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

Evaluation of the utility of specific CXR features for diagnosis of pulmonary tuberculosis in young children using multiple readers

Includes bibliographical referencesINTRODUCTION: The diagnosis of childhood pulmonary tuberculosis (TB) can be notoriously difficult. The chest X-ray (CXR) is a significant diagnostic resource in the detection of PTB in children. However, non-specific radiological features combined with variable inter-observer assessment s contribute to diagnostic uncertainty. The CXR would be of most value when used specifically to evaluate those features of childhood TB that it shows best and where expert observers agree, namely those signs indicating lymphadenopathy. AIM: To identify simple and reliable CXR features of primary TB in children by determining signs and anatomical sites of best observer agreement. METHOD: This is a retrospective descriptive study within a clinical trial performed by the South African TB Vaccine Initiative (SATVI). Healthy BCG-vaccinated newborn infants in a high TB prevalence rural area in Worcester, near Cape Town, South Africa, were followed for a minimum of two years for possible incident al pulmonary TB. Three independent, blinded, expert paediatric radiologists reported the resultant CXR images using a standardised data collection tick sheet, on which the specific anatomical sites and signs of pathology consistent with pulmonary TB were recorded. The first 200 original data collection tick sheets were sampled and recorded in a pre-compiled data spreadsheet for our study. The sampled data were t hen analysed using kappa statistics. RESULTS: The overall combined agreement for airway compression (by presumed lymphadenopathy) was 0.5%. Five % of the CXR's had soft tissue densities reflecting lymphadenopathy on the frontal view and 5% on the lateral view. The most common site reflecting lymphadenopathy through airway narrowing or displacement was the left main bronchus. The hilar region (kappa 0.27) on the frontal CXR and behind bronchus intermedius (kappa 0.18) on the lateral were the most common sites of soft tissue densities reflecting lymphadenopathy. There were no positive findings for cavitation or pleural effusion. The overall decisions reflecting PTB (lymphadenopathy or miliary) by each individual reader were 27.6% by Reader 1, 8.5% by Reader 2 and 24.6 % by Reader 3. Abnormal findings not specific for PTB were found in 3.5 % by Reader 1, 10.5% by Reader 2 and 3.5% by Reader 3.68. 3 % of the radiographs were reported as normal by Reader 1, 81.9% by Reader 2 and 66.8 % by Reader 3. Only 5% of the radiographs were found to be unreadable by one reader. The overall agreement of all three readers on PTB was 14.6 % and for normal CXR 49.2%. CONCLUSIONS: The fair degree of agreement amongst expert readers suggests that the CXR alone is not a reliable tool for detecting pulmonary TB and should be utilised in conjunction with the clinical features and/or skin tests and blood results. Soft tissue masses rather than airway compression are a more reliable sign for lymphadenopathy, with the most agreed upon sites on the frontal projection for soft tissue mass detection being the right hilar region, followed by the left hilum. Unfortunately, this study could not confirm the usefulness of the CXR in subcategorising PTB into severe and non-severe groups due to the absence of any positive features for severe PTB in the selected sample. The use of prescribed tick-sheets with specified features for detecting lymphadenopathy did not have the expected impact of promoting interobserver consensus of CXR findings in children in terms of detection of TB. The absence of a credible reference standard for lymphadenopathy remains a significant limitation

Tuberculosis Disease Detection through CXR Images based on Deep Neural Network Approach

Tuberculosis (TB) is a disease that, if left untreated for an extended period of time, can ultimately be fatal. Early TB detection can be aided by using a deep learning ensemble. In previous work, ensemble classifiers were only trained on images that shared similar characteristics. It is necessary for an ensemble to produce a diverse set of errors in order for it to be useful; this can be accomplished by making use of a number of different classifiers and/or features. In light of this, a brand-new framework has been constructed in this study for the purpose of segmenting and identifying TB in human Chest X-ray. It was determined that searching traditional web databases for chest X-ray was necessary. At this point, we pass the photos that we have collected over to Swin ResUnet3 so that they may be segmented. After the segmented chest X-ray have been provided to it, the Multi-scale Attention-based Densenet with Extreme Learning Machine (MAD-ELM) model will be applied in the detection stage in order to effectively diagnose tuberculosis from human chest X-ray. This will be done in order to maximize efficiency. Because it increased the variety of errors made by the basic classifiers, the supplied variation of the approach that was proposed was able to detect tuberculosis more effectively. The proposed ensemble method produced results with an accuracy of 94.2 percent, which are comparable to those obtained by past efforts

