Sputum Images Research Articles

Xception Taylor Cascade Neuro Network based infection level identification of tuberculosis using sputum images

Currently, one amongst most primary health problems and an enormously transmittable disease is Tuberculosis (TB). This disease spreads all over the world and is commonly developed by Mycobacterium TB (MTB). TB causes fatality if it is not identified at earlier stages. Thus, accurate and effectual model is necessary for detecting infection level of TB. Here, Xception Taylor Cascade Neuro Network (Xception T-Cascade NNet) is presented for infection level identification of TB utilizing sputum images. Firstly, input sputum image acquired from certain database is pre-processed by denoising and histogram equalization utilizing contrast limited adaptive histogram equalization (CLAHE). SegNet is utilized for bacilli segmentation and it is tuned by White Shark Optimizer (WSO). Thereafter, suitable features such as designed discrete cosine transform (DCT) with angled local directional pattern (ALDP), statistical features, shape features and gray-level co-occurrence model (GLCM) texture features are extracted for further processing. Lastly, infection level identification of TB is conducted by Xception T-Cascade NNet. However, Xception T-Cascade NNet is an integration of Xception with Cascade Neuro-Fuzzy Network (NFN) by Taylor concept. In addition, Xception T-Cascade NNet achieved 88.5% of accuracy, 90.8% of true negative rate (TNR) and 89.4% of true positive rate (TPR) and as well as minimal false negative rate (FNR) of 0.092 and false positive rate (FPR) of 0.106.

Hybrid Optimized Deep Learning-Based Bacilli Segmentation and Infection-Level Identification of Tuberculosis Using Sputum Images

Presently, one of the foremost health issues and an extremely transferrable disease is Tuberculosis which is spreading worldwide. Tuberculosis is generally produced by mycobacterium tuberculosis and can cause death if it is not detected at premature stages. Therefore, a precise and efficient approach is essential for the identification of tuberculosis. The physical analysis of sputum smears through fluorescence microscopy for the detection process and also the treatment process is a subjective and difficult task. Thus, the hybrid optimization-driven deep learning approach is designed in this paper for infection-level identification of severity prediction of tuberculosis. Here, a sputum image is considered for performing tuberculosis detection and it is acquired from a database. For executing bacilli segmentation, the Deep Contour Aware Network (DCAN) model is used. In addition, DCAN is trained by the introduced Fractional Artificial Hummingbird Algorithm (FAHA). Additionally, the LeNet scheme is applied to detect the infection level and severity detection. The LeNet is also trained using Coot-FAHA thereby; the system performance is highly increased. The devised optimization-enabled deep learning method achieved improved performance with precision, F1-score, and recall of 0.9326, 0.9386, and 0.9163, respectively.

A Enhanced Approach for Identification of Tuberculosis for Chest X-Ray Image using Machine Learning

Lungs are the primary organs affected by the infectious illness tuberculosis (TB). Mycobacterium tuberculosis, often known as Mtb, is the bacterium that causes tuberculosis. When a person speaks, spits, coughs, or breathes in, active tuberculosis can quickly spread through the air. Early TB diagnosis takes some time. Early detection of the bacilli allows for straightforward therapy. Chest X-ray images, sputum images, computer-assisted identification, feature selection, neural networks, and active contour technologies are used to diagnose human tuberculosis. Even when several approaches are used in conjunction, a more accurate early TB diagnosis can still be made. Worldwide, this leads to a large number of fatalities. An efficient technology known as the Deep Learning approach is used to diagnose tuberculosis microorganisms. Because this technology outperforms the present methods for early TB diagnosis, Despite the fact that death cannot be prevented, it is possible to lessen its effects.

Enhanced fuzzy Gaussian networks for sputum image based Mycobacterium detection

ABSTRACT The primary step involved in the diagnosis of Mycobacterium tuberculosis is the Sputum Smear Microscopy. The limitations of manual detection can be avoided with an automated technique which is carried out in this study. This paper uses the Enhanced Fuzzy Gaussian Networks (EFGN) for the detection of TB, by integrating the Gaussian model with the fuzzy and the neural network. In EFGN, the classified output obtained from the enhanced fuzzy and neural network is combined together depending on the Gaussian mixture model. The sputum smear microscopic image acts as the input, to which the process of thresholding is imposed to get the segmented result. Local Gradient Pattern (LGP), length, density, area, and histogram features are utilized to classify and count the bacilli objects using EFGN classifier. The performance is estimated based on the factors, such as Segmentation Accuracy (SA), and Mean Squared Error (MSE).

