Extreme Learning Machine Classifier Research Articles

Prognostication of Shortwave Radiation Using an Improved No-Tuned Fast Machine Learning

Shortwave radiation density flux (SRDF) modeling can be key in estimating actual evapotranspiration in plants. SRDF is the result of the specific and scattered reflection of shortwave radiation by the underlying surface. SRDF can have profound effects on some plant biophysical processes such as photosynthesis and land surface energy budgets. Since it is the main energy source for most atmospheric phenomena, SRDF is also widely used in numerical weather forecasting. In the current study, an improved version of the extreme learning machine was developed for SRDF forecasting using the historical value of this variable. To do that, the SRDF through 1981–2019 was extracted by developing JavaScript-based coding in the Google Earth Engine. The most important lags were found using the auto-correlation function and defined fifteen input combinations to model SRDF using the improved extreme learning machine (IELM). The performance of the developed model is evaluated based on the correlation coefficient (R), root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash–Sutcliffe efficiency (NSE). The shortwave radiation was developed for two time ahead forecasting (R = 0.986, RMSE = 21.11, MAPE = 8.68%, NSE = 0.97). Additionally, the estimation uncertainty of the developed improved extreme learning machine is quantified and compared with classical ELM and found to be the least with a value of ±3.64 compared to ±6.9 for the classical extreme learning machine. IELM not only overcomes the limitation of the classical extreme learning machine in random adjusting of bias of hidden neurons and input weights but also provides a simple matrix-based method for practical tasks so that there is no need to have any knowledge of the improved extreme learning machine to use it.

Extreme learning Machine-based classifier for fault diagnosis of rotating Machinery using a residual network and continuous wavelet transform

Effective fault diagnosis of rotating machinery is essential for the predictive maintenance of modern industries. In this study, a novel framework that combines a residual network (ResNet) as a backbone and an extreme learning machine (ELM) as a classifier (RNELM) is proposed to diagnose faults of rotating machinery. Firstly, continuous wavelet transform (CWT) is used to convert the raw time-domain signal into time–frequency domain images. Subsequently, the ResNet backbone in the framework extracts advanced features from the images for the ELM classifier, substantially improving the fault diagnosis performance. The performance of the framework is compared with four other methods using four evaluation metrics on datasets from Case Western Reserve University (CWRU), laboratory results and industrial applications. The experimental results show that the RNELM achieves outstanding results on the test samples of the three datasets, demonstrating the excellent performance and practicability of the proposed framework for fault diagnosis.

A Novel Region-Extreme Convolutional Neural Network for Melanoma Malignancy Recognition

Melanoma malignancy recognition is a challenging task due to the existence of intraclass similarity, natural or clinical artefacts, skin contrast variation, and higher visual similarity among the normal or melanoma-affected skin. To overcome these problems, we propose a novel solution by leveraging “region-extreme convolutional neural network” for melanoma malignancy recognition as malignant or benign. Recent works on melanoma malignancy recognition employed the traditional machine learning techniques based on various handcrafted features or the recently introduced CNN network. However, the efficient training of these models is possible, if they localize the melanoma affected region and learn high-level feature representation from melanoma lesion to predict melanoma malignancy. In this paper, we incorporate this observation and propose a novel “region-extreme convolutional neural network” for melanoma malignancy recognition. Our proposed region-extreme convolutional neural network refines dermoscopy images to eliminate natural or clinical artefacts, localizes melanoma affected region, and defines precise boundary around the melanoma lesion. The defined melanoma lesion is used to generate deep feature maps for model learning using the extreme learning machine (ELM) classifier. The proposed model is evaluated on two challenge datasets (ISIC-2016 and ISIC-2017) and performs better than ISIC challenge winners. Our region-extreme convolutional neural network recognizes the melanoma malignancy 85% on ISIC-2016 and 93% on ISIC-2017 datasets. Our region-extreme convolutional neural network precisely segments the melanoma lesion with an average Jaccard index of 0.93 and Dice score of 0.94. The region-extreme convolutional neural network has several advantages: it eliminates the clinical and natural artefacts from dermoscopic images, precisely localizes and segments the melanoma lesion, and improves the melanoma malignancy recognition through feedforward model learning. The region-extreme convolutional neural network achieves significant performance improvement over existing methods that makes it adaptable for solving complex medical image analysis problems.

