Comparative analysis of white matter lesion segmentation in multiple sclerosis patients' MRIs: evaluating the results of FCNN architecture and CVIPtools software on compressed image data

Hanieh Ajami; Heena Chakradhar; Mahdi Kargar Nigjeh; Al Mahmud; Md Sami Ul Hoque; Scott E. Umbaugh

doi:10.1117/12.3027815

30 September 2024 Comparative analysis of white matter lesion segmentation in multiple sclerosis patients' MRIs: evaluating the results of FCNN architecture and CVIPtools software on compressed image data

Hanieh Ajami, Heena Chakradhar, Mahdi Kargar Nigjeh, Al Mahmud, Md Sami Ul Hoque, Scott E. Umbaugh

Author Affiliations +

Proceedings Volume 13137, Applications of Digital Image Processing XLVII; 1313704 (2024) https://doi.org/10.1117/12.3027815
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Abstract

This study builds upon our previous investigations into identifying and classifying white matter lesions on the brain surfaces of multiple sclerosis patients using both traditional machine learning and novel deep learning models. Our prior work demonstrated the effectiveness of these methods with high-resolution MRI modalities. In this paper, we evaluate the performance of these methodologies using a degraded dataset instead of high-resolution images. Specifically, we compared a traditional machine learning approach using K-Nearest Neighbors (KNN) with a deep learning approach employing ResNet-18 with transfer learning. The KNN-based approach achieved moderate performance metrics, with an accuracy of 71.15% for lesions and 72.22% for non-lesions. These results indicate a balanced performance; however, the algorithm's efficacy was significantly compromised by the compressed images, highlighting the limitations of traditional machine learning methods in handling lossy compression artifacts. In contrast, the ResNet-18 deep learning model demonstrated remarkable performance, achieving an overall accuracy of 94% on the compressed dataset. The model's success was further reflected in high sensitivity, specificity, precision, and F1-score metrics, all hovering around 94% to 95%. By leveraging transfer learning, the deep learning approach excelled even with compressed medical images, showcasing the potential of modern deep learning techniques in handling degraded datasets, and enhancing the disease diagnosis process.

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(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Hanieh Ajami, Heena Chakradhar, Mahdi Kargar Nigjeh, Al Mahmud, Md Sami Ul Hoque, and Scott E. Umbaugh "Comparative analysis of white matter lesion segmentation in multiple sclerosis patients' MRIs: evaluating the results of FCNN architecture and CVIPtools software on compressed image data", Proc. SPIE 13137, Applications of Digital Image Processing XLVII, 1313704 (30 September 2024); https://doi.org/10.1117/12.3027815

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