Relevant 3D local binary pattern based features from fused feature descriptor for differential diagnosis of Parkinson’s disease using structural MRI

Rana, Bharti; Juneja, Akanksha; Saxena, Mohit; Gudwani, Sunita; Kumaran, Senthil; Behari, Madhuri; Agrawal, R. K.

doi:10.1016/j.bspc.2017.01.007

Cited by 20 publications

(9 citation statements)

References 23 publications

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“…To learn the kernels at the second stage, the input maps at this stage need to be produced firstly by convoluting the input images with all the learned kernels at the first stage according to (7), and combining the generated features maps based on (8). Then, the input maps are split and clustered according to (9). The subsequent procedure of the kernel learning is similar to that at the first stage.…”

Section: The Training Phase Of the Hybrid Neural Networkmentioning

confidence: 99%

“…Some variants of the LBP were applied to retinal disease screening [7] and mammographic image classification [8]. The LBP combined with minimum redundancy maximum relevance feature selection was employed to recognize Parkinson's disease [9] and classify tumors from mammograms [10]. The SIFT features were used to realize the classification of neuroblastoma histological images [11].…”

Section: Introductionmentioning

confidence: 99%

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Medical Image Classification Using a Light-Weighted Hybrid Neural Network Based on PCANet and DenseNet

et al. 2020

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Medical image classification plays an important role in disease diagnosis since it can provide important reference information for doctors. The supervised convolutional neural networks (CNNs) such as DenseNet provide the versatile and effective method for medical image classification tasks, but they require large amounts of data with labels and involve complex and time-consuming training process. The unsupervised CNNs such as principal component analysis network (PCANet) need no labels for training but cannot provide desirable classification accuracy. To realize the accurate medical image classification in the case of a small training dataset, we have proposed a light-weighted hybrid neural network which consists of a modified PCANet cascaded with a simplified DenseNet. The modified PCANet has two stages, in which the network produces the effective feature maps at each stage by convoluting inputs with various learned kernels. The following simplified DenseNet with a small number of weights will take all feature maps produced by the PCANet as inputs and employ the dense shortcut connections to realize accurate medical image classification. To appreciate the performance of the proposed method, some experiments have been done on mammography and osteosarcoma histology images. Experimental results show that the proposed hybrid neural network is easy to train and it outperforms such popular CNN models as PCANet, ResNet and DenseNet in terms of classification accuracy, sensitivity and specificity. INDEX TERMS Medical image classification, hybrid neural network, PCANet, DenseNet.

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Section: The Training Phase Of the Hybrid Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Medical Image Classification Using a Light-Weighted Hybrid Neural Network Based on PCANet and DenseNet

et al. 2020

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“…The mRMR measure is designed to analyze the quality and provide the best predictive performance of a subset of variables in view of the output variable (class attribute). According to Rana et al [31], mRMR maximizes the relevance of the selected features with class labels while concurrently minimizing the redundancy among the selected features after considering mutual information (MI). This measure is frequently used for biomedical data [15].…”

Section: ) Mrmrmentioning

confidence: 99%

Dimensionality Reduction in Gene Expression Data Sets

Souza

Francisco

Macedo³

2019

IEEE Access

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Dimensionality reduction is used in microarray data analysis to enhance prediction quality, reduce computing time, and construct more robust models. In addition, the algorithm learning performance involves an expressive number of attributes (genes) relative to the classes (samples). Therefore, in this paper, we conducted a detailed comparison of two reduction methods, attribute selection and principal component analysis, to analyze gene expression data sets. Both reduction methods were employed in the pre-processing stage and then evaluated experimentally. Furthermore, we introduced a combination of consistency-based subset evaluation (CSE) and minimum redundancy maximum relevance (mRMR), which we referred to as CSE-mRMR, to improve classification efficiency. The results indicated a significant increase in classifier hit rates with both methods, compared to using all attributes. By employing cross-validation, attribute selection outperformed PCA consistently across classifiers and datasets, and CSE-mRMR demonstrated good classification performance in the data sets. Taken together, the literature and current results suggest that the attribute selection may be relevant in the analysis and future prediction of gene expression data sets.

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“…Even though there are some medical methods of diagnosing and determining the progress of PD, the results of these experiments are subjective and depend on the clinicians' expertise. On the other hand, clinicians are expensive and the process is time consuming for patients [4]. Neuroimaging techniques have significantly improved the diagnosis of neurodegenerative diseases.…”

Section: Introductionmentioning

confidence: 99%

“…A decision model was built using SVM as a classifier with a leave-one-out cross-validation scheme, giving 86.67% accuracy. The proposed method in [4] was not focused on just individual tissues (GM,WM and CSF); rather, it considered the relationship between these areas because the morphometric change in one tissue might affect other tissues. 3D LBP was used as a feature extraction tool that could produce structural and statistical information.…”

Section: Introductionmentioning

confidence: 99%

Towards the Identification of Parkinson’s Disease Using only T1 MR Images

Soltaninejad

Cheng

Basu

2018

Lecture Notes in Computer Science

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Parkinson's Disease (PD) is one of the most common types of neurological diseases caused by progressive degeneration of dopaminergic neurons in the brain. Even though there is no fixed cure for this neurodegenerative disease, earlier diagnosis followed by earlier treatment can help patients have a better quality of life. Magnetic Resonance Imaging (MRI) has been one of the most popular diagnostic tool in recent years because it avoids harmful radiations. In this paper, we investigate the plausibility of using MRIs for automatically diagnosing PD. Our proposed method has three main steps : 1) Preprocessing, 2) Feature Extraction, and 3) Classification. The FreeSurfer library is used for the first and the second steps. For classification, three main types of classifiers, including Logistic Regression (LR), Random Forest (RF) and Support Vector Machine (SVM), are applied and their classification ability is compared. The Parkinsons Progression Markers Initiative (PPMI) data set is used to evaluate the proposed method. The proposed system prove to be promising in assisting the diagnosis of PD.

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Relevant 3D local binary pattern based features from fused feature descriptor for differential diagnosis of Parkinson’s disease using structural MRI

Cited by 20 publications

References 23 publications

Medical Image Classification Using a Light-Weighted Hybrid Neural Network Based on PCANet and DenseNet

Medical Image Classification Using a Light-Weighted Hybrid Neural Network Based on PCANet and DenseNet

Dimensionality Reduction in Gene Expression Data Sets

Towards the Identification of Parkinson’s Disease Using only T1 MR Images

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