Multiple sclerosis identification in brain<scp>MRI</scp>images using wavelet convolutional neural networks

Alijamaat, Ali; Nikravanshalmani, Alireza; Bayat, Peyman

doi:10.1002/ima.22492

Cited by 22 publications

(22 citation statements)

References 30 publications

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“…Assume the coordinates of the raw image were recorded as , and the transformed coordinates were written as . Then the transformation process could be described as: (9) in which was called affine transformation matrix and was also frequently written as . Every affine transformation can be represented through particular affine transformation matrix.…”

Section: Geometric-based Methodsmentioning

confidence: 99%

“…Eitel, et al [8] proposed their CNNbased method for MS detection with layer-wise relevance propagation. Alijamaat, et al [9] put forward wavelet CNN for MS detection in brain MRI images. Han, et al [10] used adaptive genetic algorithm (AGA) for MS recognition.…”

Section: Introductionmentioning

confidence: 99%

“…They need to coin specific features manually and it is usually boring and time-consuming. The second category of methods [7][8][9] is based on deep learning. It is common that they adopted deep neural networks which may contain over fifty layers or even two hundred layers to conduct the classification.…”

Section: Introductionmentioning

confidence: 99%

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Multiple Sclerosis Detection via 6-layer Stochastic Pooling Convolutional Neural Network and Multiple-way Data Augmentation

Wang

Lima

2021

STEMedicine

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Multiple sclerosis is one of most widespread autoimmune neuroinflammatory diseases which mainly damages body function such as movement, sensation, and vision. Despite of conventional clinical presentation, brain magnetic resonance imaging of white matter lesions is often applied to diagnose multiple sclerosis at the early stage. In this article, we proposed a 6-layer stochastic pooling convolutional neural network with multiple-way data augmentation for multiple sclerosis detection in brain MRI images. Our approach does not demand hand-crafted features unlike those traditional machine learning methods. Via application of stochastic pooling and multiple-way data augmentation, our 6-layer CNN achieved equivalent performance against those deep learning methods which consist of so many layers and parameters that ordinarily bring difficulty to training. The results showed that this 6-layer CNN obtained a sensitivity of 95.98±0.46%, a specificity of 95.67±0.92%, and an accuracy of 95.82±0.58%. According to comparison experiments, our results are better than state-of-the-art approaches. Further, we also conducted ablation experiments to examine the contribution of stochastic pooling and multiple-way data augmentation to the original CNN model. The contrast experiments revealed that our scheme of stochastic pooling and multiple-way data augmentation enhanced the original 6-layer CNN model compared to those using maximum pooling or average pooling and inadequate data augmentation.

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Section: Geometric-based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiple Sclerosis Detection via 6-layer Stochastic Pooling Convolutional Neural Network and Multiple-way Data Augmentation

Wang

Lima

2021

STEMedicine

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“…The parameter values and optimization methods used in the CNN architecture negatively affected the classification results. Alijamaat et al [22] proposed a method that incorporated a two-dimensional discrete Haar wavelet transform and CNN to study the MRIs of 38 patients and 20 healthy individuals and attained sensitivity, specificity, precision, and accuracy of 99.14%, 98.89%, 99.43%, and 99.05%, respectively, in their experiments. Oliveira et al [23] proposed a method for measuring plaque volume using MRIs from four different datasets.…”

Section: Introductionmentioning

confidence: 99%

An Accurate Multiple Sclerosis Detection Model Based on Exemplar Multiple Parameters Local Phase Quantization: ExMPLPQ

