Binary-class Classification Research Articles

An Automated Lightweight Deep Neural Network for Diagnosis of COVID-19 from Chest X-ray Images.

Coronavirus (COVID-19) is an epidemic that is rapidly spreading and causing a severe healthcare crisis resulting in more than 40 million confirmed cases across the globe. There are many intensive studies on AI-based technique, which is time consuming and troublesome by considering heavyweight models in terms of more training parameters and memory cost, which leads to higher time complexity. To improve diagnosis, this paper is aimed to design and establish a unique lightweight deep learning-based approach to perform multi-class classification (normal, COVID-19, and pneumonia) and binary class classification (normal and COVID-19) on X-ray radiographs of chest. This proposed CNN scheme includes the combination of three CBR blocks (convolutional batch normalization ReLu) with learnable parameters and one global average pooling (GP) layer and fully connected layer. The overall accuracy of the proposed model achieved 98.33% and finally compared with the pre-trained transfer learning model (DenseNet-121, ResNet-101, VGG-19, and XceptionNet) and recent state-of-the-art model. For validation of the proposed model, several parameters are considered such as learning rate, batch size, number of epochs, and different optimizers. Apart from this, several other performance measures like tenfold cross-validation, confusion matrix, evaluation metrics, sarea under the receiver operating characteristics, kappa score and Mathew’s correlation, and Grad-CAM heat map have been used to assess the efficacy of the proposed model. The outcome of this proposed model is more robust, and it may be useful for radiologists for faster diagnostics of COVID-19.

Impartially Validated Multiple Deep-Chain Models to Detect COVID-19 in Chest X-ray Using Latent Space Radiomics.

The COVID-19 pandemic continues to spread globally at a rapid pace, and its rapid detection remains a challenge due to its rapid infectivity and limited testing availability. One of the simply available imaging modalities in clinical routine involves chest X-ray (CXR), which is often used for diagnostic purposes. Here, we proposed a computer-aided detection of COVID-19 in CXR imaging using deep and conventional radiomic features. First, we used a 2D U-Net model to segment the lung lobes. Then, we extracted deep latent space radiomics by applying deep convolutional autoencoder (ConvAE) with internal dense layers to extract low-dimensional deep radiomics. We used Johnson–Lindenstrauss (JL) lemma, Laplacian scoring (LS), and principal component analysis (PCA) to reduce dimensionality in conventional radiomics. The generated low-dimensional deep and conventional radiomics were integrated to classify COVID-19 from pneumonia and healthy patients. We used 704 CXR images for training the entire model (i.e., U-Net, ConvAE, and feature selection in conventional radiomics). Afterward, we independently validated the whole system using a study cohort of 1597 cases. We trained and tested a random forest model for detecting COVID-19 cases through multivariate binary-class and multiclass classification. The maximal (full multivariate) model using a combination of the two radiomic groups yields performance in classification cross-validated accuracy of 72.6% (69.4–74.4%) for multiclass and 89.6% (88.4–90.7%) for binary-class classification.

Universum based Lagrangian twin bounded support vector machine to classify EEG signals

