Comprehensive Computer-Aided Decision Support Framework to Diagnose Tuberculosis From Chest X-Ray Images: Data Mining Study

Abstract

Background Tuberculosis (TB) is one of the most infectious diseases that can be fatal. Its early diagnosis and treatment can significantly reduce the mortality rate. In the literature, several computer-aided diagnosis (CAD) tools have been proposed for the efficient diagnosis of TB from chest radiograph (CXR) images. However, the majority of previous studies adopted conventional handcrafted feature-based algorithms. In addition, some recent CAD tools utilized the strength of deep learning methods to further enhance diagnostic performance. Nevertheless, all these existing methods can only classify a given CXR image into binary class (either TB positive or TB negative) without providing further descriptive information. Objective The main objective of this study is to propose a comprehensive CAD framework for the effective diagnosis of TB by providing visual as well as descriptive information from the previous patients’ database. Methods To accomplish our objective, first we propose a fusion-based deep classification network for the CAD decision that exhibits promising performance over the various state-of-the-art methods. Furthermore, a multilevel similarity measure algorithm is devised based on multiscale information fusion to retrieve the best-matched cases from the previous database. Results The performance of the framework was evaluated based on 2 well-known CXR data sets made available by the US National Library of Medicine and the National Institutes of Health. Our classification model exhibited the best diagnostic performance (0.929, 0.937, 0.921, 0.928, and 0.965 for F1 score, average precision, average recall, accuracy, and area under the curve, respectively) and outperforms the performance of various state-of-the-art methods. Conclusions This paper presents a comprehensive CAD framework to diagnose TB from CXR images by retrieving the relevant cases and their clinical observations from the previous patients’ database. These retrieval results assist the radiologist in making an effective diagnostic decision related to the current medical condition of a patient. Moreover, the retrieval results can facilitate the radiologists in subjectively validating the CAD decision.

Leveraging image complexity in macro-level neural network design for medical image segmentation

“…The images are relatively large, either or pixels. Following others 42 , we selected 100 images for training and the remaining 38 for testing.…”

Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review

“…[36] Single iteration-maximum pattern margin support vector machine + probability esti-kNN c , multiple-instance learning-based system: miSVM d , miSVM + probability Radiologist's reading Zambia, Tanzania, Gambia Melendez et al [37] mation and data discarding (0. Twenty-five of the included studies [14,[17][18][19]34,[36][37][38][47][48][49]52,53,56,59,[62][63][64][65][66]68,70,[72][73][74] reported comparison results with several other previous studies, while the remaining 22 did not. Twenty-six of 47 included studies [19,20,35,[38][39][40]42,43,45,[47][48][49][50]53,55,56,[58][59][60][61][62]…”

“…Twenty-five of the included studies [14,[17][18][19]34,[36][37][38][47][48][49]52,53,56,59,[62][63][64][65][66]68,70,[72][73][74] reported comparison results with several other previous studies, while the remaining 22 did not. Twenty-six of 47 included studies [19,20,35,[38][39][40]42,43,45,[47][48][49][50]53,55,56,[58][59][60][61][62]65,[69][70][71]74] were funded or sponsored by companies, private...…”

Deep Learning for Chest X-ray Analysis: A Survey

“…Image retrieval is a task investigated by a number of authors (Anavi et al, 2015(Anavi et al, , 2016Conjeti et al, 2017;Chen et al, 2018c;Silva et al, 2020;Owais et al, 2020;Haq et al, 2021). The aim of image retrieval tools is to search an image archive to find cases similar to a particular index image.…”