Makale detayı · 2025
BrainNeXt: novel lightweight CNN model for the automated detection of brain disorders using MRI images
- Yıl
- 2025
- Tür
- article
Veri kaynağı ayrımı
- YÖKSİS YÖKSİS makale kaydı
- YÖKSİS dergi adı Cognitive Neurodynamics
- Katalog eşleşmesi (ISSN) Cognitive Neurodynamics
- OpenAlex OpenAlex zenginleştirmesi (özet, atıf, konular)
Özet
OpenAlex · İngilizce
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.
Konular
Atıflar
OpenAlex cited_by_count. WoS veya Scopus atıf sayısı değildir; o kaynaklar için ayrı kolon yoktur.
11 atıf
OpenAlex cited_by_count (önbellek / veritabanı)
Yerel katalogda bu makaleye atıf yapan 3 yayın (OpenAlex referans eşleşmesi; tam dünya listesi değildir).
- Balancing accuracy and efficiency in fine-grained image classification: a systematic comparison of CNN, vision transformer, and hybrid architectures 2026
- Balancing accuracy and efficiency in fine-grained image classification: a systematic comparison of CNN, vision transformer, and hybrid architectures 2026
- Balancing accuracy and efficiency in fine-grained image classification: a systematic comparison of CNN, vision transformer, and hybrid architectures 2026