Background and objectiveThe detection of brain-related problems and neurological disorders like epilepsy, sleep disorder, and so on is done by using electroencephalogram (EEG) signals which contain noisy signals and outliers. Universum data contains a set of a sample that does not belong to any of the concerned classes and serves as the advanced knowledge about the data distribution. Earlier information has been utilized viably in improving classification performance. Recently a novel universum support vector machine (USVM) was proposed for EEG signal classification and further, a universum twin support vector machine (UTWSVM) was proposed based on USVM to improve the performance. Inspired by USVM and UTWSVM, this paper suggests a novel method called universum based Lagrangian twin bounded support vector machine (ULTBSVM), where universum data is utilized to incorporate the prior information about the data distribution to classify healthy and seizure EEG signals. MethodsIn the proposed ULTBSVM the square of the 2-norm of the slack variables is used to formulate the objective function strongly convex; hence it always gives unique solutions. Unlike twin support vector machine (TWSVM) and universum twin support vector machine (UTWSVM), the proposed ULTBSVM is having regularization terms that follow the structural risk minimization (SRM) principle and enhance the stability in the dual formulations, make the model well-posed and prevents the overfitting problem. Here, interracial EEG data have been considered as universum data to classify healthy and seizure signals. Several feature extraction techniques have been implemented to get important noiseless features. ResultsSeveral EEG datasets, as well as publicly available UCI datasets, are utilized to assess the performance of the proposed method. An analytical comparison has been performed of the proposed method with USVM and UTWSVM to detect seizure and healthy signals and for real-world data, the ULTBSVM is compared with the universum based models as well as TWSVM and the proposed method gives better results in most of the cases as compared to the other methods. ConclusionThe results clearly show that ULTBSVM is a potential method for the classification of EEG signals as well as real-world datasets having interracial data as universum data. Here we have used universum points for the binary class classification problem, but one can extend and use it for multi-class classification problems as well.

A robust intelligent zero-day cyber-attack detection technique

With the introduction of the Internet to the mainstream like e-commerce, online banking, health system and other day-to-day essentials, risk of being exposed to various are increasing exponentially. Zero-day attack(s) targeting unknown vulnerabilities of a software or system opens up further research direction in the field of cyber-attacks. Existing approaches either uses ML/DNN or anomaly-based approach to protect against these attacks. Detecting zero-day attacks through these techniques miss several parameters like frequency of particular byte streams in network traffic and their correlation. Covering attacks that produce lower traffic is difficult through neural network models because it requires higher traffic for correct prediction. This paper proposes a novel robust and intelligent cyber-attack detection model to cover the issues mentioned above using the concept of heavy-hitter and graph technique to detect zero-day attacks. The proposed work consists of two phases (a) Signature generation and (b) Evaluation phase. This model evaluates the performance using generated signatures at the training phase. The result analysis of the proposed zero-day attack detection shows higher performance for accuracy of 91.33% for the binary classification and accuracy of 90.35% for multi-class classification on real-time attack data. The performance against benchmark data set CICIDS18 shows a promising result of 91.62% for binary-class classification on this model. Thus, the proposed approach shows an encouraging result to detect zero-day attacks.

Cascade Network with Guided Loss and Hybrid Attention for Finding Good Correspondences

Finding good correspondences is a critical prerequisite in many feature based tasks. Given a putative correspondence set of an image pair, we propose a neural network which finds correct correspondences by a binary-class classifier and estimates relative pose through classified correspondences. First, we analyze that due to the imbalance in the number of correct and wrong correspondences, the loss function has a great impact on the classification results. Thus, we propose a new Guided Loss that can directly use evaluation criterion (Fn-measure) as guidance to dynamically adjust the objective function during training. We theoretically prove that the perfect negative correlation between the Guided Loss and Fn-measure, so that the network is always trained towards the direction of increasing Fn-measure to maximize it. We then propose a hybrid attention block to extract feature, which integrates the Bayesian attentive context normalization (BACN) and channel-wise attention (CA). BACN can mine the prior information to better exploit global context and CA can capture complex channel context to enhance the channel awareness of the network. Finally, based on our Guided Loss and hybrid attention block, a cascade network is designed to gradually optimize the result for more superior performance. Experiments have shown that our network achieves the state-of-the-art performance on benchmark datasets. Our code will be available in https://github.com/wenbingtao/GLHA.

Deep Regularized Discriminative Network

Traditional linear discriminant analysis (LDA) approach discards the eigenvalues which are very small or equivalent to zero, but quite often eigenvectors corresponding to zero eigenvalues are the important dimensions for discriminant analysis. We propose an objective function which would utilize both the principal as well as nullspace eigenvalues and simultaneously inherit the class separability information onto its latent space representation. The idea is to build a convolutional neural network (CNN) and perform the regularized discriminant analysis on top of this and train it in an end-to-end fashion. The backpropagation is performed with a suitable optimizer to update the parameters so that the whole CNN approach minimizes the within class variance and maximizes the total class variance information suitable for both multi-class and binary class classification problems. Experimental results on four databases for multiple computer vision classification tasks show the efficacy of our proposed approach as compared to other popular methods.