Plant Diseases Classification using Machine Learning

Plant diseases are one of source of obstruction in the quality and productivity of plants which can lead to the shortage of food supply. Therefore, plant disease classification is essential to the agriculture industry. The objective of this research is to classify the plant diseases by assessing the images of the leaves with the application of Extreme Learning Machine (ELM), a Machine Learning classification algorithm with a single layer feed-forward neural network. This work proposed image features as input where the image is pre-processed via HSV colour space and features extraction via Haralick textures. The features are then fitted in the ELM classifier to perform the model training and testing. The accuracy of ELM is then calculated after the testing has been done. The dataset used comprises of tomato plant leaves which is a subset of the Plant-Village dataset. The results produced from the ELM shows a better accuracy that is 84.94% when compared to other models such as the Support Vector Machine and Decision Tree.

Generalized Oppositional Moth Flame Optimization with Crossover Strategy: An Approach for Medical Diagnosis

In the original Moth-Flame Optimization (MFO), the search behavior of the moth depends on the corresponding flame and the interaction between the moth and its corresponding flame, so it will get stuck in the local optimum easily when facing the multi-dimensional and high-dimensional optimization problems. Therefore, in this work, a generalized oppositional MFO with crossover strategy, named GCMFO, is presented to overcome the mentioned defects. In the proposed GCMFO, GOBL is employed to increase the population diversity and expand the search range in the initialization and iteration jump phase based on the jump rate; crisscross search (CC) is adopted to promote the exploitation and/or exploration ability of MFO. The proposed algorithm’s performance is estimated by organizing a series of experiments; firstly, the CEC2017 benchmark set is adopted to evaluate the performance of GCMFO in tackling high-dimensional and multimodal problems. Secondly, GCMFO is applied to handle multilevel thresholding image segmentation problems. At last, GCMFO is integrated into kernel extreme learning machine classifier to deal with three medical diagnosis cases, including the appendicitis diagnosis, overweight statuses diagnosis, and thyroid cancer diagnosis. Experimental results and discussions show that the proposed approach outperforms the original MFO and other state-of-the-art algorithms on both convergence speed and accuracy. It also indicates that the presented GCMFO has a promising potential for application.

Advanced extreme learning machine‐based ensemble classification scheme with enhanced data perturbation for human DNA sequences

AbstractThe dramatic growth in machine learning has brought in significant features and quantified non‐linear associations in the data derived from sensitive medical datasets. The data should be preserved without influencing the associated classifications by applying a robust, effective and reliable data perturbation technique before enforcing ensemble classification. In this paper, an Integrated Condensation Scheme imposed Privacy Preserving Rotation‐based Data Perturbation and Ensemble Classification (ICS‐PPR‐DPEC) is proposed for ensuring privacy of such sensitive data. Condensation Algorithm‐based Data Perturbation is used for constructing homogenous groups determined from the distance between tuples. It also generates a rotation matrix for conducting perturbation that ensures higher data sensitivity protection before it is sent for classification. Advanced Extreme Learning Machine‐based Ensemble Classification Scheme includes kernel, norm‐optimized and regularized Extreme Learning Machine (ELM)‐based classifiers for attaining predominant classification accuracy in identifying human DNA sequences. This approach facilitates classification by constructing ensembles which are trained through randomly resampled ELM classifiers. It includes an objective function that systematically improves the accuracy and diversity among resulting ensembles. The experimental results of the proposed ICS‐PPR‐DPEC are found to be excellent in terms of classification Accuracy, Precision, Recall, and Kappa statistic when compared to the benchmarked techniques.

EEG-based motor imagery classification using digraph Fourier transforms and extreme learning machines

Motor imagery patterns are extensively exploited in brain-computer interface systems in order to control outer devices without using peripheral nerves or muscles. Classification of these patterns can be based on the associated electroencephalogram (EEG) signals. Recent approaches addressed this classification problem through techniques exploiting mainly information from one or two EEG channels. However, these approaches overlook correlations between multiple EEG channels. In this paper, we create motor-imagery classification systems based on graph-theoretic models of multichannel EEG signals. In particular, multivariate autoregressive models are used to establish the relations between the EEG channels and construct directed graph signals. Also, we constructed undirected graph signal models with Gaussian-weighted distances between graph nodes. Then, a novel variant of the graph Fourier transform is applied to the directed and undirected graph models with and without edge weights. Distinctive features were thus extracted from the transform coefficients. Additional features were computed using common spatial patterns, polynomial representations and principal components of EEG signals. Significant performance improvements were achieved using extreme learning machine (ELM) classifiers. For Dataset Ia of the BCI Competition 2003, our approach led to a classification accuracy of 96.58% with fully-connected weighted directed graph features computed on the delta-band EEG signals. For the six subjects of the Dataset 1 of the BCI Competition IV, our approach compared well with other state-of-the-art methods in the alpha and beta EEG bands.

