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A machine learning approach for multiple sclerosis diagnosis through Detecron Architecture

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Abstract

Multiple sclerosis is a prevalent inflammatory disease affecting the central nervous system, leading to demyelination. Neuroradiology relies on accurate analysis of white matter lesions for diagnosis and prognosis. Automated methods for segmenting lesions in MRI scans offer crucial benefits of objectivity and efficiency, making them particularly valuable for analyzing large datasets. In contrast, manual delineation of MRI lesions is both time-consuming and prone to subjective bias. To overcome these issues, this paper proposes and develops an automated diagnosis approach using the Detecron-2 architecture. The method utilizes a fully modified Convolutional Neural Network on 3D FLAIR-weighted Magnetic Resonance Images.The approach is trained and validated on a dataset of 3000 images acquired from a Siemens 3Tesla MRI machine at the National Institute of Neurology Mongi Ben Hmida in Tunisia, using technical metrics. Comparisons with recent achievements demonstrate promising results. By addressing challenges in data augmentation and deep learning configurations, the proposed model effectively mitigates issues as overfitting. Notably, it achieves an impressive average detection accuracy of 87%, specificity (= 80,19%), precision (= 80%), sensitivity (= 76,1%) and intersection over Union (= 87,9%) when assessing healthy and pathological images. Additionally, the study recognizes the manual monitoring of multiple sclerosis plaques as a time-consuming and challenging task for clinicians. It highlights the importance of lesion segmentation for quantitative analysis of disease progression. As a second focus, the research aims to develop an automated segmentation to enhance the accuracy and efficiency of lesion identification, addressing the inconsistencies and variations observed among different observers.

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Data availability

The datasets generated during and/or analyzed during the current study are not publicly available due to the exclusive nature of the national institute of neurology Mongi ben Hmida’s proprietary database. WHY DATA ARE NOT PUBLIC but are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Higher Institute of Medical Technologies of Tunis, Research Laboratory of Biophysics and Medical Technologies LR13ES07, University of Tunis El Manar, Tunis, Tunisia

    Chaima Dachraoui & Salam Labidi

  2. National Engineering School of Tunis, Laboratory of Signal Image and Energy Mastery LR13ES03, University of Tunis, Tunis, Tunisia

    Aymen Mouelhi

  3. Computer Science Department, National School of Veterinary Medicine Sidi Thabet, Tunisia Military Academy FondekJdid, Tunis, Tunisia

    Amine Mosbeh & Wassim Sliti

  4. Faculty of Medicine of Tunisia, National Institute of Neurology Mongi Ben Hmida, University of Tunis El Manar, Tunis, Tunisia

    Cyrine Drissi

  5. IMT Atlantique, Bretagne, Pays de la Loire, LaTIM UMR 1101, UBL, Brest, France

    Basel Solaiman

Authors
  1. Chaima Dachraoui
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Contributions

All authors contributed to the study correction. Material preparation, data collection, analysis and paper preparation were performed by [Chaima Dachraoui]. The first draft of the manuscript was written by [ChaimaDachraoui] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Chaima Dachraoui.

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Ethical approval

No consent was recommended for this medical research. Personal patient data was not included in the paper. These are anonymous images and well processed protecting the privacy of patients. It is a thesis work in collaboration with the national institute of neurology. We have no direct contact with the patients. The work took place purely in the doctors’ reading room on anonymous acquisitions.

Conflict of interest

The author(s) received no financial support for the research, authorship, and/or publication of this article. The authors declare that they have no known conflict of interest that could have appeared to influence the work reported in this paper.

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Dachraoui, C., Mouelhi, A., Mosbeh, A. et al. A machine learning approach for multiple sclerosis diagnosis through Detecron Architecture. Multimed Tools Appl 83, 42837–42859 (2024). https://doi.org/10.1007/s11042-023-17055-5

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