Transfer Learning approach for grading of Diabetic Retinopathy

There has been a wide interest in applying Deep Learning (DL) algorithms for automated binary and multi class classification of colour fundus images affected with Diabetic Retinopathy (DR). These algorithms have shown high sensitivity and specificity for detecting DR in non-clinical setup. Transfer learning has been successfully tested in many medical imaging applications like skin cancer detection, pulmonary nodule detection, Alzheimer’s disease etc. This paper experiments with the different DL architectures such as VGG19, InceptionV3, ResNet50, MobileNet and NASNet for automated DR classification (binary and multi class) on Messidor dataset. The dataset is publicly available, and comprises of 1200 retinal fundus images. The images belong to four different classes of DR namely, normal (class 0), mild (class 1), moderate (class 2) and severe (class 3), graded based on the severity level of DR. In our experiment, we have enhanced the quality of input images by applying algorithms like CLAHE (Contrast Limited Adaptive Histogram Equalisation) algorithm and Powerlaw transformation as pre-processing techniques, which work on the small image patches with high accuracy, contrast limiting and image sharpening. Hyper parameter tuning on pretrained InceptionV3 architecture, resulted in enhancing the accuracy of the model. Both binary and multi class results were analysed considering inter class (one class with another class) accuracies. We achieved an accuracy of 78% between class 0 and class 1, the accuracy between class 0 and class 2 further reduced to 69%, while class 1 and class 2 showed an accuracy of 61%. Moreover, the interclass class accuracy between class 1 and class 3 was 62%, class 2 and class 3 further reduced to 49%. The accuracy further diminished between class 0 and class 3 to 32%. These experiments suggest that the pretrained models provided better results in classifying normal and mild, but they were not that much efficient in classifying moderate-severe and normal-severe binary classifications.

Hybrid CNN-SVD Based Prominent Feature Extraction and Selection for Grading Diabetic Retinopathy Using Extreme Learning Machine Algorithm

This paper exploits the extreme learning machine (ELM) approach to address diabetic retinopathy (DR), a medical condition in which impairment occurs to the retina caused by diabetes. DR, a leading cause of blindness worldwide, is a sort of swelling leakage due to excessive blood sugar in the retina vessels. An early-stage diagnosis is therefore beneficial to prevent diabetes patients from losing their sight. This study introduced a novel method to detect DR for binary class and multiclass classification based on the APTOS-2019 blindness detection and Messidor-2 datasets. First, DR images have been pre-processed using Ben Graham’s approach. After that, contrast limited adaptive histogram equalization (CLAHE) has been used to get contrast-enhanced images with lower noise and more distinguishing features. Then a novel hybrid convolutional neural network-singular value decomposition model has been developed to reduce input features for classifiers. Finally, the proposed method uses an ELM algorithm as the classifier that minimizes the training time cost. The experiments focus on accuracy, precision, recall, and F1-score and demonstrate the feasibility of adopting the proposed scheme for DR diagnosis. The method outperforms the existing techniques and shows an optimistic accuracy and recall of 99.73% and 100%, respectively, for binary class. For five stages of DR classification, the proposed model achieved an accuracy of 98.09% and 96.26% for APTOS-2019 and Messidor-2 datasets, respectively, which outperformed the existing state-of-art models.

