CoviDetector: A transfer learning-based semi supervised approach to detect Covid-19 using CXR images

Chowdhury, Deepraj; Das, Anik; Dey, Aritra; Banerjee, Soham; Golec, Muhammed; Kollias, