A lightweight deep convolutional neural network model for skin cancer image classification
Article
Tuncer, Turker, Barua, Prabal Datta, Tuncer, Ilknur, Dogan, Sengul and Acharya, U. Rajendra. 2024. "A lightweight deep convolutional neural network model for skin cancer image classification." Applied Soft Computing. 162. https://doi.org/10.1016/j.asoc.2024.111794
| Article Title | A lightweight deep convolutional neural network model for skin cancer image classification |
|---|---|
| ERA Journal ID | 17759 |
| Article Category | Article |
| Authors | Tuncer, Turker, Barua, Prabal Datta, Tuncer, Ilknur, Dogan, Sengul and Acharya, U. Rajendra |
| Journal Title | Applied Soft Computing |
| Journal Citation | 162 |
| Article Number | 111794 |
| Number of Pages | 8 |
| Year | 2024 |
| Publisher | Elsevier |
| Place of Publication | Netherlands |
| ISSN | 1568-4946 |
| 1872-9681 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.asoc.2024.111794 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S1568494624005684 |
| Abstract | Deep learning models, particularly transformers and convolutional neural networks (CNNs), have been commonly used to achieve high classification accuracy for image data. Since introducing transformers, researchers have predominantly embraced these models to obtain impressive classification rates with novel approaches. In light of this scenario, we present a novel lightweight CNN called TurkerNet. Our primary objective is to attain a superior classification performance while minimizing the number of trainable parameters. TurkerNet comprises four essential components: the input block, residual bottleneck block, efficient block, and output block. To evaluate the performance of our proposed model, we conducted experiments using an open-access image dataset, specifically curated to include skin cancer images classified into two categories: benign and malignant. Our proposed (TurkerNet) model achieved a remarkable testing accuracy of 92.12% on this public dataset. Our model performed better than state-of-the-art techniques developed for automated skin cancer detection. Moreover, our proposed TurkerNet is a lightweight model. In this aspect, the presented TurkerNet is highly accurate with low trainable parameters. |
| Keywords | Image classification; Lightweight CNN ; Skin tumor image classification ; TurkerNet |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 420311. Health systems |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Firat University, Turkey |
| School of Business | |
| Interior Ministry, Turkiye | |
| School of Mathematics, Physics and Computing |
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