Improving the separability of drowsiness and alert EEG signals using analytic form of wavelet transform

Drowsiness is a state in which individual decision-making ability is declined. Drowsy driving is one of the key factors for worldwide accidents which claim many lives, causing injuries and permanent disability. Electroencephalogram (EEG) signals are the most common source used for the detection of drowsiness because of changes in neuron behavior. More hidden information can be obtained if it is decomposed into multi-components. Therefore, a flexible analytic wavelet transform (FAWT) based drowsiness detection is explored in this paper. FAWT decomposes a signal in multi-frequency subbands. Multiple features are extracted from these subbands and selected using statistical analysis. Different classification techniques are employed to separate drowsy and alert signals. An accuracy of 95.6%, a sensitivity of 95.2%, a specificity of 98.6%, a precision of 99.1%, and 96.1% of area under the curve are achieved with an extreme learning machine classifier. This shows that our developed method can find more hidden information which is helpful for accurate detection of drowsiness.

Detection of k-complexes in EEG signals using a multi-domain feature extraction coupled with a least square support vector machine classifier

Sleep scoring is one of the primary tasks for the classification of sleep stages using electroencephalogram (EEG) signals. It is one of the most important diagnostic methods in sleep research and must be carried out with a high degree of accuracy because any errors in the scoring in the patient’s sleep EEG recordings can cause serious problems. The aim of this research is to develop a new automatic method for detecting the most important characteristics in sleep stage 2 such as k-complexes based on multi-domain features. In this study, each EEG signal is divided into a set of segments using a sliding window technique. Based on extensive experiments during the training phase, the size of the sliding window is set to 0.5 s (s). Then a set of statistical, fractal, frequency and non-linear features are extracted from each epoch based on the time domain, Katz's algorithm, power spectrum density (PSD) and tunable Q-factor wavelet transform (TQWT). As a result, a vector of twenty-two features is obtained to represent each EEG segment. In order to detect k-complexes, the extracted features were analysed for their ability to detect the k-complex waveforms. Based on the analysis of the features, twelve out of twenty-two features are selected and forwarded to a least square support vector machine (LS-SVM) classifier to identify k-complexes in EEG signals. A set of various classification techniques of K-means and extreme learning machine classifiers are used to compare the obtained results and to evaluate the performance of the proposed method.The experimental results showed that the proposed method, based on multi-domain features, achieved better recognition results than other methods and classifiers. An average accuracy, sensitivity and specificity of 97.7 %, 97 %, and 94.2 % were obtained, respectively, with the CZ-A1 channel according to the R&K standard. The experimental results with high classification performance demonstrated that the technique can help doctors optimize the diagnosis and treatment of sleep disorders.

Optimizing High-Performance Weighted Extreme Learning Machine with DAPM for Imbalance Credit Card Fraud Detection

Fraudulent activities associated with financial transactions are observed in the present scenario, especially with the use of credit cards, at a fast rate. As banking services are rising in digitalization and mobile banking is on the increase in structured written requests, credit card payments rates rise all year, with billions of transactions detected as unfair. For financial institutions to maintain the goodwill of their customers a fraud detection system requiring different detection strategies is therefore extremely important. Researchers and practitioners, using different algorithms, have proposed many methods for fraud detection to find the pattern of fraud. Data mining (DM) algorithms were influential to detect fraudulent transactions by combating fraudsters' attacks on the classic frameworks for preventing fraud. The paper aims to classify fraudulent transactions by Weighted Extreme Learning Machine (WELM) classifiers of 2 Artificial Neural networks (ANN) and 3 separate data sets of Credit Card Fraud (CCF). We use a high-performance Weighted Extreme Learning Machine (HPWELM). The efficiency of the classifiers is calculated based on accuracy, precision, recall & G-mean. The research work has been implemented in Python 3.6. The results are represented in form of tables & snapshots. Results demonstrate that accuracy of the HPWELM classifier has achieved a remarkable improvement in the training and testing phases of the algorithm.

