Detection of Multiple Sclerosis using Deep Learning

Abstract

Accurate detection of white matter lesions in 3D Magnetic Resonance Images (MRIs) of patients with Multiple Sclerosis is essential for diagnosis and treatment evaluation of MS. It is strenuous for the optimal treatment of the disease to detect early MS and estimate its progression. In this study, we propose efficient Multiple Sclerosis detection techniques to improve the performance of a supervised machine learning algorithm and classify the progression of the disease. Detection of MS lesions become more intricate due to the presence of unbalanced data with a very small number of lesions pixel. Our pipeline is evaluated on MS patients data from the Laboratory of Imaging Technologies. Fluid-attenuated inversion recovery (FLAIR) series are incorporated to introduce a faster system alongside maintaining readability and accuracy. Our approach is based on convolutional neural networks (CNN). We have trained the model using transfer learning and used softmax as an activation function to classify the progression of the disease. Our results significantly show the effectiveness of the usage of MRI of MS lesions. Experiments on 30 patients and 100 healthy brain MRIs can accurately predict disease progression. Manual detection of lesions by clinical experts is complicated and time-consuming as a large amount of MRI data is required to analyze. We analyze the accuracy of the proposed model on the dataset. Our approach exhibits a significant accuracy rate of up to 98.24%.

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

“…Similarly, in most of the previous studies that used MRI for the diagnosis of MS, the proposed models classified the patient sample with MS versus healthy controls [ 49 , 51 , 52 , 55 , 82 , 83 , 84 , 85 , 91 , 97 , 98 ], and the discrimination between these two classes is relatively simple. However, there is a need to devise a model that discriminates among MS and other diseases that are similar on MRI scan like brain tumors.…”

“…However, it should be noted that the studies in the literature that achieved 100% results such as [ 19 , 39 , 44 , 51 , 66 , 81 , 91 , 101 ] suffer from several limitations.…”

“…Age is identified as one of the significant factors for the diagnosis of MS because age brings changes in the brain [ 91 ]. The studies that merely used MRI did not consider this factor.…”

“…Al Jannat [ 91 ] developed a neural network-based system to accurately detect white matter MS lesions. The dataset contained 3766 slices of MR images from 30 patients with MS and 100 slices of healthy brain MRIs.…”

Machine learning classification of multiple sclerosis in children using optical coherence tomography

“…This sensitivity and specificity surpass VEP, where the sensitivity for predicting MS diagnosis in adults ranges between 20% and 50% in children with clinically isolated syndromes, and 25% and 50% in children with clinically definite MS. 20 , 21 Importantly, the sensitivity and specificity of OCT in diagnosing MS comes close to that of the current diagnostic gold standard, MRI. Studies looking at binary classification between MS and controls 22 , 23 or MS and NMOSD 24 using artificial intelligence have reported high sensitivity at 96% and 87%, respectively. Our results suggest that auto-segmented OCT features may be as useful and readily clinically applicable in diagnosing MS in children without any a priori criteria, although MRI may have greater sensitivity and specificity when screening for other pathologies.…”