Pathological brain detection based on AlexNet and transfer learning

Covid-19 Classification by FGCNet with Deep Feature Fusion from Graph Convolutional Network and Convolutional Neural Network.

Emotion recognition represents the position and motion of facial muscles. It contributes significantly in many fields. Current approaches have not obtained good results. This paper aimed to propose a new emotion recognition system based on facial expression images. We enrolled 20 subjects and let each subject pose seven different emotions: happy, sadness, surprise, anger, disgust, fear, and neutral. Afterward, we employed biorthogonal wavelet entropy to extract multiscale features, and used fuzzy multiclass support vector machine to be the classifier. The stratified cross validation was employed as a strict validation model. The statistical analysis showed our method achieved an overall accuracy of 96.77±0.10%. Besides, our method is superior to three state-of-the-art methods. In all, this proposed method is efficient.

Facial Emotion Recognition Based on Biorthogonal Wavelet Entropy, Fuzzy Support Vector Machine, and Stratified Cross Validation

Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU

Intelligent facial emotion recognition based on stationary wavelet entropy and Jaya algorithm

To classify brain images into pathological or healthy is a key pre-clinical state for patients. Manual classification is tiresome, expensive, time-consuming, and irreproducible. In this study, we aimed to present an automatic computer-aided system for brain-image classification. We used 90 T2-weighted images obtained by magnetic resonance images. First, we used weighted-type fractional Fourier transform WFRFT to extract spectrums from each magnetic resonance image. Second, we used principal component analysis PCA to reduce spectrum features to only 26. Third, those reduced spectral features of different samples were combined and were fed into support vector machine SVM and its two variants: generalized eigenvalue proximal SVM and twin SVM. A 5 × 5-fold cross-validation results showed that this proposed "WFRFT+PCA+generalized eigenvalue proximal SVM" yielded sensitivity of 99.53%, specificity of 92.00%, precision of 99.53%, and accuracy of 99.11%, which are comparable with the proposed "WFRFT+PCA+twin SVM" and better than the proposed "WFRFT+PCA+SVM." Besides, all three proposed methods were superior to eight state-of-the-art algorithms. Thus, WFRFT is effective, and the proposed methods can be used in practical. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 317-327, 2015

Magnetic resonance brain image classification based on weighted-type fractional Fourier transform and nonparallel support vector machine

To develop an automatic tea-category identification system with a high recall rate, we proposed a computer-vision and machine-learning based system, which did not require expensive signal acquiring devices and time-consuming procedures. We captured 300 tea images using a 3-CCD digital camera, and then extracted 64 color histogram features and 16 wavelet packet entropy (WPE) features to obtain color information and texture information, respectively. Principal component analysis was used to reduce features, which were fed into a fuzzy support vector machine (FSVM). Winner-take-all (WTA) was introduced to help the classifier deal with this 3-class problem. The 10 × 10-fold stratified cross-validation results show that the proposed FSVM + WTA method yields an overall recall rate of 97.77%, higher than 5 existing methods. In addition, the number of reduced features is only five, less than or equal to existing methods. The proposed method is effective for tea identification.

https://www.mdpi.com/1099-4300/17/10/6663/pdf

Identification of Green, Oolong and Black Teas in China via Wavelet Packet Entropy and Fuzzy Support Vector Machine

Background: We proposed a new automatic and rapid computer-aided diagnosis system to detect pathological brain images obtained in the scans of magnetic resonance imaging (MRI). Methods: For simplification, we transformed the problem to a binary classification task (pathological or normal). It consisted of two steps: first, Hu moment invariants (HMI) were extracted from a specific MR brain image; then, seven HMI features were fed into two classifiers: twin support vector machine (TSVM) and generalised eigenvalue proximal SVM (GEPSVM). Results: Then, a 5 × 5-fold cross validation on a data set containing 90 MR brain images, demonstrated that the proposed methods “HMI + GEPSVM” and “HMI + TSVM” achieved classification accuracy of 98.89%, higher than eight state-of-the-art methods: “DWT + PCA + BP-NN”, “DWT + PCA + RBF-NN”, “DWT + PCA + PSO-KSVM”, “WE + BP-NN”, “WE + KSVM”, “DWT + PCA + GA-KSVM”, “WE + PSO-KSVM” and “WE + BBO-KSVM”. Conclusion: The proposed methods are superior to other methods on pat...

Pathological brain detection in MRI scanning via Hu moment invariants and machine learning

AIM To develop an automatic magnetic resonance (MR) brain classification that can assist physicians to make a diagnosis and reduce wrong decisions. METHOD This article investigated the binary particle swarm optimization (BPSO) approach and proposed its three new variants: BPSO with mutation and time-varying acceleration coefficients (BPSO-MT), BPSO with mutation (BPSO-M), and BPSO with time-varying acceleration coefficients (BPSO-T). We first extracted wavelet entropy (WE) features from both approximation and detail sub-bands of eight-level decomposition. Afterwards, we used the proposed BPSO-M, BPSO-T, and BPSO-MT to select features. Finally, the selected features were fed into a probabilistic neural network (PNN). RESULTS The proposed BPSO-MT performed better than BPSO-T and BPSO-M. It finally selected two features of entropies of the following two sub-bands (V1, D1). The proposed system "WE + BPSO-MT + PNN" yielded perfect classification on Data160 and Data66. In addition, it yielded 99.53% average accuracy for the Data255, over 10 repetitions of k-fold stratified cross validation (SCV), higher than state-of-the-art approaches. CONCLUSIONS The proposed method is effective for MR brain classification.

Magnetic resonance brain classification by a novel binary particle swarm optimization with mutation and time-varying acceleration coefficients.

Magnetic resonance (MR) imaging is widely used in daily medical treatment. It could help in pre-surgical, diagnosis, prognosis, and postsurgical processes. It could be beneficial for diagnosis to classify MR images of brain into healthy or abnormal automatically and accurately, since the information set MRIs generate is too large to interpret with manual methods. We propose a new approach with wavelet-entropy as the features and the kernel based extreme learning machine (K-ELM) to be the classifier. Our method employs 2D-discreet wavelet transform (DWT), and calculates the entropy as features. Then, a K-ELM is trained to classify images as pathological or healthy. A 10 × 10-fold cross validation is conducted to prevent overfitting. The method achieves the sensitivity as 97.48 %, the specificity as 94.44 %, and the overall accuracy as 97.04 % based on 125 MR images. The performance suggests the classifier is robust and effective by comparison with the recently published approaches.

Jianfei Yang

Papers

Magnetic resonance brain image classification based on weighted-type fractional Fourier transform and nonparallel support vector machine

Identification of Green, Oolong and Black Teas in China via Wavelet Packet Entropy and Fuzzy Support Vector Machine

Pathological brain detection in MRI scanning via Hu moment invariants and machine learning

Magnetic resonance brain classification by a novel binary particle swarm optimization with mutation and time-varying acceleration coefficients.

A pathological brain detection system based on kernel based ELM