A Novel Smart Motor Imagery Intention Human-Computer Interaction Model Using Extreme Learning Machine and EEG Signals.

The brain is the central nervous system that governs human activities. However, in modern society, more and more diseases threaten the health of the brain and nerves and spinal cord, making the human brain unable to conduct normal information interaction with the outside world. The rehabilitation training of the brain-computer interface can promote the nerve repair of the sensorimotor cortex in patients with brain diseases. Therefore, the research of brain-computer interface for motor imaging is of great significance for patients with brain diseases to restore motor function. Due to the characteristics of non-stationary, nonlinear, and individual differences of EEG signals, there are still many difficulties in the analysis and classification of EEG signals at this stage. In this study, the Extreme Learning Machine (ELM) model was used to classify motor-imaging EEG signals, identify the user’s intention, and control external devices. Considering that single-modal features cannot represent the core information, this study uses a fusion feature that combines temporal and spatial features as the final feature data. The fusion features are input to the trained ELM classifier, and the final classification result is obtained. Two sets of BCI competition data in the BCI competition public database are used to verify the validity of the model. The experimental results show that the ELM model has achieved a classification accuracy of 0.7832 in the classification task of Data Sets IIb, which is higher than other comparison algorithms, and shows universal applicability among different subjects. In addition, the average recognition rate of this model in the Data Sets IIIa classification task reaches 0.8347, which has obvious advantages compared with the comparative classification algorithm. The classification effect is smaller than the classification effect obtained by the champion algorithm of the same project, which has certain reference value.

Seizure Detection Based on Adaptive Feature Extraction by Applying Extreme Learning Machines

Epilepsy is one of the most common chronic disorder which negatively affects the patients' life. The functionality of the brain can be obtained from brain signals and it is vital to analyze and examine the brain signals in seizure detection processes. In this study, we performed machine learning-based and signal processing methods to detect epileptic signals. To do that, we examined three different EEG signals (healthy, ictal, and interictal) with two different classes (healthy ones and epileptic ones). Our proposed method consists of three stages which are preprocessing, feature extraction, and classification. In the preprocessing phase, EEG signals normalized to scale all samples into [0,1] range. After Stockwell Transform was applied and chaotic features and Parseval's Energy collected from each EEG signal. In the last part, EEG signals were classified with ELM (Extreme Learning Machines) with different parameters. Our study shows the best classification accuracy obtained from the Sigmoid activation function with the number of 100 hidden neurons. The highlights of this study are: Stockwell Transform is used; Entropy values are selected based on the adaptive process. Threshold values are determined according to the error rates; ELM classifier algorithm is applied.

Skin Lesion Segmentation and Multiclass Classification Using Deep Learning Features and Improved Moth Flame Optimization.

Manual diagnosis of skin cancer is time-consuming and expensive; therefore, it is essential to develop automated diagnostics methods with the ability to classify multiclass skin lesions with greater accuracy. We propose a fully automated approach for multiclass skin lesion segmentation and classification by using the most discriminant deep features. First, the input images are initially enhanced using local color-controlled histogram intensity values (LCcHIV). Next, saliency is estimated using a novel Deep Saliency Segmentation method, which uses a custom convolutional neural network (CNN) of ten layers. The generated heat map is converted into a binary image using a thresholding function. Next, the segmented color lesion images are used for feature extraction by a deep pre-trained CNN model. To avoid the curse of dimensionality, we implement an improved moth flame optimization (IMFO) algorithm to select the most discriminant features. The resultant features are fused using a multiset maximum correlation analysis (MMCA) and classified using the Kernel Extreme Learning Machine (KELM) classifier. The segmentation performance of the proposed methodology is analyzed on ISBI 2016, ISBI 2017, ISIC 2018, and PH2 datasets, achieving an accuracy of 95.38%, 95.79%, 92.69%, and 98.70%, respectively. The classification performance is evaluated on the HAM10000 dataset and achieved an accuracy of 90.67%. To prove the effectiveness of the proposed methods, we present a comparison with the state-of-the-art techniques.

