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 17 publications

(9 citation statements)

References 14 publications

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“…An αlayer convolutional neural network is proposed as the backbone network based on the nCM concept. Its structure is listed in Table 2, where α is defined as the number of weighted layers (NWL)-either convolutional layer or fully connected layer (FCL) (21). The total layers of the backbone network are calculated as α = 9 i=1 α i = 12 (see Table 2) via trialand-error method.…”

Section: Backbone Networkmentioning

confidence: 99%

RETRACTED: PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis

Wang

Zhu

Zhang

2021

Front. Public Health

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Objective: COVID-19 is a sort of infectious disease caused by a new strain of coronavirus. This study aims to develop a more accurate COVID-19 diagnosis system.Methods: First, the n-conv module (nCM) is introduced. Then we built a 12-layer convolutional neural network (12l-CNN) as the backbone network. Afterwards, PatchShuffle was introduced to integrate with 12l-CNN as a regularization term of the loss function. Our model was named PSCNN. Moreover, multiple-way data augmentation and Grad-CAM are employed to avoid overfitting and locating lung lesions.Results: The mean and standard variation values of the seven measures of our model were 95.28 ± 1.03 (sensitivity), 95.78 ± 0.87 (specificity), 95.76 ± 0.86 (precision), 95.53 ± 0.83 (accuracy), 95.52 ± 0.83 (F1 score), 91.7 ± 1.65 (MCC), and 95.52 ± 0.83 (FMI).Conclusion: Our PSCNN is better than 10 state-of-the-art models. Further, we validate the optimal hyperparameters in our model and demonstrate the effectiveness of PatchShuffle.

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Section: Backbone Networkmentioning

confidence: 99%

RETRACTED: PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis

Wang

Zhu

Zhang

2021

Front. Public Health

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

Cited by 17 publications

References 14 publications

RETRACTED: PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis

RETRACTED: PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis

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

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

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