Improved algorithm for multiple sclerosis diagnosis in MRI using convolutional neural network

Soltani, Azam; Nasri, Saeed

doi:10.1049/iet-ipr.2019.0366

Cited by 13 publications

(8 citation statements)

References 14 publications

(26 reference statements)

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“…The up sampling path consists of DB layer and transition up (TU) layer. DB layer is composed of batch normalization (BN) 23 , ReLU 24 , 3 × 3 convolution, and dropout with probability p = 0.2. TD layer is composed of BN, ReLU, 1 × 1 convolution, dropout with probability p = 0.2 and 2 × 2 maximum pooling.…”

Section: Methodsmentioning

confidence: 99%

A new detection model of microaneurysms based on improved FC-DenseNet

Wang

Yao

et al. 2022

Sci Rep

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Diabetic retinopathy (DR) is a frequent vascular complication of diabetes mellitus and remains a leading cause of vision loss worldwide. Microaneurysm (MA) is usually the first symptom of DR that leads to blood leakage in the retina. Periodic detection of MAs will facilitate early detection of DR and reduction of vision injury. In this study, we proposed a novel model for the detection of MAs in fluorescein fundus angiography (FFA) images based on the improved FC-DenseNet, MAs-FC-DenseNet. FFA images were pre-processed by the Histogram Stretching and Gaussian Filtering algorithm to improve the quality of FFA images. Then, MA regions were detected by the improved FC-DenseNet. MAs-FC-DenseNet was compared against other FC-DenseNet models (FC-DenseNet56 and FC-DenseNet67) or the end-to-end models (DeeplabV3+ and PSPNet) to evaluate the detection performance of MAs. The result suggested that MAs-FC-DenseNet had higher values of evaluation metrics than other models, including pixel accuracy (PA), mean pixel accuracy (MPA), precision (Pre), recall (Re), F1-score (F1), and mean intersection over union (MIoU). Moreover, MA detection performance for MAs-FC-DenseNet was very close to the ground truth. Taken together, MAs-FC-DenseNet is a reliable model for rapid and accurate detection of MAs, which would be used for mass screening of DR patients.

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Section: Methodsmentioning

confidence: 99%

A new detection model of microaneurysms based on improved FC-DenseNet

Wang

Yao

et al. 2022

Sci Rep

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“…Recently, deep learning has achieved more attention in many image processing fields [41,42]. The advantage of deep learning benefits from the powerful representation ability of convolutional neural networks (CNN).…”

Section: The Learning-based Biqa Methodsmentioning

confidence: 99%

CPDINet: Blind image quality assessment via a content perception and distortion inference network

Shao

Liu

Zhang

2022

IET Image Processing

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Nowadays, it is still challenging for the blind image quality assessment (BIQA) to accurately predict quality scores of distorted images, since distorted images have rich content information and complex distortions. To solve these problems, a content perception and distortion inference network for BIQA is proposed, which divides IQA task into content perception and distortion inference processes. Since humans try to understand image content before perceiving quality scores, a content feature extractor is designed to explore content information in an image to deal with the content variation problem. To handle the distortion diversity problem, a distortion feature extractor is proposed to capture distortion features in images. Because extracted content features and distortion ones have different characteristics, attention-based fusion blocks to fuse multi-scale content features and distortion ones as guidance to selectively enhance important features based on calculated weight scores are proposed. With fused features, a quality prediction module is designed to regress multi-scale features to quality scores. Experiments are performed on six public IQA datasets, including LIVE, CSIQ, TID2013, LIVEC, KonIQ-10k, and SPAQ. Experimental results show that the proposed method can effectively predict quality scores for both synthetically and authentically distorted images than its peers, including the state-of-the-art methods.

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“…In the same manner, Soltani et al [ 80 ] proposed methods for improving the CNN classifier for MS disease detection using MRI. The proposed model consisted of seven layers and was employed for feature extraction and classification.…”

Section: Related Studiesmentioning

confidence: 99%

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

Aslam

Khan

Bashamakh

et al. 2022

Sensors

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Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS. Globally, a new case of MS is reported every five minutes. In this review, we discuss the proposed approaches to diagnosing MS using machine learning (ML) published between 2011 and 2022. Numerous models have been developed using different types of data, including magnetic resonance imaging (MRI) and clinical data. We identified the methods that achieved the best results in diagnosing MS. The most implemented approaches are SVM, RF, and CNN. Moreover, we discussed the challenges and opportunities in MS diagnosis to improve AI systems to enable researchers and practitioners to enhance their approaches and improve the automated diagnosis of MS. The challenges faced by automated MS diagnosis include difficulty distinguishing the disease from other diseases showing similar symptoms, protecting the confidentiality of the patients’ data, achieving reliable ML models that are also easily understood by non-experts, and the difficulty of collecting a large reliable dataset. Moreover, we discussed several opportunities in the field such as the implementation of secure platforms, employing better AI solutions, developing better disease prognosis systems, combining more than one data type for better MS prediction and using OCT data for diagnosis, utilizing larger, multi-center datasets to improve the reliability of the developed models, and commercialization.

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Improved algorithm for multiple sclerosis diagnosis in MRI using convolutional neural network

Cited by 13 publications

References 14 publications

A new detection model of microaneurysms based on improved FC-DenseNet

A new detection model of microaneurysms based on improved FC-DenseNet

CPDINet: Blind image quality assessment via a content perception and distortion inference network

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

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