Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests

Nayak, Deepak Ranjan; Dash, Ratnakar; Majhi, Banshidhar

doi:10.1016/j.neucom.2015.11.034

Cited by 224 publications

(90 citation statements)

References 19 publications

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“…Nayak, Dash and Majhi (2016) [14] used a smart computer aided diagnosis system (CADS) to identify disease brain images. This system includes three aspects, applying 2D DWT to extract features, using PPCA to reduce features and using ADBRF to identify normal and disease brain.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Methods for Intelligent Abnormal Brain Identification

Liu

Snetkov³

2017

Proceedings of the 2017 International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2017)

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Abstract-This survey paper describes a focused literature survey of machine learning methods in order to detect pathological brain. Based on the published time and emerging methods, this paper introduces in details the methods used in each documents. Because of the requirement to select a good approach in the process of pathological brain analysis, we compare the classification results of different methods and present a promising future.

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Section: Methodsmentioning

confidence: 99%

Machine Learning Methods for Intelligent Abnormal Brain Identification

Liu

Snetkov³

2017

Proceedings of the 2017 International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2017)

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“…The results thereby obtained had 100 percent accuracy as compared to other DWT based algorithm for classification. Nayak et al [13] further extracted the 2D wavelet components of an MR image and reduced them using probabilistic principal component analysis (PPCA). Finally, with the reduced feature set of thirteen, authors used AdaBoost random forest classifier and claimed 100 percent accuracy.…”

Section: Mri Related Workmentioning

confidence: 99%

“…where is the row vector of observed variable, * stands for multiplication, is the row vector of latent variables, and is the isotropic error term [13]. The -by-weight matrix relates the latent and observation variables, and the vector permits the model to have a nonzero mean.…”

Section: Feature Extraction and Dimensionality Reduction Usingmentioning

confidence: 99%

A Five-Level Wavelet Decomposition and Dimensional Reduction Approach for Feature Extraction and Classification of MR and CT Scan Images

Srivastava

Purwar

2017

Applied Computational Intelligence and Soft Computing

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This paper presents a two-dimensional wavelet based decomposition algorithm for classification of biomedical images. The twodimensional wavelet decomposition is done up to five levels for the input images. Histograms of decomposed images are then used to form the feature set. This feature set is further reduced using probabilistic principal component analysis. The reduced set of features is then fed into either nearest neighbor algorithm or feed-forward artificial neural network, to classify images. The algorithm is compared with three other techniques in terms of accuracy. The proposed algorithm has been found better up to 3.3%, 12.75%, and 13.75% on average over the first, second, and third algorithm, respectively, using KNN and up to 6.22%, 13.9%, and 14.1% on average using ANN. The dataset used for comparison consisted of CT Scan images of lungs and MR images of heart as obtained from different sources.

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“…Nayak et al (2016) [1] presented a brain image classification algorithm based on random forest. Alweshah and Abdullah (2015) [2] hybridized firefly algorithm (FA) and probabilistic neural network (PNN).…”

Section: Introductionmentioning

confidence: 99%

Identification of Alcoholism Based on Wavelet Renyi Entropy and Three-Segment Encoded Jaya Algorithm

Wang

Muhammad

et al. 2018

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The alcohol use disorder (AUD) is an important brain disease, which could cause the damage and alteration of brain structure. The current diagnosis of AUD is mainly done manually by radiologists. This study proposes a novel computer-vision-based method for automatic detection of AUD based on wavelet Renyi entropy and three-segment encoded Jaya algorithm from MRI scans. The wavelet Renyi entropy is proposed to provide multiresolution and multiscale analysis of features, describe the complexity of the brain structure, and extract the distinctive features. Grid search method was used to select the optimal wavelet decomposition level and Renyi order. The classifier was constructed based on feedforward neural network and a three-segment encoded (TSE) Jaya algorithm providing parameter-free training of the weights, biases, and number of hidden neurons. We have conducted the experimental evaluation on 235 subjects (114 are AUDs and 121 healthy). -fold cross validation has been used to avoid overfitting and report out-of-sample errors. The results showed that the proposed method outperforms four state-of-the-art approaches in terms of accuracy. The proposed TSE-Jaya provides a better performance, compared to the conventional approaches including plain Jaya, multiobjective genetic algorithm, particle swarm optimization, bee colony optimization, modified ant colony system, and real-coded biogeography-based optimization.

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Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests

Cited by 224 publications

References 19 publications

Machine Learning Methods for Intelligent Abnormal Brain Identification

Machine Learning Methods for Intelligent Abnormal Brain Identification

A Five-Level Wavelet Decomposition and Dimensional Reduction Approach for Feature Extraction and Classification of MR and CT Scan Images

Identification of Alcoholism Based on Wavelet Renyi Entropy and Three-Segment Encoded Jaya Algorithm

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