Gradient Feature-Oriented 3-D Domain Adaptation for Hyperspectral Image Classification

Domain adaptation, which cleverly applies the classifier learned from the source domain with sufficient labeled samples to the target domain with limited labeled samples, provides a feasible alternative to handle the small training sample problem of hyperspectral image (HSI) classification and has attracted much attention in the research field recently. Apparently, feature discriminative ability is vital for domain adaptation, which plays a crucial role during the migration process of transfer learning. In this article, a gradient feature-oriented 3-D domain adaptation (GF-3DDA) approach is proposed for HSI classification. First, 3-D Gabor is employed to remove noise from the original data, and two 2-D gradient-based features, 2-D Sobel gradient (SG) and 2-D derivative-of-Gaussian (DtG), are extended to the 3-D domain to coincide with the integrated spatial–spectral organization of HSI. Thus, the 3-D Sobel–Gabor gradient (3DSGG) and 3-D derivative-of-Gaussian-Gabor (3DDGG) features are achieved. Second, a 3-D domain adaptation method is implemented to jointly exploit the second- and fourth-order statistical descriptors in the spatial–spectral dimensions, which could effectively reduce domain shifts and thus achieve improved domain adaptation. Third, all the extracted domain-adapted feature modules are collaboratively classified by extreme learning machine (ELM), and the probability-like outputs of every ELM classifier are combined together to accomplish the classification task. Four hyperspectral data sets that each contains two scenes, i.e., Pavia, Shanghai–Hangzhou, Indiana, and Houston, are tested in the experiments. When only ten labeled samples per class are used in the target domain, the classification accuracies on four hyperspectral data sets achieved by our GF-3DDA approach are 93.31%, 84.35%, 69.32%, and 80.06%, respectively.

Improved Classification Accuracy for Diagnosing the Early Stage of Parkinson’s Disease Using Alpha Stable Distribution

Early diagnosis of the neurodegenerative disorder Parkinson’s disease (PD) and Scan without Evidence of Dopaminergic Deficit (SWEDD) is essential for effective patient management in neurodisorders, as both have the same clinical characteristics. The present work intends to propose an efficient method for analyzing volume rendering Single-Photon Emission Computed Tomography (SPECT) image slices, using Alpha stable distribution-based intensity normalization techniques for discriminating early PD from Healthy Control (HC) and SWEDD. The Volume rendering image (VRI) slices of early PD, HC and SWEDD are chosen from the database called Parkinson’s Progression Markers Initiative (PPMI). The alpha stable distribution technique is adapted to normalize the intensity values outside the striatum of the VRI in order to keep up the uniform intensity values throughout the database images. The shape features and image surface-related features are taken out from the VRI slices of the three different groups. The most optimized feature set is computed based on its consistency by Genetic Algorithm (GA). The computed optimized features of VRI slices show a remarkable performance in detecting the early stage of PD. The Extreme learning machine (ELM) classifier with Radial Basis Function (RBF) kernel shows a better performance accuracy of 99.12% than Support vector machine (SVM). Performance measures of the classifiers have ensured the validity of the experiments. Thus, the proposed method is advantageous to the neurologist in the early diagnosis of PD.

Minimum class variance class-specific extreme learning machine for imbalanced classification

Imbalanced problems occur in real-world applications when the number of majority instances far exceeds the number of minority instances. Traditional extreme learning machine (ELM) classifier becomes biased towards the majority class due to imbalanced learning. To handle this inherent drawback, several modifications of ELM have been proposed such as weighted ELM (WELM), variances-constrained WELM (VW-ELM) to tackle the class imbalance problem effectively. One of our recent works class-specific ELM (CSELM) employs class-specific regularization and has been shown to outperform WELM for imbalanced learning. Motivated by CSELM, this work proposes a minimum class variance class-specific extreme learning machine (MCVCSELM), a variant of CSELM for tackling binary class imbalance problems more effectively. MCVCSELM uses the advantages of both the minimum class variance and the class-specific regularization. The proposed work also has lower computational complexity compared to WELM and VW-ELM. In class-specific cost regulation ELM (CCR-ELM), the calculation of the regularization parameters does not consider class distribution and class overlap. However, the performance of the CCR-ELM is comparable to ELM. MCVCSELM utilizes a class-specific regularization parameter whose value is decided by using the class proportion. The experimental results on 38 binary class datasets with different imbalanced ratios demonstrate that the proposed algorithm outperforms several state-of-the-art methods for imbalanced learning.

