Attention TurkerNeXt: Investigations into Bipolar Disorder Detection Using OCT Images
Yazarlar (8)
Universal Eye Center, Türkiye
Universal Eye Center, Türkiye
Firat Üniversitesi, Türkiye
Sağlık Bilimleri Üniversitesi, Türkiye
Elazığ Fethi Sekin City Hospital, Türkiye
Doç. Dr. Filiz ÖZSOY
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Firat Üniversitesi, Türkiye
Firat Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
|
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Diagnostics |
| Dergi ISSN | 2075-4418 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q1 |
| Makale Dili | Türkçe |
| Basım Tarihi | 11-2023 |
| Cilt No | 13 |
| Sayı | 22 |
| DOI Numarası | 10.3390/diagnostics13223422 |
| Makale Linki | http://dx.doi.org/10.3390/diagnostics13223422 |
| Özet |
| Background and Aim: In the era of deep learning, numerous models have emerged in the literature and various application domains. Transformer architectures, particularly, have gained popularity in deep learning, with diverse transformer-based computer vision algorithms. Attention convolutional neural networks (CNNs) have been introduced to enhance image classification capabilities. In this context, we propose a novel attention convolutional model with the primary objective of detecting bipolar disorder using optical coherence tomography (OCT) images. Materials and Methods: To facilitate our study, we curated a unique OCT image dataset, initially comprising two distinct cases. For the development of an automated OCT image detection system, we introduce a new attention convolutional neural network named “TurkerNeXt”. This proposed Attention TurkerNeXt encompasses four key modules: (i) the patchify stem block, (ii) the Attention TurkerNeXt block, (iii) the patchify downsampling block, and (iv) the output block. In line with the swin transformer, we employed a patchify operation in this study. The design of the attention block, Attention TurkerNeXt, draws inspiration from ConvNeXt, with an added shortcut operation to mitigate the vanishing gradient problem. The overall architecture is influenced by ResNet18. Results: The dataset comprises two distinctive cases: (i) top to bottom and (ii) left to right. Each case contains 987 training and 328 test images. Our newly proposed Attention TurkerNeXt achieved 100% test and validation accuracies for both cases. Conclusions: We curated a novel OCT dataset and introduced a new CNN, named TurkerNeXt in this research. Based on the research findings and classification results, our proposed TurkerNeXt model demonstrated excellent classification performance. This investigation distinctly underscores the potential of OCT images as a biomarker for bipolar disorder. |
| Anahtar Kelimeler |
| Attention TurkerNeXt | biomarker discovering | bipolar disorder | OCT image classification |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| SCOPUS | 17 |
| Google Scholar | 17 |