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

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 app...

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Vydané v:Electronics (Basel) Ročník 11; číslo 17; s. 2634
Hlavní autori: 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.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.09.2022
Predmet:
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
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11172634