Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Jun 25, 2020
Date Accepted: Nov 9, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Mining-based Diagnosis: Comprehensive Computer-Aided Decision Support Framework to Diagnose Tuberculosis from Chest X-Ray Images with Retrieval of Descriptive Evidence
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 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. Additionally, 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 +ive or TB -ive) without providing further descriptive information.
Objective:
Therefore, the main objective of this study is to propose a comprehensive CAD framework for the effective diagnosis of TB with providing the visual as well as descriptive information from the previous patients's 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 multi-level similarity measure algorithm is devised based on multi-scale information fusion to retrieve the best-matched cases from the previous database.
Results:
Its performance was evaluated based on two well-known CXR datasets made available by the U.S. National Library of Medicine and the National Institute 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 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’ DB. 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 also facilitate the radiologists in subjectively validating the CAD decision.
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