Transfer-transfer model with MSNet: An automated accurate multiple sclerosis and myelitis detection system

Tatli, Sinan; Macin, Gulay; Tasci, Irem; Tasci, Burak; Barua, Prabal Datta; Baygin, Mehmet; Tuncer, Turker; Dogan, Sengul; Ciaccio, Edward J.; Acharya, U. Rajendra

doi:10.1016/j.eswa.2023.121314

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…The diagnosis of bipolar disorder is often complex and time-consuming using traditional clinical methods. AI techniques are used in the literature to detect psychiatric diseases [18][19][20][21][22][23][24]. However, in recent years, the combination of optical coherence tomography (OCT) imaging technology and artificial intelligence (AI) techniques has enabled a faster and more accurate diagnosis of this psychiatric disorder.…”

Section: Introductionmentioning

confidence: 99%

Attention TurkerNeXt: Investigations into Bipolar Disorder Detection Using OCT Images

Arslan,

Kaya,

Tasci

et al. 2023

Diagnostics

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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.

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Section: Introductionmentioning

confidence: 99%

Attention TurkerNeXt: Investigations into Bipolar Disorder Detection Using OCT Images

Arslan,

Kaya,

Tasci

et al. 2023

Diagnostics

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“…The results indicate high accuracy, sensitivity, and specificity values for Axial, Sagittal, and Hybrid imaging approaches. Tatli et al[33] used a model named MSNet. It was tested on 706 MRI data and achieved high accuracy, sensitivity, and F1-scores based on 10-fold cross-validation results.Wang et al (2021)…”

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confidence: 99%

Exemplar MobileNetV2-Based Artificial Intelligence for Robust and Accurate Diagnosis of Multiple Sclerosis

Ekmekyapar,

Taşcı

2023

Diagnostics

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Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system that prominently affects young adults due to its debilitating nature. The pathogenesis of the disease is focused on the inflammation and neurodegeneration processes. Inflammation is associated with relapses, while neurodegeneration emerges in the progressive stages of the disease. Magnetic resonance imaging (MRI) is commonly used for the diagnosis of MS, and guidelines such as the McDonald criteria are available. MRI is an essential tool to demonstrate the spatial distribution and changes over time in the disease. This study discusses the use of image processing techniques for the diagnosis of MS and specifically combines the MobileNetV2 network with exemplar-based learning, IMrMr feature selection, and K-Nearest Neighbors (KNN) classification methods. Experiments conducted on two different datasets (Dataset 1 and Dataset 2) demonstrate that these methods provide high accuracy in diagnosing MS. Dataset 1 comprises 128 patients with 706 MRI images, 131 MS patients with 667 MRI images, and 150 healthy control subjects with 1373 MRI images. Dataset 2 includes an MS group with 650 MRI images and a healthy control group with 676 MRI images. The results of the study include 10-fold cross-validation results performed on different image sections (Axial, Sagittal, and Hybrid) for Dataset 1. Accuracy rates of 99.76% for Axial, 99.48% for Sagittal, and 98.02% for Hybrid sections were achieved. Furthermore, 100% accuracy was achieved on Dataset 2. In conclusion, this study demonstrates the effective use of powerful image processing methods such as the MobileNetV2 network and exemplar-based learning for the diagnosis of MS. These findings suggest that these methods can be further developed in future research and offer significant potential for clinical applications in the diagnosis and monitoring of MS.

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A simplified method for relapsing-remitting multiple sclerosis detection: Insights from resting EEG signals

Şaşmaz Karacan,

Saraoğlu

2024

Computers in Biology and Medicine

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Transfer-transfer model with MSNet: An automated accurate multiple sclerosis and myelitis detection system

Cited by 5 publications

References 29 publications

Attention TurkerNeXt: Investigations into Bipolar Disorder Detection Using OCT Images

Attention TurkerNeXt: Investigations into Bipolar Disorder Detection Using OCT Images

Exemplar MobileNetV2-Based Artificial Intelligence for Robust and Accurate Diagnosis of Multiple Sclerosis

A simplified method for relapsing-remitting multiple sclerosis detection: Insights from resting EEG signals

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