Pulmonary Nodule Classification Using Feature and Ensemble Learning-Based Fusion Techniques

The Pulmonary nodule indicates the presence of lung cancer. The deep convolutional neural networks (DCNNs) have been widely used to classify the pulmonary nodule as benign or malignant. However, an individual learner usually performs unsatisfactorily due to limited response space, incorrect selection of hypothesis space, or falling into local minimums. To investigate these issues, we propose ensemble learners fusion techniques based on averaging of prediction score and maximum vote score (MAX-VOTE). First, the support vector machine (SVM) and AdaBoostM2 machine learning algorithms are trained on the deep features from DCNNs. The results of both classifiers are fused separately based on averaging of the prediction score. Secondly, the feature fusion technique is developed by fusing the feature of three DCNNs (AlexNet, VGG-16 and VGG-19) through predefined rules. After that, the SVM and AdaBoostM2 are trained on fused features independently to build ensemble learners by fusing the multiple DCNN learners. The predictions of all DCNN learners are fused based on the MAX-VOTE. The results show that the ensemble learners based MAX-VOTE technique yields better performance out of twelve single learners for binary class classification of pulmonary nodules. The proposed fusion techniques are also tested for multi-class classification problem. The SVM based feature fusion technique performs better as compared to all the implemented and the state-of-the-art techniques. The achieved maximum accuracy, AUC and specificity scores are 96.89%±0.25, 99.21%±0.10 and 97.70%±0.21, respectively.

An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks

With the rapid expansion of intelligent resource-constrained devices and high-speed communication technologies, the Internet of Things (IoT) has earned wide recognition as the primary standard for low-power lossy networks (LLNs). Nevertheless, IoT infrastructures are vulnerable to cyber-attacks due to the constraints in computation, storage, and communication capacity of the endpoint devices. From one side, the majority of newly developed cyber-attacks are formed by slightly mutating formerly established cyber-attacks to produce a new attack that tends to be treated as normal traffic through the IoT network. From the other side, the influence of coupling the deep learning techniques with the cybersecurity field has become a recent inclination of many security applications due to their impressive performance. In this paper, we provide the comprehensive development of a new intelligent and autonomous deep-learning-based detection and classification system for cyber-attacks in IoT communication networks that leverage the power of convolutional neural networks, abbreviated as IoT-IDCS-CNN (IoT based Intrusion Detection and Classification System using Convolutional Neural Network). The proposed IoT-IDCS-CNN makes use of high-performance computing that employs the robust Compute Unified Device Architectures (CUDA) based Nvidia GPUs (Graphical Processing Units) and parallel processing that employs high-speed I9-core-based Intel CPUs. In particular, the proposed system is composed of three subsystems: a feature engineering subsystem, a feature learning subsystem, and a traffic classification subsystem. All subsystems were developed, verified, integrated, and validated in this research. To evaluate the developed system, we employed the Network Security Laboratory-Knowledge Discovery Databases (NSL-KDD) dataset, which includes all the key attacks in IoT computing. The simulation results demonstrated a greater than 99.3% and 98.2% cyber-attack classification accuracy for the binary-class classifier (normal vs. anomaly) and the multiclass classifier (five categories), respectively. The proposed system was validated using a K-fold cross-validation method and was evaluated using the confusion matrix parameters (i.e., true negative (TN), true positive (TP), false negative (FN), false positive (FP)), along with other classification performance metrics, including precision, recall, F1-score, and false alarm rate. The test and evaluation results of the IoT-IDCS-CNN system outperformed many recent machine-learning-based IDCS systems in the same area of study.

A machine learning approach to predict ethnicity using personal name and census location in Canada.