Comparison of Mycobacterium Tuberculosis Image Detection Accuracy Using CNN and Combination CNN-KNN

Mycobacterium tuberculosis is a pathogenic bacterium that causes respiratory tract disease in the lungs, namely tuberculosis (TB). The problem is to find out the bacterial colonies when the observation is still done manually using a microscope with a magnification of 1000 times. It took a long time and was tiring for the observer's eye. Based on this background, an automatic detection system for Mycobacterium tuberculosis was designed. Mycobacterium tuberculosis image data were obtained from the Semarang City Health Center. The dataset used is 220 sputum images, which are divided into 180 training data and 40 testing data. The method used in this research is a combination of Convolutional Neural Network (CNN) and K-Nearest Neighbor (KNN). CNN is used for image feature extraction. Furthermore, the results of the CNN feature extraction are classified using the KNN. The results of the accuracy of the combination of CNN-KNN and CNN were also compared. The stages of the process are color transformation, feature extraction, and data training with CNN, then classification with KNN. The results of the classification test between CNN and the CNN-KNN combination show that the CNN-KNN combination is better. The result of CNN-KNN accuracy is 92.5%, while CNN's accuracy is 90%.

Development of a Machine learning image segmentation-based algorithm for the determination of the adequacy of Gram-stained sputum smear images

BackgroundMachine learning (ML) prepares and trains a model through supervised or unsupervised learning methods. Sputum, a respiratory tract secretion, is a common laboratory specimen that aids in diagnosing respiratory diseases, including pulmonary tuberculosis (TB). Gram stain is an easy, cost-effective stain, which may be applied to sputum smears to screen out an unsatisfactory sample. ML model may help in screening sputum smears. MethodsThis collaborative project was carried out from June 2020-July 2021. In this study, a color-based segmentation ML algorithm using K-Means clustering was developed. A library of stained sputum smears was built. The Bartletts criteria (based on neutrophil and squamous cell count) for screening and selecting satisfactory sputum smears were used. A smartphone camera was used to take several photographs of satisfactory, as well as unsatisfactory, smears. The image segmentation algorithm was applied to medical image analysis, color-segmentation of sputum images was done. The hue saturation value (HSV) color ranges were defined on a prototype image. Then, all connected pixels were identified as a single object, and morphological operations were applied. ResultsUsage of AI-driven model on the slide-image revealed the slide adequacy as the cell count was acceptable based on Bartlett’s criteria. Both the manual cell counts (Range: 126–203 neutrophils, 14–47 squamous cells) and the model counts (Range: 117–242 neutrophils, 14–37 squamous cells) are within acceptable limits. ConclusionThe use of a model to screen a large number of sputum slides may be a boon in resource-limited settings where trained microscopists may not be easily available.

Identification the number of Mycobacterium tuberculosis based on sputum image using local linear estimator

Infectious disease caused by infection of Mycobacterium tuberculosis is called tuberculosis (TB). A common method in detecting TB is by identifying number of mycobacterium TB in sputum manually. Unfortunately, manually calculation by pathologists take a relatively long time. Previous researches on TB bacteria were still limited to detect the absence or presence of mycobacterium TB in images of sputum. This research aims are identifying number of mycobacterium TB and determining accuracy of classification TB severity by approaching nonparametric Poisson regression model and applying an estimator namely local linear. Steps include processing of image, reducing of dimension by applying partial least square and discrete wavelet transformation, and then identifying the number of mycobacterium TB by using the proposed model approach. In this research, we get deviance values of 28.410 for nonparametric and 93.029 for parametric approaches and the average of classification accuracy values for 4 iterations of 92.75% for nonparametric and 85.5% for parametric approaches. Thus, for identifying many of mycobacterium TB met in images of sputum and classifying of TB severity, the proposed identifying method gives higher accuracy and shorter time in identifying number of mycobacterium TB than parametric linear regression method.

