Skin cancer diagnosis using hybrid deep pre-trained convolutional neural networks
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Skin cancer diagnosis using hybrid deep pre-trained convolutional neural networks |
| 2. | Creator | Author's name, affiliation, country | Maha Ibrahim Khaleel; University of Qom; Iraq |
| 2. | Creator | Author's name, affiliation, country | Amir Lakizadeh; Alsafwa University College; Iraq |
| 3. | Subject | Discipline(s) | Skin Cancer Diagnosis Using Hybrid Deep Pre-Trained Convolutional Neural Networks and Minimum Redundancy Maximum Relevance Feature Selection Algorithm |
| 3. | Subject | Keyword(s) | Convolutional neural networks; Feature selection; MRMR algorithm; Pre-trained networks; Skin cancer |
| 4. | Description | Abstract | As a variant of skin cancer, melanoma represents a substantial menace to the health and overall well-being of individuals. Statistics reveal that 55% of skin cancer patients succumb to this particular disease. However, early detection plays a crucial role in reducing mortality rates and saving lives. In the past several decades, there has been a rise in the adoption of deep learning algorithms, capturing the interest of researchers working in this field. One popular method involves utilizing pre-trained deep neural networks. In this study, a hybrid approach is employed to extract features from melanoma images. This approach integrates the utilization of pre-trained architectures, including AlexNet, ResNet-50, and GoogleNet. During the transfer training phase, these networks are fine-tuned to detect skin cancer by adjusting the learning rate. Subsequently, the maximum relevance minimum redundancy (MRMR) algorithm is employed to select optimal features based on the concepts of minimum redundancy and maximum relevance in order to minimize feature redundancy and enhance classification accuracy. The bagging technique is employed for the classification of various skin cancer types. The experimental results demonstrate the success of the suggested approach, yielding 98.9% accuracy. Furthermore, the results indicate the superiority of this method according to precision, recall, and F1-score in comparison with existing algorithms. |
| 5. | Publisher | Organizing agency, location | Institute of Advanced Engineering and Science |
| 6. | Contributor | Sponsor(s) | maha ibrahim ,Department of Computer Engineering and Information Technology, University of Qom, Qom, Iran |
| 7. | Date | (YYYY-MM-DD) | 2025-06-01 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://ijai.iaescore.com/index.php/IJAI/article/view/25716 |
| 10. | Identifier | Digital Object Identifier (DOI) | http://doi.org/10.11591/ijai.v14.i3.pp2291-2301 |
| 11. | Source | Title; vol., no. (year) | IAES International Journal of Artificial Intelligence (IJ-AI); Vol 14, No 3: June 2025 |
| 12. | Language | English=en | en |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2025 Institute of Advanced Engineering and Science This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. |