BackgroundCanada is an ethnically-diverse country, yet its lack of ethnicity information in many large databases impedes effective population research and interventions. Automated ethnicity classification using machine learning has shown potential to address this data gap but its performance in Canada is largely unknown. This study conducted a large-scale machine learning framework to predict ethnicity using a novel set of name and census location features.MethodsUsing census 1901, the multiclass and binary class classification machine learning pipelines were developed. The 13 ethnic categories examined were Aboriginal (First Nations, Métis, Inuit, and all-combined)), Chinese, English, French, Irish, Italian, Japanese, Russian, Scottish, and others. Machine learning algorithms included regularized logistic regression, C-support vector, and naïve Bayes classifiers. Name features consisted of the entire name string, substrings, double-metaphones, and various name-entity patterns, while location features consisted of the entire location string and substrings of province, district, and subdistrict. Predictive performance metrics included sensitivity, specificity, positive predictive value, negative predictive value, F1, Area Under the Curve for Receiver Operating Characteristic curve, and accuracy.ResultsThe census had 4,812,958 unique individuals. For multiclass classification, the highest performance achieved was 76% F1 and 91% accuracy. For binary classifications for Chinese, French, Italian, Japanese, Russian, and others, the F1 ranged 68–95% (median 87%). The lower performance for English, Irish, and Scottish (F1 ranged 63–67%) was likely due to their shared cultural and linguistic heritage. Adding census location features to the name-based models strongly improved the prediction in Aboriginal classification (F1 increased from 50% to 84%).ConclusionsThe automated machine learning approach using only name and census location features can predict the ethnicity of Canadians with varying performance by specific ethnic categories.

Brain wave classification for divergent hand movements

Brain-Computer Interface (BCI) is an emerging technology in medical diagnosis and rehabilitation. In this study, by the acquisition of Electroencephalogram (EEG) signals from 30 healthy participants who perform four different hand movements, necessary features are extracted and classified to determine their accuracies. Statistical time domain features are extracted from the mu and beta frequency band. The Event related desynchronization (ERD)/Event related synchronization (ERS) measurements are extracted, from which it was evident that both mu and beta frequency bands are more efficient in the C3 channel. By applying the Paired Samples t-test, the extracted features are analyzed and were determined to have a 95% significant level of difference between the mu and beta band, being statistically efficient in the beta band of the C3 channel. By employing different classifiers such as Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Naïve Bayesian classifier and Binary Decision Tree (BDT) algorithms on both channel’s mu and beta frequency bands, it was observed that the performance of beta frequency band classifiers shows 90% accuracy in binary class classification. In the comparative study of all these classifiers, LDA and Naïve Bayes show above 95% accuracy for binary class classification.

Binary Classification for Failure Risk Assessment.

Survival analysis is tremendously powerful, and is a popular methodology for analyzing time to event models in bioinformatics. Furthermore, several of its extensions can simultaneously perform variable selection in conjunction with model estimation. While this flexibility is extremely desirable, under certain scenarios, binary class variable selection and classification methods might provide more reliable risk estimates. Synthetic simulations and real data case studies suggest that when (1) randomly censored points comprise only a small portion of data, (2) biological markers are weak, (3) it is desired to compute risk across predetermined time intervals, and (4) the assumptions of the competing time to event models are violated, binary class models tend to perform superior. In practice, it might be prudent to test both model families to guarantee adequate analysis. Here we describe the pipeline of binary class feature selection and classification for time to event risk assessment.

Feature subset and time segment selection for the classification of EEG data based motor imagery

The selection of feature subset and time segment is of great significance to the benefit of motor imagery classification. Hence, applying it in classification as well as itself alone has become an increasingly important research field in the brain-computer interface (BCI) systems. Most of the existing literatures only focus on binary-class classification situations in a fixed time segment. However, the modern BCI systems usually have to deal with more motor imagery classes. In this paper, we propose two Parzen window based methods to select the discriminative feature subset and subject-specific time segment. Further, we extend the proposed methods to multi-class issues. Finally, a soft Naive Bayesian classifier is designed to solve not only binary-class but also multi-class motor imagery problems. The proposed methods are validated on two well-known datasets, BCI competition IV dataset 2a and 2b. Experimental results reveal that the proposed methods achieve an improvement of 4.38 % for 2a and 2.54 % for 2b in comparison with the state-of-the-art methods, respectively. Meanwhile, both proposed methods achieve an improvement of 0.04 for 2a and 0.05 for 2b in kappa coefficient, respectively. Besides, the proposed multi-class methods would potentially contribute to the online BCI systems in practice.

The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems.