Mycobacterium tuberculosis images classification based on combining of convolutional neural network and support vector machine

Mycobacterium Tuberculosis (TB bacteria) is a rod-shaped bacterium with a very small size. This bacterium can cause lung disease known as Tuberculosis. These TB bacteria can be seen at least by using a conventional microscope with magnification 1000 times. Images that have been seen in a microscope will be further processed by digital image processing. The data used in this study were 100 captions. Based on the color of the TB bacteria, a sputum image is detected and then cropping is done. Total data on TB bacteria and non-bacterial crops in automatic cropping were 1266 crops consisting of 633 TB bacteria and 633 non-TB bacteria. The size of the TB bacteria and open TB bacteria have different pixel sizes, so it needs to resize the image with a size of 50 x 50 pixels. There are several Convolutional Neural Networks (CNN) architectures that have been tried in solving classification problems among them LeNet, AlexNet, ZFNet, GoogleNet, VGGNet and ResNet. In other studies, the accuracy was 95.05% using the Inception V3 method. In the case of this classification of TB bacteria, researchers proposed the ResNet-101 architecture with 224x224x3 pixel input data specifications, 347layer and 1000 full connected layer (fc1000). As for the classification, researchers used the Support Vector Machine (SVM) to determine TB bacteria or not TB bacteria. The results of this study resulted in an accuracy of 97.6%, 97.9% precision, 97.4% recall and F1 score 97.6%.

An Automated Intelligent Identification and Counting System Procedure for Tuberculosis

Tuberculosis (TB) is an infectious disease caused by Mycobacterium Tuberculosis or TB Bacilli. Currently, the classification of TB bacilli is carried out by microbiologist by using Ziehl-Nielsen (ZN) stained smear sputum slide under a light microscopy. However, the manual evaluation is time-consuming and lead to slow decision. Furthermore, the sensitivity is less due to incline of human error which lead to inaccurate conclusion. Therefore, this study proposes an intelligence identification and counting system to detect the presence of TB bacilli in the ZN-stained smear sputum image. This system is designed to identify the presence of TB bacilli and count the number of TB bacilli by applying digital image processing and artificial intelligence techniques. In image acquisition, there are 70 samples images of ZN-stained smear sputum image were collected from Hospital Universiti Sains Malaysia (HUSM) Kubang Kerian, Kota Bharu, Kelantan, Malaysia. The image processing technique consists of contrast enhancement, segmentation, and feature extraction. The contrast of original image was enhanced by the combination of global enhancement, local enhancement and Contrast Limited Adaptive Histogram Equalization (CLAHE). Then, the enhanced image was segmented using color thresholding and the features were extracted consists of on 18 colour features, 15 shape features and 5 texture features. Afterward, the features underwent feature selection to select the relevant features by using Neighborhood Component Analysis (NCA) and ReliefF Analysis. The study showed that there are relevant features were chosen by ReliefF at feature weight more than 0.004 including (8 colour features, 11 shape feature and 3 texture features) for improving the performance and accuracy of Multilayer Perceptron (MLP) trained by Scaled Conjugate Gradient (SCG). For classification process, MLP, k-Nearest Neighborhood (k-NN) and Support Vector Machine (SVM) are used with 6 folds cross-validation. It was found that MLP has the highest of accuracy, sensitivity and specificity with 93.8%, 93.4% and 94.1% respectively.

Automated identification of mycobacterium bacillus from sputum images for tuberculosis diagnosis

Tuberculosis is a terrible, transferrable disease instigated by infection with Mycobacterium tuberculosis bacillus. A common diagnosis of this infection is the microscopic examination of sputum smears of affected persons. This paper presents an automatic system for detecting the bacillus present in the stained microscopic images of sputum which eases the job of laboratory technician with manual detection. In this work, the Channel Area Thresholding algorithm is proposed for segmentation of the bacterial image. The intensity-based local bacilli features are extracted using location-oriented histogram and speeded up robust feature algorithm. The exact classification of the bacilli objects after segmentation is carried out using deep learning neural networks. Experimental results show that the proposed work had the classification accuracy of about 97.55 on sputum image dataset.

Image Enhancement Sputum Containing Mycobacterium Tuberculosis Using A Spatial Domain Filter

Image enhancement sputum is needed to identify bacteria mycobacterium tuberculosis (TB). The number of TB bacteria in sputum images determines the severity of tuberculosis sufferers. In this paper, we study image enhancement sputum techniques by using spatial domain filter-based methods such as median filtering, Gaussian filtering, adaptive noise-removal filtering and bilateral filtering. These filtering techniques are used to overcome the problems when taking sputum images such as adjusting the focus of the lens, lighting and dirt that stick to the lens and on the slide glass. The obtained results for 100 data sputum images from this study are average means square error (MSE) of median filtering, Gaussian filtering, adaptive noise-removal filtering and bilateral filtering, i.e., 30.68, 17.10, 18.92 and 26.28, respectively. Also, average peak signal-to-noise ratio (PSNR) of them are 33.70 dB, 35.91 dB, 35.59 dB and 34.01 dB, respectively. The avarage computational time are 0.09 sec, 0.18 sec, 0.38 sec and 134 sec respectively.

