2020
DOI: 10.1049/iet-spr.2019.0543
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Review of advanced computational approaches on multiple sclerosis segmentation and classification

Abstract: In this study, a survey of multiple sclerosis (MS) classification and segmentation process is presented, which is based on magnetic resonance imaging. Knowledge of MS lesions is gained by determining the number of sample lesions in order that the lesion development level can be followed precisely; therefore, the effects of pharmaceuticals in medical tests can be accurately assessed. Accurate recognition of MS lesions in magnetic resonance images is an additionally complex process because of their changing shap… Show more

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Cited by 43 publications
(12 citation statements)
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“…Therefore, prior studies have attempted to build automated segmentation methods for cortical tubers, mostly adapted from focal cortical dysplasia (FCD) detection models. Conventional methods for detecting FCD or TSC tubers include a morphometric MRI analysis or surface‐based morphometry algorithms, which are based on rule‐based feature extractions 30,31 …”
Section: Discussionmentioning
confidence: 99%
“…Therefore, prior studies have attempted to build automated segmentation methods for cortical tubers, mostly adapted from focal cortical dysplasia (FCD) detection models. Conventional methods for detecting FCD or TSC tubers include a morphometric MRI analysis or surface‐based morphometry algorithms, which are based on rule‐based feature extractions 30,31 …”
Section: Discussionmentioning
confidence: 99%
“…This microcontroller has the ability to perform activities related to WIFI and is therefore widely used as a WIFI unit [20]. There are many types of ESP8266 units available ranging from ESP8266-01 to ESP8266-12 [26][27][28][29][30]. What we use in the tutorial is ESP8266-01 because it is cheaper and readily available.…”
Section: Bl O C K D Ia G R a Mmentioning
confidence: 99%
“…A comprehensive review is very important to help future generations design better automatic segmentation models based on the predecessors. In the past few years, there have also been related reviews (Danelakis et al, 2018;Kaur et al, 2020;Shanmuganathan et al, 2020) published. Danelakis et al (2018) reviews the methods of automatically segmenting MS lesions and pointed out that MRI data acquisition and the injection of the contrast medium during data acquisition are great challenges in the future.…”
Section: Introductionmentioning
confidence: 99%
“…Kaur et al (2020) reviews the state-of-the-art methods by 2019 and lists the future directions obtained from these methods for future reference. Shanmuganathan et al (2020) reviews the classification and segmentation methods of MS lesions and compares the classification and segmentation methods separately. Their comparison of various strategies shows that the segmentation methods based on deep learning achieve better performance.…”
Section: Introductionmentioning
confidence: 99%
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