artificial intelligence » Deep learning » Optimization » Self attention