Segmentation of Tuberculosis Bacilli Using Watershed Transformation and Fuzzy C-Means

The easily transmitted Tuberculosis (TB) disease is attributed to the fact that Mycobacterium Tuberculosis (MTB) bacteria/viruses can be transmitted through the air. One of the methods to screen the TB disease is by reading sputum slides. Sputum slides are colored sputum samples of TB patients placed on microscopic slides. However, TB disease microscopic analysis has some limitations since it requires high accuracy reading and well-trained health personnel to avoid errors in the process of interpretation. Furthermore, the number of TB patients in the Primary Health Care (PHC) and the process of manual calculation of bacteria in a field of view often complicate the decision-making in the screening process conducted by the medical staffs. In this paper, the researchers propose the use of Watershed Transformation and Fuzzy C-Means combination to help solve the problem. The researchers collect the photo shooting of three PHC in Indonesia with 55 images of sputum from different TB patients. The assessed results of the proposed method are compared with the opinions of three Microbiology doctors. The comparison shows Cohen’s Kappa Coefficient value of 0.838. It suggests that the proposed method can detect Acid Resistant Bacteria (ARB) although it needs some improvement to achieve higher accuracy.

Fractional crow search‐based support vector neural network for patient classification and severity analysis of tuberculosis

The world is chasing towards the automation in the severity analysis and classification of the patients based on the severity of tuberculosis (TB). The automatic classification is very much useful for developing countries that are struggling to reduce the fatality rate of the persons suffering from TB as it is a top standing infectious disease. Thus, the automatic classification of the TB patients using the sputum images with the proposed fractional crow search-based support vector neural network is presented. The proposed classification method is the integration of the fractional theory in the crow search algorithm that increases the computational speed and reduces the cost and time spent on analysing the test samples. The importance of the proposed method is that it requires minimum manual power and hence, the inaccuracies are reduced. The experimentation performed using the Ziehl-Neelsen sputum smear microscopy image database proves that the proposed classifier is highly accurate and offered an improved performance in terms of accuracy rate, true positive rate, and false-positive rate.

FHDT: Fuzzy and Hyco-entropy-based Decision Tree Classifier for Tuberculosis Diagnosis from Sputum Images

Tuberculosis (TB) is one of the infectious diseases spread by the infectious agent Mycobacterium tuberculosis. Sputum smear microscopy is the primary tool used for the diagnosis of pulmonary TB, but has its limitations such as low sensitivity and large observation time. Hence, an automated technique is preferred for the diagnosis of TB. This paper develops a technique for TB diagnosis based on the bacilli count by proposing Fuzzy and Hyco-entropy-based Decision Tree (FHDT) classifier using sputum smear microscopic images. The proposed technique involves three steps: segmentation, feature extraction and classification. Initially, the input sputum smear microscopic image is subjected to a colour space transformation, for which a thresholding is applied to obtain the segmented result. Important features such as length, density, area and few histogram features are extracted for FHDT-based classification that classifies the segments into few-bacilli, non-bacilli and overlapping bacilli. An entropy function, called hyco-entropy, is designed for the optimal selection of feature. For further analysis of classification, that is, to count the number in the overlapping bacilli, the fuzzy classifier is adopted. FHDT classifier is evaluated in terms of Segmentation Accuracy (SA), Mean Squared Error (MSE) and Missing Count (MC) using microscopic images taken from ZNSM-iDB, where it can attain maximum mean SA of 0.954 and mean MC of 2.4.