Development of a simple reliable radiographic scoring system to aid the diagnosis of pulmonary tuberculosis

Rationale: Chest radiography is sometimes the only method available for investigating patients with possible pulmonary tuberculosis (PTB) with negative sputum smears. However, interpretation of chest radiographs in this context lacks specificity for PTB, is subjective and is neither standardized nor reproducible. Efforts to improve the interpretation of chest radiography are warranted. Objectives To develop a scoring system to aid the diagnosis of PTB, using features recorded with the Chest Radiograph Reading and Recording System (CRRS). METHODS: Chest radiographs of outpatients with possible PTB, recruited over 3 years at clinics in South Africa were read by two independent readers using the CRRS method. Multivariate analysis was used to identify features significantly associated with culture-positive PTB. These were weighted and used to generate a score. RESULTS: 473 patients were included in the analysis. Large upper lobe opacities, cavities, unilateral pleural effusion and adenopathy were significantly associated with PTB, had high inter-reader reliability, and received 2, 2, 1 and 2 points, respectively in the final score. Using a cut-off of 2, scores below this threshold had a high negative predictive value (91.5%, 95%CI 87.1,94.7), but low positive predictive value (49.4%, 95%CI 42.9,55.9). Among the 382 TB suspects with negative sputum smears, 229 patients had scores <2; the score correctly ruled out active PTB in 214 of these patients (NPV 93.4%; 95%CI 89.4,96.3). The score had a suboptimal negative predictive value in HIV-infected patients (NPV 86.4, 95% CI 75,94). CONCLUSIONS: The proposed scoring system is simple, and reliably ruled out active PTB in smear-negative HIV-uninfected patients, thus potentially reducing the need for further tests in high burden settings. Validation studies are now required

ResNet18 Supported Inspection of Tuberculosis in Chest Radiographs With Integrated Deep, LBP, and DWT Features

The lung is a vital organ in human physiology and disease in lung causes various health issues. The acute disease in lung is a medical emergency and hence several methods are developed and implemented to detect the lung abnormality. Tuberculosis (TB) is one of the common lung disease and premature diagnosis and treatment is necessary to cure the disease with appropriate medication. Clinical level assessment of TB is commonly performed with chest radiographs (X-ray) and the recorded images are then examined to identify TB and its harshness. This research proposes a TB detection framework using integrated optimal deep and handcrafted features. The different stages of this work include (i) X-ray collection and processing, (ii) Pretrained Deep-Learning (PDL) scheme-based feature mining, (iii) Feature extraction with Local Binary Pattern (LBP) and Discrete Wavelet Transform (DWT), (iv) Feature optimization with Firefly-Algorithm, (v) Feature ranking and serial concatenation, and (vi) Classification by means of a 5-fold cross confirmation. The result of this study validates that, the ResNet18 scheme helps to achieve a better accuracy with SoftMax (95.2%) classifier and Decision Tree Classifier (99%) with deep and concatenated features, respectively. Further, overall performance of Decision Tree is better compared to other classifiers

Pulmonary tuberculosis in the elderly : diagnostic criteria and its epidemiology in old age homes

