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

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

Pulmonary tuberculosis diagnosis, differentiation and disease management : a review of radiomics applications

Pulmonary tuberculosis is a worldwide epidemic that can only be fought effectively with early and accurate diagnosis and proper disease management. The means of diagnosis and disease management should be easily accessible, cost effective and be readily available in the high tuberculosis burdened countries where it is most needed. Fortunately, the fast development of computer science in recent years has ensured that medical images can accurately be quantified. Radiomics is one such tool that can be used to quantify medical images. This review article focuses on the literature currently available on the application of radiomics explicitly for the purpose of diagnosis, differentiation from other pulmonary diseases and disease management of pulmonary tuberculosis. Despite using a formal search strategy, only five articles could be found on the application of radiomics to pulmonary tuberculosis. In all five articles reviewed, radiomic feature extraction was successfully used to quantify digital medical images for the purpose of comparing, or differentiating, pulmonary tuberculosis from other pulmonary diseases. This demonstrates that the use of radiomics for the purpose of tuberculosis disease management and diagnosis remains a valuable data mining opportunity not yet realised.https://sciendo.com/journal/PJMPEam2022Nuclear Medicin

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

Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases

Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI

Diseases of the Chest, Breast, Heart and Vessels 2019-2022

This open access book focuses on diagnostic and interventional imaging of the chest, breast, heart, and vessels. It consists of a remarkable collection of contributions authored by internationally respected experts, featuring the most recent diagnostic developments and technological advances with a highly didactical approach. The chapters are disease-oriented and cover all the relevant imaging modalities, including standard radiography, CT, nuclear medicine with PET, ultrasound and magnetic resonance imaging, as well as imaging-guided interventions. As such, it presents a comprehensive review of current knowledge on imaging of the heart and chest, as well as thoracic interventions and a selection of "hot topics". The book is intended for radiologists, however, it is also of interest to clinicians in oncology, cardiology, and pulmonology

Diseases of the Chest, Breast, Heart and Vessels 2019-2022

This open access book focuses on diagnostic and interventional imaging of the chest, breast, heart, and vessels. It consists of a remarkable collection of contributions authored by internationally respected experts, featuring the most recent diagnostic developments and technological advances with a highly didactical approach. The chapters are disease-oriented and cover all the relevant imaging modalities, including standard radiography, CT, nuclear medicine with PET, ultrasound and magnetic resonance imaging, as well as imaging-guided interventions. As such, it presents a comprehensive review of current knowledge on imaging of the heart and chest, as well as thoracic interventions and a selection of "hot topics". The book is intended for radiologists, however, it is also of interest to clinicians in oncology, cardiology, and pulmonology

The Effectiveness of Transfer Learning Systems on Medical Images

Deep neural networks have revolutionized the performances of many machine learning tasks such as medical image classification and segmentation. Current deep learning (DL) algorithms, specifically convolutional neural networks are increasingly becoming the methodological choice for most medical image analysis. However, training these deep neural networks requires high computational resources and very large amounts of labeled data which is often expensive and laborious. Meanwhile, recent studies have shown the transfer learning (TL) paradigm as an attractive choice in providing promising solutions to challenges of shortage in the availability of labeled medical images. Accordingly, TL enables us to leverage the knowledge learned from related data to solve a new problem. The objective of this dissertation is to examine the effectiveness of TL systems on medical images. First, a comprehensive systematic literature review was performed to provide an up-to-date status of TL systems on medical images. Specifically, we proposed a novel conceptual framework to organize the review. Second, a novel DL network was pretrained on natural images and utilized to evaluate the effectiveness of TL on a very large medical image dataset, specifically Chest X-rays images. Lastly, domain adaptation using an autoencoder was evaluated on the medical image dataset and the results confirmed the effectiveness of TL through fine-tuning strategies. We make several contributions to TL systems on medical image analysis: Firstly, we present a novel survey of TL on medical images and propose a new conceptual framework to organize the findings. Secondly, we propose a novel DL architecture to improve learned representations of medical images while mitigating the problem of vanishing gradients. Additionally, we identified the optimal cut-off layer (OCL) that provided the best model performance. We found that the higher layers in the proposed deep model give a better feature representation of our medical image task. Finally, we analyzed the effect of domain adaptation by fine-tuning an autoencoder on our medical images and provide theoretical contributions on the application of the transductive TL approach. The contributions herein reveal several research gaps to motivate future research and contribute to the body of literature in this active research area of TL systems on medical image analysis

MENTORING DEEP LEARNING MODELS FOR MASS SCREENING WITH LIMITED DATA

Deep Learning (DL) has an extensively rich state-of-the-art literature in medical imaging analysis. However, it requires large amount of data to begin training. This limits its usage in tackling future epidemics, as one might need to wait for months and even years to collect fully annotated data, raising a fundamental question: is it possible to deploy AI-driven tool earlier in epidemics to mass screen the infected cases? For such a context, human/Expert in the loop Machine Learning (ML), or Active Learning (AL), becomes imperative enabling machines to commence learning from the first day with minimum available labeled dataset. In an unsupervised learning, we develop pretrained DL models that autonomously refine themselves through iterative learning, with human experts intervening only when the model misclassifies and for a limited amount of data. We introduce a new terminology for this process, calling it mentoring. We validated this concept in the context of Covid-19 in three distinct datasets: Chest X-rays, Computed Tomography (CT) scans, and cough sounds, each consisting of 1364, 4714, and 10,000 images, respectively. The framework classifies the deep features of the data into two clusters (0/1: Covid-19/non-Covid-19). Our main goal is to strongly emphasize the potential use of AL in predicting diseases during future epidemics. With this framework, we achieved the AUC scores of 0.76, 0.99, and 0.94 on cough sound, Chest X-rays, and CT scans dataset using only 40%, 33%, and 30% of the annotated dataset, respectively. For reproducibility, the link of implementation is provided: https://github.com/2ailab/Active-Learning