Medical image analysis using wavelet transform and deep belief networks

Khatami, Amin; Khosravi, Abbas; Nguyen, Thanh; Lim, Chee Peng; Nahavandi, Saeid

doi:10.1016/j.eswa.2017.05.073

Cited by 114 publications

(52 citation statements)

References 37 publications

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“…According to non-linear techniques, several researchers developed various techniques for MDC. A review of recent techniques [13][14][15][16][17][18] discussed in this section, where the advantage and the drawback of these techniques are presented below.…”

Section: Literature Surveymentioning

confidence: 99%

An Effective Fuzzy Neural Network with Reinforcement Learning Approach for Medical Data Classification

Prasad¹,

Francis²,

Huq³

2020

IJIES

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Nowadays, several researchers are facing challenges on the prediction of diseases from the huge volume of medical databases. So, researchers are using data mining techniques like association rules, classification and clustering to address challenges. The physicians make the right decisions for successful diagnosis of various diseases by using the prediction. The existing work classifies the data to predict the certain diseases, but still it faces the difficulties due to overfitting in the training data. The main aim of this research work is to classify the Medical Data (MD) by developing the feature selection based approach in MD Classification as MDC. The irrelevant features are eliminated from the MD by using Recursive Feature Elimination (RFE) method, then ranked the features to reduce the computation cost of the proposed method. The ranked features from the RFE are given as input to the Fuzzy Neural Network (FNN) with Reinforcement Learning (RL), which is used for classification. The proposed RFE-FNN method has the accuracy of the 98.57%, 98.15% sensitivity, 98.64% specificity and 95.47% F-Measure in Heart disease dataset.

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Section: Literature Surveymentioning

confidence: 99%

An Effective Fuzzy Neural Network with Reinforcement Learning Approach for Medical Data Classification

Prasad¹,

Francis²,

Huq³

2020

IJIES

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“…These features, however are linearly related to the input image and such a simple transformation is not likely to provide relevant features in our case. In other approaches, Localy Binary Patterns (LBP) [8], completed LBP [9], gray-level co-occurence matrices [10], or Artificial Neural Networks (ANNs) [11] have been also used to obtain features, all on input images significantly different from the ones available in our case.…”

Section: Related Workmentioning

confidence: 99%

A Convolutional Neural Network for the automatic diagnosis of collagen VI-related muscular dystrophies

Bazaga

Roldán

Badosa

et al. 2019

Applied Soft Computing

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The development of machine learning systems for the diagnosis of rare diseases is challenging mainly due the lack of data to study them. Despite this challenge, this paper proposes a system for the Computer Aided Diagnosis (CAD) of low-prevalence, congenital muscular dystrophies from confocal microscopy images. The proposed CAD system relies on a Convolutional Neural Network (CNN) which performs an independent classification for non-overlapping patches tiling the input image, and generates an overall decision summarizing the individual decisions for the patches on the query image. This decision scheme points to the possibly problematic areas in the input images and provides a global quantitative evaluation of the state of the patients, which is fundamental for diagnosis and to monitor the efficiency of therapies.

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“…Deep learning, a subfield of machine learning, is used in the identification of meaningful features of the image. DBNs recently show impressive performance in various image analysis tasks [48][49][50]. In this study, DBN is used to convert the overlapped patches into feature vectors.…”

Section: A Patch To Feature Vector Conversionmentioning

confidence: 99%

“…The DBNs [31] have learning ability to extract a deep hierarchical representation of data images. Here, DBN is used as feature extractor because it can generate robust features that lead to the improvement in classification performance [48][49][50][51]. Preprocessing, i.e., cropping, normalization, smoothing, and balancing, is used to achieve the best representative features of raw images.…”

Section: A Patch To Feature Vector Conversionmentioning

confidence: 99%

Automatic Deep Feature Learning via Patch-Based Deep Belief Network for Vertebrae Segmentation in CT Images

Qadri

et al. 2018

Applied Sciences

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Precise automatic vertebra segmentation in computed tomography (CT) images is important for the quantitative analysis of vertebrae-related diseases but remains a challenging task due to high variation in spinal anatomy among patients. In this paper, we propose a deep learning approach for automatic CT vertebra segmentation named patch-based deep belief networks (PaDBNs). Our proposed PaDBN model automatically selects the features from image patches and then measures the differences between classes and investigates performance. The region of interest (ROI) is obtained from CT images. Unsupervised feature reduction contrastive divergence algorithm is applied for weight initialization, and the weights are optimized by layers in a supervised fine-tuning procedure. The discriminative learning features obtained from the steps above are used as input of a classifier to obtain the likelihood of the vertebrae. Experimental results demonstrate that the proposed PaDBN model can considerably reduce computational cost and produce an excellent performance in vertebra segmentation in terms of accuracy compared with state-of-the-art methods.

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Medical image analysis using wavelet transform and deep belief networks

Cited by 114 publications

References 37 publications

An Effective Fuzzy Neural Network with Reinforcement Learning Approach for Medical Data Classification

An Effective Fuzzy Neural Network with Reinforcement Learning Approach for Medical Data Classification

A Convolutional Neural Network for the automatic diagnosis of collagen VI-related muscular dystrophies

Automatic Deep Feature Learning via Patch-Based Deep Belief Network for Vertebrae Segmentation in CT Images

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