Ecuadorian regulatory traffic sign detection by using HOG features and ELM classifier

This article presents an algorithm for Ecuadorian regulatory traffic signs detection, under extreme lighting conditions during the day. The method is composed of the following modules, i) video stabilization to reduce vertical oscilation, ii) a method to for obtaining regions of interest (ROIs) based on color information and geometric restrictions, iii) a two-stage multi classification algorithm, based on Extreme Learning Machine (ELM) and HOG descriptor, to classify by form and content. A database of Ecuadors regulatory traffic signs has also been created. It has more than 47,000 images of Ecuadorian road signs divided into 16 classes. The experimental results, from the recognition module, generate an accuracy of 99,85 %, and a sensitivity of 99,78 % in the first stage, and an accuracy of 96,71 % and a sensitivity of 94,16 % in the second stage. In addition, its robustness was compared with two other classifiers, in order to choose the one with the best performance in terms of accuracy and low computational cost. This system works at 24 frames per second, and it was tested in real driving conditions, from 6:00 a.m. until 7:00 p.m., on the streets of several cities and highways of Ecuador.

Fusing dual‐tree quaternion wavelet transform and local mesh based features for grading of diabetic retinopathy using extreme learning machine classifier

AbstractDiabetic retinopathy (DR) is one of the most frequent microvascular complications of diabetes mellitus, which damages micro‐ and macrovascular systems. Hence, early detection and grading are important for its effective treatment. This study presents a comprehensive micro‐macro feature extraction algorithm for the grading of DR using retinal images. The method employed is a mutliresolutional microtechnique, based on dual‐tree quaternion wavelet transform fused with local mesh patterns. Since the pixel level model is unable to capture macrolevel features and is difficult for efficient decision‐making, this process additionally proposes a macrolevel feature extraction technique based on feature gradients. The macrolevel descriptor considers a group of pixels to find feature gradients of macrolevel lesions. Feature extracted using the micro‐macro approaches is summarized, and a comparison study using three machine learning classifiers is considered. Performance of the classifiers is determined by conducting a 10‐fold cross‐validation procedure. Among the classifiers, the highest classification accuracy of 93.2% is exhibited by radial basis function kernel extreme learning machine. Simulation results illustrate the adaptability and competency of the novel micro‐macro approach with high accuracy and sensitivity. The promising result assures the excellence of the proposed method for automated DR grading over other state‐of‐the‐art techniques.

A Novel Recursive Gene Selection Method Based on Least Square Kernel Extreme Learning Machine.

This paper presents a recursive feature elimination (RFE) mechanism to select the most informative genes with a least square kernel extreme learning machine (LSKELM) classifier. Describing the generalization ability of LSKELM in a way that is related to small norm of weights, we propose a ranking criterion to evaluate the importance of genes by the norm of weights obtained by LSKELM. The proposed method is called LSKELM-RFE which first employs the original genes to build a LSKELM classifier, and then ranks the genes according to their importance given by the norm of output weights of LSKELM and finally removes a "least important" gene. Benefiting from the random mapping mechanism of the extreme learning machine (ELM) kernel, there are no parameter of LSKELM-RFE needs to be manually tuned. A comparative study among our proposed algorithm and other two famous RFE algorithms has shown that LSKELM-RFE outperforms other RFE algorithms in both the computational cost and generalization ability.

Extreme Learning Machine based Differentiation of Pulmonary Tuberculosis in Chest Radiographs using Integrated Local Feature Descriptors

Background and ObjectiveComputer aided diagnostics of Pulmonary Tuberculosis in chest radiographs relies on the differentiation of subtle and non-specific alterations in the images. In this study, an attempt has been made to identify and classify Tuberculosis conditions from healthy subjects in chest radiographs using integrated local feature descriptors and variants of extreme learning machine. MethodsLung fields in the chest images are segmented using Reaction Diffusion Level Set method. Local feature descriptors such as Median Robust Extended Local Binary Patterns and Gradient Local Ternary Patterns are extracted. Extreme Learning Machine (ELM) and Online Sequential ELM (OSELM) classifiers are employed to identify Tuberculosis conditions and, their performances are analysed using standard metrics. ResultsResults show that the adopted segmentation method is able to delineate lung fields in both healthy and Tuberculosis images. Extracted features are statistically significant even in images with inter and intra subject variability. Sigmoid activation function yields accuracy and sensitivity values greater than 98% for both the classifiers. Highest sensitivity is observed with OSELM for minimal significant features in detecting Tuberculosis images. ConclusionAs ELM based method is able to differentiate the subtle changes in inter and intra subject variations of chest X-ray images, the proposed methodology seems to be useful for computer-based detection of Pulmonary Tuberculosis.