Automatic Methods for Mycobacterium Detection on Stained Sputum Smear Images: a Survey

Mycobacterium tuberculosis (MTB) is one of the leading causes of adult morbidity and mortality worldwide, especially in developing countries like India. MTB is caused by the mycobacterium bacillus which mainly generates infections on lung region but sometimes affects other parts also. Sputum smear microscopy is the widely used tool for MTB diagnosis in most of the developing countries since it is less costly. Manual detection of bacilli from stained sputum images are time consuming since it may take 15 minutes per slide for detection, reducing number of slides which affects the accuracy of the output. Thus computer aided automatic methods provide obviously an optimum solution in disease diagnosis within less time and without highly experienced laboratory experts. There are so many papers published for automatic tuberculosis diagnosis from microscopic sputum images so far. This paper provides a survey of those published papers from the year 2002 to 2016. Thus it provides an overview of available methods and its accuracy and hence it will be useful for researchers and practitioners working in the field of automation of sputum smear microscopy.

Study of Colour Model for Segmenting Mycobacterium Tuberculosis in Sputum Images

One of method to diagnose Tuberculosis (TB) disease is sputum test. The presence and number of Mycobacterium tuberculosis (MTB) in sputum are identified. The presence of MTB can be seen under light microscope. Before investigating through stained light microscope, the sputum samples are stained using Ziehl–Neelsen (ZN) stain technique. Because there is no standard procedure in staining, the appearance of sputum samples may vary either in background colour or contrast level. It increases the difficulty in segmentation stage of automatic MTB identification. Thus, this study investigated the colour models to look for colour channels of colour model that can segment MTB well in different stained conditions. The colour models will be investigated are each channel in RGB, HSV, CIELAB, YCbCr, and C-Y colour model and the clustering algorithm used is k-Means. The sputum image dataset used in this study is obtained from community health clinic in a district in Indonesia. The size of each image was set to 1600x1200 pixels which is having variation in number of MTB, background colour, and contrast level. The experiment result indicates that in all image conditions, blue, hue, Cr, and Ry colour channel can be used to segment MTB in one cluster well.

Сегментация микроскопических изображений мокроты, окрашенной по методу циля-нильсена, с использованием искусственных нейронных сетей

Сегментация микроскопических изображений мокроты, окрашенной по методу циля-нильсена, с использованием искусственных нейронных сетей

Colour segmentation of multi variants tuberculosis sputum images using self organizing map

Lung tuberculosis detection is still identified from Ziehl-Neelsen sputum smear images in low and middle countries. The clinicians decide the grade of this disease by counting manually the amount of tuberculosis bacilli. It is very tedious for clinicians with a lot number of patient and without standardization for sputum staining. The tuberculosis sputum images have multi variant characterizations in colour, because of no standardization in staining. The sputum has more variants colour and they are difficult to be identified. For helping the clinicians, this research examined the Self Organizing Map method for colouring image segmentation in sputum images based on colour clustering. This method has better performance than k-means clustering which also tried in this research. The Self Organizing Map could segment the sputum images with y good result and cluster the colours adaptively.

Computer Aided Diagnosis System for Early Lung Cancer Detection

Lung cancer continues to rank as the leading cause of cancer deaths worldwide. One of the most promising techniques for early detection of cancerous cells relies on sputum cell analysis. This was the motivation behind the design and the development of a new computer aided diagnosis (CAD) system for early detection of lung cancer based on the analysis of sputum color images. The proposed CAD system encompasses four main processing steps. First is the preprocessing step which utilizes a Bayesian classification method using histogram analysis. Then, in the second step, mean shift segmentation is applied to segment the nuclei from the cytoplasm. The third step is the feature analysis. In this step, geometric and chromatic features are extracted from the nucleus region. These features are used in the diagnostic process of the sputum images. Finally, the diagnosis is completed using an artificial neural network and support vector machine (SVM) for classifying the cells into benign or malignant. The performance of the system was analyzed based on different criteria such as sensitivity, specificity and accuracy. The evaluation was carried out using Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate the efficiency of the SVM classifier over other classifiers, with 97% sensitivity and accuracy as well as a significant reduction in the number of false positive and false negative rates.

Image Recognition and Counting for the Bacilli Cell Based on the Microscopic Image

In order to automatically detect bacilli in sputum image with microscopy, an intelligent recognition method based on machine vision is presented. Firstly, a novel background filter was designed based on the single layer perceptron to realize object segmentation from background. After eliminating the short twig and small area noise, the suspicious goals and the image noise are separated. In the feature extraction, besides the base features of single bacillus two important features are presented to solve the difficult problem of identification and counting for the overlapping and winding bacilli cells. Finally, an EBP neural network classifier is designed for the accurate identification and counting of the bacilli cells. Experimental results verified the effectiveness of the presented method.