BrainNeXt: novel lightweight CNN model for the automated detection of brain disorders using MRI images
Article
Poyraz, Melahat, Poyraz, Ahmet Kursad, Dogan, Yusuf, Gunes, Selva, Mir, Hasan S., Paul, Jose Kunnel, Barua, Prabal Datta, Baygin, Mehmet, Dogan, Sengul, Tuncer, Turker, Molinari, Filippo and Acharya, Rajendra. 2025. "BrainNeXt: novel lightweight CNN model for the automated detection of brain disorders using MRI images." Cognitive Neurodynamics. 19 (1). https://doi.org/10.1007/s11571-025-10235-z
| Article Title | BrainNeXt: novel lightweight CNN model for the automated detection of brain disorders using MRI images |
|---|---|
| ERA Journal ID | 3179 |
| Article Category | Article |
| Authors | Poyraz, Melahat, Poyraz, Ahmet Kursad, Dogan, Yusuf, Gunes, Selva, Mir, Hasan S., Paul, Jose Kunnel, Barua, Prabal Datta, Baygin, Mehmet, Dogan, Sengul, Tuncer, Turker, Molinari, Filippo and Acharya, Rajendra |
| Journal Title | Cognitive Neurodynamics |
| Journal Citation | 19 (1) |
| Number of Pages | 17 |
| Year | 2025 |
| Publisher | Springer |
| Place of Publication | Netherlands |
| ISSN | 1871-4080 |
| 1871-4099 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s11571-025-10235-z |
| Web Address (URL) | https://link.springer.com/article/10.1007/s11571-025-10235-z |
| Abstract | The main aim of this study is to propose a novel convolutional neural network, named BrainNeXt, for the automated brain disorders detection using magnetic resonance images (MRI) images. Furthermore, we aim to investigate the performance of our proposed network on various medical applications. To achieve high/robust image classification performance, we gathered a new MRI dataset belonging to four classes: (1) Alzheimer's disease, (2) chronic ischemia, (3) multiple sclerosis, and (4) control. Inspired by ConvNeXt, we designed BrainNeXt as a lightweight classification model by incorporating the structural elements of the Swin Transformers Tiny model. By training our model on the collected dataset, a pretrained BrainNeXt model was obtained. Additionally, we have suggested a feature engineering (FE) approach based on the pretrained BrainNeXt, which extracted features from fixed-sized patches. To select the most discriminative/informative features, we employed the neighborhood component analysis selector in the feature selection phase. As the classifier for our patch-based FE approach, we utilized the support vector machine classifier. Our recommended BrainNeXt approach achieved an accuracy of 100% and 91.35% for training and validation. The recommended model obtained the test classification accuracy of 94.21%. To further improve the classification performance, we suggested a patch-based DFE approach, which achieved a test accuracy of 99.73%. The obtained results, surpassing 90% accuracy on the test dataset, demonstrate the effectiveness and high classification performance of the proposed models. |
| Keywords | BrainNeXt; MRI dataset; Deep feature engineering; INCA |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 420308. Health informatics and information systems |
| Byline Affiliations | Elazig Fethi Sekin City Hospital, Turkey |
| Firat University, Turkey | |
| American University of Sharjah, United Arab Emirates | |
| Government Medical College, India | |
| School of Business | |
| Erzurum Technical University, Turkey | |
| Polytechnic University of Turin, Italy | |
| School of Mathematics, Physics and Computing |
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