The pursuit to spot abnormal behaviors in and out of a network system is what led to a system known as intrusion detection systems for soft computing besides many researchers have applied machine learning around this area. Obviously, a single classifier alone in the classifications seems impossible to control network intruders. This limitation is what led us to perform dimensionality reduction by means of correlation-based feature selection approach (CFS approach) in addition to a refined ensemble model. The paper aims to improve the Intrusion Detection System (IDS) by proposing a CFS + Ensemble Classifiers (Bagging and Adaboost) which has high accuracy, high packet detection rate, and low false alarm rate. Machine Learning Ensemble Models with base classifiers (J48, Random Forest, and Reptree) were built. Binary classification, as well as Multiclass classification for KDD99 and NSLKDD datasets, was done while all the attacks were named as an anomaly and normal traffic. Class labels consisted of five major attacks, namely Denial of Service (DoS), Probe, User-to-Root (U2R), Root to Local attacks (R2L), and Normal class attacks. Results from the experiment showed that our proposed model produces 0 false alarm rate (FAR) and 99.90% detection rate (DR) for the KDD99 dataset, and 0.5% FAR and 98.60% DR for NSLKDD dataset when working with 6 and 13 selected features.

Binary Class Classification of Software Faults in Software Modules using Popular Machine Learning Techniques

Software engineering is an important area that deals with development and maintenance of software. After developing a software, it is always important to track its performance. One has to always see whether the software functions according to customer requirements. To ensure this, faulty and non- faulty modules must be identified. For this purpose, one can make use of a model for binary class classification of faults. Different technique's outputs differ in one or the other way with respect to the following: fault dataset used, complexity, classification algorithm implemented, etc. Various machine learning techniques can be used for this purpose. But this paper deals with the best classification algorithms available till date and they are decision tree, random forest, naive bayes and logistic regression (tree-based techniques and bayesian based techniques). The motive behind developing such a project is to identify the faulty modules within a software before the actual software testing takes place. As a result, the time consumed by testers or the workload of the testers can be reduced to an extent. This work is very well useful to those working in software industry and also to those people carrying out research in software engineering where the lifecycle of development of a software is discussed.

A Worker’s Fitness-for-Duty Status Identification Based on Biosignals to Reduce Human Error in Nuclear Power Plants

Human error has been highlighted as main cause of industrial and nuclear accidents. One of the key issues related to human error is a worker’s fitness for duty (FFD). FFD refers to the mental and physical ability of employees to safely perform their job. The objective of this study is to investigate the feasibility of identifying a worker’s FFD status using biosignals. The FFD statuses examined were with respect to alcohol use, depression, stress, anxiety, and sleep deprivation. Biosignals examined in the study include electrical activity in the brain measured by electroencephalogram and referred to as EEG, electrical activity of the heartbeat measured by electrocardiogram and referred to as ECG, galvanic skin response (GSR), blood volume pulse (BVP), dynamic changes in blood pressure and referred to as BPHEG, and respiration. A total of 114 volunteers participated in the study as experimental subjects from whom biodata were collected during their resting states (eyes closed and eyes open). The steps followed in the study include signal preprocessing, power spectrum feature analysis, important feature selection, and support vector machine (SVM) classification using 5-fold cross validation to identify a worker’s FFD status. Among the 70 biosignal indicators, important features were selected by Multivariate Analysis of Variance (MANOVA). The best model developed with the SVM used 64 biosignal indicators and showed a binary (fit or unfit) classification accuracy of 99.4% and a multi-classification accuracy of 97.7%. While limitations of the current work remain, the study indicates the possibility of implementing an effective FFD management program to reduce human error in plant operations. A thumbnail sketch of the study is as follows: 1. To reduce human error in nuclear operations, use of biosignals was investigated to identify FFD status of workers. 2. EEG, ECG, GSR, BVP, BPHEG, and respiration signals were used to identify a worker’s FFD status. 3. The SVM-based model was successfully implemented for multi-class and binary-class FFD classification.

