Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection

Krishnamoorthy, Sujatha; Zhang, Yaxi; Kadry, Seifedine; Khan, Muhammad Attique; Alhaisoni, Majed; Nasser, Mustafa S.; Yu, Weifeng; Alqahtani, Abdullah

doi:10.1155/2023/4776770

Cited by 3 publications

(1 citation statement)

References 60 publications

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“…In their proposed method using the VGG16 network, they obtained a specificity of 72.00% and sensitivity of 70.00%. Krishnamoorthy et al [23] detected MS lesions with the CNN network. The dataset included 1000 healthy controls and 1000 MS images.…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

confidence: 99%

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

Ekmekyapar,

Taşcı

2023

Diagnostics

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

Tatli,

Macin,

Tasci

et al. 2024

Expert Systems with Applications

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Synaptic Insights: A Holistic Approach to Brain Pathology Classification through Transfer Learning and Ensemble Techniques

Palaniappan,

Siva

2023

Preprint

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Background: Detecting brain diseases like tumors, multiple sclerosis, and strokes at an early stage is challenging due to limited access imaging technologies. Analyzing Magnetic Resonance Imaging (MRI) scans can help spot the disease's progression, which will be especially beneficial for clinicians to plan précised treatment. Methods: This research study proposes a novel framework for classifying brain pathologies from MRI modalities to improve clinicians' decision-making ability. This research study developed three distinct deep learning models: a scratch CNN model, a ResNet 101 model improved with transfer learning(m-ResNet101), and an Inception V3 model enhanced through transfer learning (m-InceptionV3). To further improve the efficacy in pathology classification, Weighted Snapshot Fusion Ensemble (WSFE) algorithm is employed to optimize the performance of the proposed model. The internal dynamics of the proposed model is visualized through GradCAM visualization. Results: m-ResNet101 model built on applying a transfer learning approach outperformed every other model, achieving an accuracy - 98.72%, F1 score - 99.35%, precision - 99.17%, and recall - 99.21%. Snapshot ensembled model on combining snapshots of m-ResNet-101 model achieves an impressive accuracy of 99.23%, F1 score of 99.46%, precision of 99.34%, and recall of 98.63%. Conclusion: The research findings suggest that combining transfer learning and snapshot ensembling will improve the model's performance in classifying brain pathology. Furthermore, the feature maps generated through the GradCAM experiment visually highlight the areas and features within an image that greatly influence the model to make a final classification. Such visuals make the models more transparent and trustworthy, which is critical for deploying AI-based models in healthcare networks.

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Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection

Cited by 3 publications

References 60 publications

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

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

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

Synaptic Insights: A Holistic Approach to Brain Pathology Classification through Transfer Learning and Ensemble Techniques

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