Pathological brain detection using curvelet features and least squares SVM

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

doi:10.1007/s11042-016-4171-y

Cited by 31 publications

(14 citation statements)

References 32 publications

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“…(Chaplot et al, 2006) preferred support vector machine (SVM) based classification to obtain high accuracy scores using 52-images. The SVM is still a popular classifier on recent MR classification studies (Gudigar et al, 2019;Nayak et al, 2018;. (Gudigar et al 2019) have analyzed brain images by using various multiresolution techniques like discrete wavelet transform (DWT), curvelet and shearlet transforms.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural networks for multi-class brain disease detection using MRI images

Talo

Yıldırım

Baloğlu

et al. 2019

Computerized Medical Imaging and Graphics

234

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The brain disorders may cause loss of some critical functions such as thinking, speech and movement. So, the early detection of brain diseases may help to get the timely best treatment. One of the common methods used to diagnose these disorders is the magnetic resonance imaging (MRI) technique. Manual diagnosis of brain abnormalities is time-consuming and difficult to perceive the minute changes in the MRI images especially in the early stages of abnormalities. Proper selection of the features and classifiers to obtain the highest performance is a challenging task. Hence, deep learning models have been widely used for medical image analysis over the past few years. In this study, we have employed the AlexNet, Vgg-16, ResNet-18, ResNet-34 and ResNet-50 pre-trained models to automatically classify MR images in to normal, cerebrovascular, neoplastic, degenerative, and inflammatory diseases classes. We have also compared their classification performance with pre-trained models which are the state-of-art architectures. We have obtained the best classification accuracy of 95.23% ± 0.6 with ResNet-50 model among the five pre-trained models. Our model is ready to be tested with huge MRI images of brain abnormalities. The outcome of model will also help the clinicians to validate their finding after manual reading of the MRI images.

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Section: Introductionmentioning

confidence: 99%

Convolutional neural networks for multi-class brain disease detection using MRI images

Talo

Yıldırım

Baloğlu

et al. 2019

Computerized Medical Imaging and Graphics

234

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show abstract

“…The setting of training and test images is shown in Table 2. The 5-fold SCV has repeated for 75 runs with the aim of improving generalization ability of the network [12,13,15] which results in reliable and robust system.…”

Section: Cross Validation Settingmentioning

confidence: 99%

“…Adaboost with random forests (ADBRF) was used for binary classification. Nayak et al [13] suggested a system using fast discrete curvelet transform (FDCT), principal component analysis (PCA) and least squares SVM (LS-SVM) with three different kernels to perform binary classification. Zhang et al [14] suggested a novel system employing fourier entropy (FRFE) as feature and multi-layer perceptron (MLP) as classifier.…”

Section: Introductionmentioning

confidence: 99%

Multi class disorder detection of magnetic resonance brain images using composite features and neural network

2019

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Brain disorder recognition has becoming a promising area of study. In reality, some disorders share similar features and signs, making the task of diagnosis and treatment challenging. This paper presents a rigorous and robust computer aided diagnosis system for the detection of multiple brain abnormalities which can assist physicians in the diagnosis and treatment of brain diseases. In this system, we used energy of wavelet sub bands, textural features of gray level co-occurrence matrix and intensity feature of MR brain images. These features are ranked using Wilcoxon test. The composite features are classified using back propagation neural network. Bayesian regulation is adopted to find the optimal weights of neural network. The experimentation is carried out on datasets DS-90 and DS-310 of Harvard Medical School. To enhance the generalization capability of the network, fivefold stratified cross validation technique is used. The proposed system yields multi class disease classification accuracy of 100% in differentiating 90 MR brain images into 18 classes and 97.81% in differentiating 310 MR brain images into 6 classes. The experimental results reveal that the composite features along with BPNN classifier create a competent and reliable system for the identification of multiple brain disorders which can be used in clinical applications. The Wilcoxon test outcome demonstrates that standard deviation feature along with energies of approximate and vertical sub bands of level 7 contribute the most in achieving enhanced multi class classification performance results.

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“…The proposed framework was compared with other similar techniques on the basis of accuracy and was claimed to be better. Also Nayak et al in [8] diagnosed pathological brain by using fifty largest coefficients from level-5 discrete curvelet transform and then reducing the feature vector using PPCA. SVM is then used to classify between healthy and pathological brain.…”

Section: Mri Related Workmentioning

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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Pathological brain detection using curvelet features and least squares SVM

Cited by 31 publications

References 32 publications

Convolutional neural networks for multi-class brain disease detection using MRI images

Convolutional neural networks for multi-class brain disease detection using MRI images

Multi class disorder detection of magnetic resonance brain images using composite features and neural network

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

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