A comparative analysis of machine learning methods for emotion recognition using EEG and peripheral physiological signals

Emotion recognition using brain signals has the potential to change the way we identify and treat some health conditions. Difficulties and limitations may arise in general emotion recognition software due to the restricted number of facial expression triggers, dissembling of emotions, or among people with alexithymia. Such triggers are identified by studying the continuous brainwaves generated by human brain. Electroencephalogram (EEG) signals from the brain give us a more diverse insight on emotional states that one may not be able to express. Brainwave EEG signals can reflect the changes in electrical potential resulting from communications networks between neurons. This research involves analyzing the epoch data from EEG sensor channels and performing comparative analysis of multiple machine learning techniques [namely Support Vector Machine (SVM), K-nearest neighbor, Linear Discriminant Analysis, Logistic Regression and Decision Trees each of these models] were tested with and without principal component analysis (PCA) for dimensionality reduction. Grid search was also utilized for hyper-parameter tuning for each of the tested machine learning models over Spark cluster for lowered execution time. The DEAP Dataset was used in this study, which is a multimodal dataset for the analysis of human affective states. The predictions were based on the labels given by the participants for each of the 40 1-min long excerpts of music. music. Participants rated each video in terms of the level of arousal, valence, like/dislike, dominance and familiarity. The binary class classifiers were trained on the time segmented, 15 s intervals of epoch data, individually for each of the 4 classes. PCA with SVM performed the best and produced an F1-score of 84.73% with 98.01% recall in the 30th to 45th interval of segmentation. For each of the time segments and “a binary training class” a different classification model converges to a better accuracy and recall than others. The results prove that different classification models must be used to identify different emotional states.

A novel CNN based Alzheimer’s disease classification using hybrid enhanced ICA segmented gray matter of MRI

Predicting Alzheimer’s Disease (AD) from Mild Cognitive Impairment (MCI) and Cognitive Normal (CN) has become wide. Recent advancement in neuroimaging in adoption with machine learning techniques are especially useful for pattern recognition of medical imaging to assist the physician in early diagnosis of AD. It is observed that the early abnormal brain atrophy and healthy brain atrophy are same. In our endeavor, we proposed a model that differentiation MCI and CN more accurately to escalate early diagnosis of AD. In this paper, we applied both binary and multi class classification, 4463 Slide are divided in to two groups one for training and another for testing at subject level, achieves 100 % of accuracy, 100 % of sensitivity and 100 % of Specificity in the case of AD-CN. 96.2 % of accuracy, 93 % Sensitivity and 100 % Specificity in the case of AD-MCI. 98.0 % of accuracy, 96 % of sensitivity, 100 specificity in the case of CN-MCI. 86.7 % accuracy, 89.6 % of sensitivity, 86.61 % of specificity in the case of AD-MCI-CN. The model is further tested using 10 fold cross validation and obtained 98.0 % of accuracy, to differentiate CN and MCI. Our proposed framework generated results are significantly improving prediction of AD from MCI and CN than compare to the previous work flows and used to differentiate the AD at early stage.

A novel one-class classification approach to accurately predict disease-gene association in acute myeloid leukemia cancer.

Disease causing gene identification is considered as an important step towards drug design and drug discovery. In disease gene identification and classification, the main aim is to identify disease genes while identifying non-disease genes are of less or no significant. Hence, this task can be defined as a one-class classification problem. Existing machine learning methods typically take into consideration known disease genes as positive training set and unknown genes as negative samples to build a binary-class classification model. Here we propose a new One-class Classification Support Vector Machines (OCSVM) method to precisely classify candidate disease genes. Our aim is to build a model that concentrate its focus on detecting known disease-causing gene to increase sensitivity and precision. We investigate the impact of our proposed model using a benchmark consisting of the gene expression dataset for Acute Myeloid Leukemia (AML) cancer. Compared with the traditional methods, our experimental result shows the superiority of our proposed method in terms of precision, recall, and F-measure to detect disease causing genes for AML. OCSVM codes and our extracted AML benchmark are publicly available at: https://github.com/imandehzangi/OCSVM.