The majority of today's elderly people were primarily infected with Mycobacterium tuberculosis at a time when no effective chemotherapeutic treatment was available. With the progressive decline in cell mediated immunity that accompanies aging, the potential to reactivate a dormant lesion, or to be re-infected increases. The latter particularly applies in areas of high density living e.g. homes for the elderly. The incidence of pulmonary tuberculosis in whites in South Africa is very similar to that in industrialized countries (approximately 16/100 000). In a survey of old age homes in East London (South Africa) involving 809 white subjects the prevalence rate was found to be 1403/100 000; clusters were found in individual homes where up to 10% of residents had tuberculosis. The age specific incidence in the community for whites was 86/100 000, and in homes for the aged the incidence in 648 elderly subjects followed for 2 years was 1080/100 000. It is concluded that the elderly living in high density accommodation constitute a high-risk group for the development of the disease. The diagnosis of pulmonary tuberculosis in the elderly may be complicated by the high prevalence of atypical radiographic changes, difficulty in obtaining sputum, and the high false-negative rate of the tuberculin skin test. Thus, the value of haematological and biochemical abnormalities in 93 elderly tuberculotics, 113 elderly non-tuberculotics and 264 young tuberculotics were investigated. The results in the elderly tuberculotic patients were: Normochromic normocytic anaemia (70%), leucocytosis (55%), thrombocytosis (33%), rapid ESR in 90%, lymphopenia (22%) and monocytopenia (37%); hyponatraemia (60%), hypokalaemia (42%) and hypoalbuminaemia (83%), serum bilirubin (20%) and alkaline phosphatase, aspartic transaminase and lactic dehydrogenase are elevated in approximately 2/3 of patients. In comparison with the younger group (mean age 48 years) with cavitating tuberculosis, the prevalence of elderly patients (with generally mild and non-cavitating disease) with elevated bilirubin, alkaline phosphatase and liver enzymes was approximately 50% higher. When the results of liver enzyme elevations in the elderly tuberculotics were compared retrospectively with elderly patients with non-tuberculotic destructive lung disease, the former group had significantly higher values. The sensitivity (76%), specificity (48%) and positive predictive value (60%) suggest that liver enzyme abnormalities may provide useful contributory data in the non-invasive diagnosis of pulmonary tuberculosis in the elderly. The chest radiographs in 93 consecutive cases of bacteriologically proven pulmonary tuberculosis showed infrequent apical involvement (7%), with the most frequent abnormality being opacification of the middle and lower zones of the lungs; half the cases had a pleural reaction. cavitation occurs in only 1/3 of patients, and was sited equally in the apical zones and in the mid and lower zones. These findings contrast with the pattern of cavitating apico-posterior disease commonly seen in reactivated tuberculosis in younger adults. A series of 21 patients was studied to compare the yield of sputum smear examination with sputum culture for M. tuberculosis. Sputum production in non-cavitating disease was found to be infrequent and unpredictable and the number of bacilli is usually scanty. Repeated Culture of sputum for M. tuberculosis is required to improve the likelihood of obtaining a positive bacteriological diagnosis. On the basis of this study at least 4 negative sputum cultures are required to exclude the disease. In a study of 10 patients the impact of 4-drug therapy on the viability of M. tubercle in their sputum was assessed. Viable tubercle bacilli continue to be excreted in patients with cavitating pulmonary disease on treatment for up to 9 weeks. It is suggested that patients with cavitating disease should probably not be allowed to return to high density accommodation for the elderly until their sputum is clear of acid fast bacilli on sputum smear examinations. The usefulness of using annual tuberculin skin reaction (Mantoux) tests as a screening procedure was evaluated in 648 residents in old age homes. The criteria for further investigation for pulmonary tuberculosis was either recent conversion to positive (reaction equal to 10 mm or more) or a year-on-year increase of greater than 12 mm, or any reaction> 20 mm. 206 subjects were identified as "possibly having the disease" and of these the diagnosis of pulmonary tuberculosis confirmed in 13 cases. 10/13 patients had Mantoux reactions of greater than 20 mm and 3/13 between 10 mm and 19 mm. As a result of this study the recommendation is made that a yearly Mantoux test is a useful screening procedure, and will help identify a population who should be further investigated with chest radiographs and sputum cultures

Tuberculosis diagnosis from pulmonary chest x-ray using deep learning.

Doctoral Degree. University of KwaZulu-Natal, Durban.Tuberculosis (TB) remains a life-threatening disease, and it is one of the leading causes of mortality in developing countries. This is due to poverty and inadequate medical resources. While treatment for TB is possible, it requires an accurate diagnosis first. Several screening tools are available, and the most reliable is Chest X-Ray (CXR), but the radiological expertise for accurately interpreting the CXR images is often lacking. Over the years, CXR has been manually examined; this process results in delayed diagnosis, is time-consuming, expensive, and is prone to misdiagnosis, which could further spread the disease among individuals. Consequently, an algorithm could increase diagnosis efficiency, improve performance, reduce the cost of manual screening and ultimately result in early/timely diagnosis. Several algorithms have been implemented to diagnose TB automatically. However, these algorithms are characterized by low accuracy and sensitivity leading to misdiagnosis. In recent years, Convolutional Neural Networks (CNN), a class of Deep Learning, has demonstrated tremendous success in object detection and image classification task. Hence, this thesis proposed an efficient Computer-Aided Diagnosis (CAD) system with high accuracy and sensitivity for TB detection and classification. The proposed model is based firstly on novel end-to-end CNN architecture, then a pre-trained Deep CNN model that is fine-tuned and employed as a features extractor from CXR. Finally, Ensemble Learning was explored to develop an Ensemble model for TB classification. The Ensemble model achieved a new stateof- the-art diagnosis accuracy of 97.44% with a 99.18% sensitivity, 96.21% specificity and 0.96% AUC. These results are comparable with state-of-the-art techniques and outperform existing TB classification models.Author's Publications listed on page iii