Diagnosis of Smear-Negative Pulmonary Tuberculosis using Ensemble Method: A Preliminary Research

Indonesia is one of 22 countries with the highest burden of Tuberculosis in the world. According to WHO’s 2015 report, Indonesia was estimated to have one million new tuberculosis (TB) cases per year. Unfortunately, only one-third of new TB cases are detected. Diagnosis of TB is difficult, especially in the case of smear-negative pulmonary tuberculosis (SNPT). The SNPT is diagnosed by TB trained doctors based on physical and laboratory examinations. This study is preliminary research that aims to determine the ensemble method with the highest level of accuracy in the diagnosis model of SNPT. This model is expected to be a reference in the development of the diagnosis of new pulmonary tuberculosis cases using input in the form of symptoms and physical examination in accordance with the guidelines for tuberculosis management in Indonesia. The proposed SNPT diagnosis model can be used as a cost-effective tool in conditions of limited resources. Data were obtained from medical records of tuberculosis patients from the Jakarta Respiratory Center. The results show that the Random Forest has the best accuracy, which is 90.59%, then Adaboost of 90.54% and Bagging of 86.91%

An investigation into advanced digital microscopic technologies

High dynamic range (HDR) imaging technology has been widely implemented in digital microscopes for taking still images of high-contrast specimens. However, capturing HDR microscopic video is much more challenging. In this dissertation, an HDR microscopic video system based on GPU accelerated computing is presented. By combining CPU and GPU computing, it is possible to build a stable HDR video system using a single off-the-shelf camera. The computing efficiency analysis shows that capturing multiple frames of different exposure intervals, aligning consecutive neighbouring frames, constructing HDR radiance map and tone mapping the radiance map for display, can all be realised by using GPU computing to accelerate the processing speed. The experimental results were presented to show the effectiveness of the system and how HDR video can reveal much more detail than conventional videos. The idea of employing HDR imaging technology in 3D surface construction has been proposed as a solution to the Shape From Focus limitation. Shape From Focus (SFF) is the most effective technique for recovering 3D object shape in optical microscopic scenes. Although numerous methods have recently been proposed, less attention has been paid to the quality of source images, which directly affects the accuracy of 3D shape recovery. One of the critical factors impacting source image quality is the high dynamic range issue, which is caused by the gap between the high dynamic ranges of the real world scenes and the low dynamic range images that the cameras capture. To overcome this issue, a novel microscopic 3D shape recovery system based on high dynamic range (HDR) imaging technique is proposed. By combining SFF and HDR, it is possible to build a robust 3D system using a single off-the-shelf camera and a traditional optical microscope. Experiments on constructing 3D shapes of difficult-to-image materials have been conducted, in terms of metal and shining plastic surfaces where conventional imaging techniques will have difficulty capturing detail, and will thus result in poor 3D reconstruction. The experimental results show the proposed HDR-based SFF 3D method yields more accurate and robust results than traditional non-HDR techniques for a variety materials. After the analysis of HDR and Shape From Focus techniques, another project about microscopy was presented, which is tuberculosis bacteria detection. Tuberculosis (TB) is an infectious disease in low- and middle-income countries. There are many tools behind physical examinations for TB detection, but the most effective method is visual examination using microscopes, in terms of fluorescent microscopy and bright field microscopy. However, the former method is on average 10% more sensitive than the latter. This project not only aims to detect tuberculosis automatically to help technicians, but also aims at the construction of a subsequent autofocus system based on the detection of tuberculosis. The focus analysis, which is the initial step of shape from the focus technique, acted on the region of tuberculosis exists, regardless of the other areas. In this case, a new TB detection method based on Random Forest using fluorescent microscopic images was presented. Experiments on three types of classifiers, in terms of Random Forest (RF), linear SVM (LinSVM), Cross-Validation SVM (CVSVM), were conducted. The experimental results indicate that the RF-based learning method for TB bacteria classification using fluorescent images achieved higher performance than the other two machine learning methods

Special issue on microscopic image processing

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Deep learning approach to bacterial colony classification

In microbiology it is diagnostically useful to recognize various genera and species of bacteria. It can be achieved using computer-aided methods, which make the recognition processes more automatic and thus significantly reduce the time necessary for the classification. Moreover, in case of diagnostic uncertainty (the misleading similarity in shape or structure of bacterial cells), such methods can minimize the risk of incorrect recognition. In this article, we apply the state of the art method for texture analysis to classify genera and species of bacteria. This method uses deep Convolutional Neural Networks to obtain image descriptors, which are then encoded and classified with Support Vector Machine or Random Forest. To evaluate this approach and to make it comparable with other approaches, we provide a new dataset of images. DIBaS dataset (Digital Image of Bacterial Species) contains 660 images with 33 different genera and species of bacteria