Macin

Taşçı

et al. 2022

Applied Sciences

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Multiple sclerosis (MS) is a chronic demyelinating condition characterized by plaques in the white matter of the central nervous system that can be detected using magnetic resonance imaging (MRI). Many deep learning models for automated MS detection based on MRI have been presented in the literature. We developed a computationally lightweight machine learning model for MS diagnosis using a novel handcrafted feature engineering approach. The study dataset comprised axial and sagittal brain MRI images that were prospectively acquired from 72 MS and 59 healthy subjects who attended the Ozal University Medical Faculty in 2021. The dataset was divided into three study subsets: axial images only (n = 1652), sagittal images only (n = 1775), and combined axial and sagittal images (n = 3427) of both MS and healthy classes. All images were resized to 224 × 224. Subsequently, the features were generated with a fixed-size patch-based (exemplar) feature extraction model based on local phase quantization (LPQ) with three-parameter settings. The resulting exemplar multiple parameters LPQ (ExMPLPQ) features were concatenated to form a large final feature vector. The top discriminative features were selected using iterative neighborhood component analysis (INCA). Finally, a k-nearest neighbor (kNN) algorithm, Fine kNN, was deployed to perform binary classification of the brain images into MS vs. healthy classes. The ExMPLPQ-based model attained 98.37%, 97.75%, and 98.22% binary classification accuracy rates for axial, sagittal, and hybrid datasets, respectively, using Fine kNN with 10-fold cross-validation. Furthermore, our model outperformed 19 established pre-trained deep learning models that were trained and tested with the same data. Unlike deep models, the ExMPLPQ-based model is computationally lightweight yet highly accurate. It has the potential to be implemented as an automated diagnostic tool to screen brain MRIs for white matter lesions in suspected MS patients.

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“…Numerous studies have been conducted on this subject recently due to the importance of early diagnosis of MS. MRI is a noninvasive method for diagnosing and detecting mild cognitive disorders, Alzheimer's, Parkinson's, and other neurological diseases [ 8 ]. Lesions caused by diseases such as MS, Alzheimer's, and other prevalent brain diseases cannot always be distinguished from one another [ 9 , 10 ]. Researchers are attempting to use MRI images to detect and diagnose neurological disorders with the least amount of error.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for MS Diagnosis Through Nonlinear EEG Descriptors and Metaheuristic Optimized Classification Learning

Mohseni

Moghaddasi

2022

Computational Intelligence and Neuroscience

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Multiple sclerosis (MS), a disease of the central nervous system, affects the white matter of the brain. Neurologists interpret magnetic resonance images that are often complicated, time-consuming, and contradictory. Using EEG signals, this disease can be analyzed and diagnosed more accurately. However, it is imperative that MS be diagnosed by specialists using assistive technology. Until now, a few methods have been proposed in this field that are sometimes associated with different challenges in analysis. This paper presents a hybrid approach to MS diagnosis in order to decrease classification error rates. Using the proposed method, EEG descriptors in both the time and frequency domains are analyzed. After the feature extraction stage, a modified version of the ant colony optimization method (m-ACO) was used to select the appropriate subset of features. Then, the support vector machine is used to determine whether the disease exists. A metaheuristic algorithm adjusts the support vector machine’s parameters to withstand overfitting challenges. Despite a limited number of input channels, significant classification accuracy has been achieved using wavelet analysis techniques, dividing all five subbands of EEG signals, signal windowing, and extracting efficient features from the data. Additionally, alpha, beta, and gamma bands of the signal are important, and the accuracy, sensitivity, and specificity levels are higher than 98.5%. Compared to similar MS diagnostic methods, the proposed method achieved significantly higher diagnostic accuracy. Application and implementation of this method can be effective in treating neurological diseases, including multiple sclerosis.

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Multiple sclerosis identification in brainMRIimages using wavelet convolutional neural networks

Cited by 22 publications

References 30 publications

Multiple Sclerosis Detection via 6-layer Stochastic Pooling Convolutional Neural Network and Multiple-way Data Augmentation

Multiple Sclerosis Detection via 6-layer Stochastic Pooling Convolutional Neural Network and Multiple-way Data Augmentation

An Accurate Multiple Sclerosis Detection Model Based on Exemplar Multiple Parameters Local Phase Quantization: ExMPLPQ

A Hybrid Approach for MS Diagnosis Through Nonlinear EEG Descriptors and Metaheuristic Optimized Classification Learning

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