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Deep learning and classic machine learning models in the automatic diagnosis of multiple sclerosis using retinal vessels

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Abstract

This study aims to automatically detect multiple sclerosis (MS) in terms of the changes in retinal vessels using Scanning laser ophthalmoscopy (SLO) images. Although much research has been done to diagnose MS patients, these diagnostic techniques have always been based on using Magnetic resonance imaging (MRI) images which cannot be a complete technique in diagnosing this disease. Using SLO images and examining the condition of its vessels using computer technology, biomarkers in the vessel can be identified to help diagnose MS patients. However, in the first step, the color images are converted to gray and after that are improved using a combination of algorithm Tylor Coye and DWT, then, the images are segmented and retinal vessels are extracted. Besides, two different techniques are used in classification stage. In the first technique, classic Machine learning different features are extracted from the resulting regions and entered into several multiple classifiers, the results of which give us an accuracy of 72%, moreover in the second technique segmented images enter the transfer learning model and ultimately lead us to 98% accuracy in the distinction between MS patients and Healthy Controls (HCs).

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Data availability

Collecting this dataset was approved by the ethics committee of Isfahan University of Medical Sciences and was conducted according to the Declaration of Helsinki in the applicable version.

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Acknowledgements

Words cannot express my gratitude to my professors for their invaluable patience and feedback. I also could not have undertaken this journey, who generously provided knowledge and expertise.

Lastly, I would be remiss in not mentioning my family, especially my parents, spouse, and my kid. Their belief in me has kept my spirits and motivation high during this process.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran

    Neda Yaghoubi & Hassan Masumi

  2. Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran

    Mohammad Hossein Fatehi

  3. Isfahan Neurosciences Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

    Fereshteh Ashtari

  4. School of Advanced Technologies in Medicine, Medical Image & Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

    Rahele Kafieh

  5. Department of Engineering, Durham University, South Road, Durham, UK

    Rahele Kafieh

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  1. Neda Yaghoubi
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  2. Hassan Masumi
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  3. Mohammad Hossein Fatehi
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  4. Fereshteh Ashtari
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  5. Rahele Kafieh
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Correspondence to Rahele Kafieh.

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Ethical statement

Collecting this dataset was approved by the ethics committee of Isfahan University of Medical Sciences and was conducted according to the Declaration of Helsinki in the applicable version.

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Yaghoubi, N., Masumi, H., Fatehi, M.H. et al. Deep learning and classic machine learning models in the automatic diagnosis of multiple sclerosis using retinal vessels. Multimed Tools Appl 83, 37483–37504 (2024). https://doi.org/10.1007/s11042-023-16812-w

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