Comparison of K-Means Clustering and Otsu Thresholding Methods in the Detection of Tuberculosis Extra Pulmonary Bacilli in the HSV Color Space

Tuberculosis Extra Pulmonary (TBEP) is an infectious disease caused by the bacterium Mycobacterium tuberculosis and can cause death. Patients suffering from this disease must be treated quickly without waiting long. Currently, anyone who will be detected caused by this bacterium takes a long time and costs a lot. The biopsy is one of the techniques used to take the patient's lung fluid and give Ziehl Neelsen chemical dye and then observe using a microscope to determine this TBEP disease. This research aims to help detect bacteria quickly and precisely by performing computer-aided image processing by creating an application system. The technique used is to develop the segmentation method. The segmentation process is to develop a Hue Saturation Value (HSV) color space transformation technique with the K-Means and Otsu Thresholding techniques. From the results of the two methods used, it turns out that the Otsu Thresholding method can detect TBEP results with more accuracy than the K-Means method. So the method developed is beneficial in accelerating and minimizing costs for detecting TBEP

A novel algorithm for detection of tuberculosis bacilli in sputum smear fluorescence images

This work proposes an algorithm aimed at recognizing and accounting Koch bacilli in digital images of microbiological sputum samples stained with auramine, in order to determine the degree of concentration and the state of the disease (tuberculosis). The algorithm was developed with the main objective of maximizing the sensitivity and specificity of the analysis of microbiological samples (recognition and counting of bacilli) according to each preparation method (direct and diluted pellets) in order to reduce the subjectivity of the visual inspection applied by the specialist at the time of analyzing the samples. The proposed algorithm consists of a background removal, an image improvement stage based on consecutive morphological closing operations, a segmentation stage of objects of interest based on thresholdization and a classification stage based on SVM. Each algorithmic stage was developed taking into account the method of preparation of the sample to be processed, being this aspect the main contribution of the proposed work, since it was possible to achieve very satisfactory results in terms of specificity and sensitivity. In this context, sensitivity levels of 91.24% and 93.79% were obtained. Specificity levels of 90.33% and 94.85% were also achieved for direct and diluted pellet methods respectively

Automated methods for tuberculosis detection/diagnosis : a literature review

Funding: Welcome Trust Institutional Strategic Support fund of the University of St Andrews, grant code 204821/Z/16/Z.Tuberculosis (TB) is one of the leading infectious causes of death worldwide. The effective management and public health control of this disease depends on early detection and careful treatment monitoring. For many years, the microscopy-based analysis of sputum smears has been the most common method to detect and quantify Mycobacterium tuberculosis (Mtb) bacteria. Nonetheless, this form of analysis is a challenging procedure since sputum examination can only be reliably performed by trained personnel with rigorous quality control systems in place. Additionally, it is affected by subjective judgement. Furthermore, although fluorescence-based sample staining methods have made the procedure easier in recent years, the microscopic examination of sputum is a time-consuming operation. Over the past two decades, attempts have been made to automate this practice. Most approaches have focused on establishing an automated method of diagnosis, while others have centred on measuring the bacterial load or detecting and localising Mtb cells for further research on the phenotypic characteristics of their morphology. The literature has incorporated machine learning (ML) and computer vision approaches as part of the methodology to achieve these goals. In this review, we first gathered publicly available TB sputum smear microscopy image sets and analysed the disparities in these datasets. Thereafter, we analysed the most common evaluation metrics used to assess the efficacy of each method in its particular field. Finally, we generated comprehensive summaries of prior work on ML and deep learning (DL) methods for automated TB detection, including a review of their limitations.Publisher PDFPeer reviewe

Mobile Diagnosis 2.0

Mobile sensing and diagnostic capabilities are becoming extremely important for a wide range of emerging applications and fields spanning mobile health, telemedicine, point-of-care diagnostics, global health, field medicine, democratization of sensing and diagnostic tools, environmental monitoring, and citizen science, among many others. The importance of low-cost mobile technologies has been underlined during this current COVID-19 pandemic, particularly for applications such as the detection of pathogens, including bacteria and viruses, as well as for prediction and management of different diseases and disorders. This book focuses on some of these application areas and provides a timely summary of cutting-edge results and emerging technologies in these interdisciplinary fields