Attention TurkerNeXt: Investigations into Bipolar Disorder Detection Using OCT Images

Arslan, Sermal and Kaya, Mehmet Kaan and Tasci, Burak and Kaya, Suheda and Tasci, Gulay and Ozsoy, Filiz and Dogan, Sengul and Tuncer, Turker (2023) Attention TurkerNeXt: Investigations into Bipolar Disorder Detection Using OCT Images. Diagnostics, 13 (22). p. 3422. ISSN 2075-4418

[thumbnail of diagnostics-13-03422.pdf] Text
diagnostics-13-03422.pdf - Published Version

Download (2MB)

Abstract

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.

Item Type: Article
Subjects: STM Open Academic > Medical Science
Depositing User: Unnamed user with email admin@eprint.stmopenacademic.com
Date Deposited: 11 Nov 2023 06:47
Last Modified: 11 Nov 2023 06:47
URI: http://publish.sub7journal.com/id/eprint/1616

Actions (login required)

View Item
View Item