Evolution of Machine Learning in Tuberculosis Diagnosis: A Review of Deep Learning-Based Medical Applications

• Published in: Electronics (Basel) Vol. 11; no. 17; p. 2634
• Main Authors: Singh, Manisha, Pujar, Gurubasavaraj Veeranna, Kumar, Sethu Arun, Bhagyalalitha, Meduri, Akshatha, Handattu Shankaranarayana, Abuhaija, Belal, Alsoud, Anas Ratib, Abualigah, Laith, Beeraka, Narasimha M., Gandomi, Amir H.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2022
• Subjects: Algorithms; Artificial intelligence; Automation; Bacteria; Bacterial infections; Computer-aided medical diagnosis; Data mining; Deep learning; Developing countries; Diagnosis; Diagnostic imaging; Drug resistance; Fuzzy logic; Genetic algorithms; Immune system; Infectious diseases; LDCs; Light emitting diodes; Lung diseases; Lungs; Machine learning; Methods; Microscopy; Neural networks; Patients; Pattern recognition; Tuberculosis; X-rays; India
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics11172634

Tuberculosis (TB) is an infectious disease that has been a major menace to human health globally, causing millions of deaths yearly. Well-timed diagnosis and treatment are an arch to full recovery of the patient. Computer-aided diagnosis (CAD) has been a hopeful choice for TB diagnosis. Many CAD approaches using machine learning have been applied for TB diagnosis, specific to the artificial intelligence (AI) domain, which has led to the resurgence of AI in the medical field. Deep learning (DL), a major branch of AI, provides bigger room for diagnosing deadly TB disease. This review is focused on the limitations of conventional TB diagnostics and a broad description of various machine learning algorithms and their applications in TB diagnosis. Furthermore, various deep learning methods integrated with other systems such as neuro-fuzzy logic, genetic algorithm, and artificial immune systems are discussed. Finally, multiple state-of-the-art tools such as CAD4TB, Lunit INSIGHT, qXR, and InferRead DR Chest are summarized to view AI-assisted future aspects in TB diagnosis.