Implementation and evaluation of a bony structure suppression software tool for chest X-ray imaging

Includes abstract.Includes bibliographical references.This project proposed to implement a bony structure suppression tool and analyse its effects on a texture-based classification algorithm in order to assist in the analysis of chest X-ray images. The diagnosis of pulmonary tuberculosis (TB) often includes the evaluation of chest X-ray images, and the reliability of image interpretation depends upon the experience of the radiologist. Computer-aided diagnosis (CAD) may be used to increase the accuracy of diagnosis. Overlapping structures in chest X-ray images hinder the ability of lung texture analysis for CAD to detect abnormalities. This dissertation examines whether the performance of texturebased CAD tools may be improved by the suppression of bony structures, particularly of the ribs, in the chest region

High Morbidity during Treatment and Residual Pulmonary Disability in Pulmonary Tuberculosis: Under-Recognised Phenomena

BACKGROUND In pulmonary tuberculosis (PTB), morbidity during treatment and residual pulmonary disability can be under-estimated. METHODS Among adults with smear-positive PTB at an outpatient clinic in Papua, Indonesia, we assessed morbidity at baseline and during treatment, and 6-month residual disability, by measuring functional capacity (six-minute walk test [6MWT] and pulmonary function), quality of life (St George's Respiratory Questionnaire [SGRQ]) and Adverse Events ([AE]: new symptoms not present at outset). Results were compared with findings in locally-recruited volunteers. RESULTS 200 PTB patients and 40 volunteers were enrolled. 6WMT was 497m (interquartile range 460-529) in controls versus 408m (IQR 346-450) in PTB patients at baseline (p<0.0001) and 470m (IQR 418-515) in PTB patients after 6 months (p=0.02 versus controls). SGRQ total score was 0 units (IQR 0-2.9) in controls, versus 36.9 (27.4-52.8) in PTB patients at baseline (p<0.0001) and 4.3 (1.7-8.8) by 6 months (p<0.0001). Mean percentage of predicted FEV1 was 92% (standard deviation 19.9) in controls, versus 63% (19.4) in PTB patients at baseline (p<0.0001) and 71% (17.5) by 6 months (p<0.0001). After 6 months, 27% of TB patients still had at least moderate-severe pulmonary function impairment, and 57% still had respiratory symptoms, despite most achieving 'successful' treatment outcomes, and reporting good quality of life. More-advanced disease at baseline (longer illness duration, worse baseline X-ray) and HIV positivity predicted residual disability. AE at any time during treatment were common: itch 59%, arthralgia 58%, headache 40%, nausea 33%, vomiting 16%. CONCLUSION We found high 6-month residual pulmonary disability and high AE rates. Although PTB treatment is highly successful, the extent of morbidity during treatment and residual impairment could be overlooked if not specifically sought. Calculations of PTB-related burden of disease should acknowledge that TB-related morbidity does not stop at 6 months. Early case detection and treatment are key in minimising residual impairment.The study received funding from the Australian Respiratory Council, Royal Australasian College of Physicians (Covance Award to APR), National Health and Medical Research Council (NHMRC) of Australia (Grants 605806 and 496600, a scholarship to APR, and fellowships to APR, TWY, PMK, NMA). Graeme Maguire is supported by an NHMRC Practitioner Fellowship and the Margaret Ross Chair in Indigenous Health. Views expressed in this publication are those of the authors and do not reflect the views of NHMRC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

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

Supporting holistic care for patients with tuberculosis in a remote Indigenous community: a case report

Context: Tuberculosis (TB) is a serious infectious disease with high rates of morbidity and mortality if left untreated. In Australia, TB has been virtually eradicated in non-Indigenous Australian-born populations but in remote Aboriginal and/or Torres Strait Islander communities TB presents a rare but significant public health issue. Remote health services are most likely to encounter patients with suspected and confirmed TB diagnosis but may be unprepared for supporting someone with this disease and the complexities of balancing public health risk with patient autonomy. Issue: This case study will outline the process for diagnosis and treatment of a TB patient in a remote Cape York community. This case involved significant delay in diagnosis and required several strategies to achieve successful disease eradication. The process of treatment, however, had a significant effect on the patient’s physical health, and social and emotional wellbeing. Lessons learned: This case highlights the importance of early collaboration between medical, nursing, Indigenous health worker and allied health services and the importance of technology such as electronic information records to support opportunistic access to diagnostic services and treatment. The enactment of the TB protocol should include discussions about the consequences of any restrictions of movement, employment or social/community roles. Identifying alternative opportunities to engage in meaningful roles may reduce the impact the disease has on a patient’s quality of life

Deep Learning Models for Classification of Lung Diseases

This thesis focuses on the importance of early detection in lung cancer through the use of medical imaging techniques and deep learning models. The current practice of examining nodules larger than 7 mm can delay detection and allow cancerous nodules to grow undetected. The project aims to detect nodules as small as 3 mm to improve the chances of early cancer identification. The use of constrained volume datasets and transfer learning techniques addresses the scarcity of medical data, and deep neural networks are employed for classification and segmentation tasks. Despite the limited dataset, the results demonstrate the effectiveness of the proposed models. Class activation maps and segmentation techniques enhance accuracy and provide insights into the most critical areas for diagnosis. This research contributes to the understanding of lung disease diagnosis and highlights the potential of deep learning in medical imaging.&nbsp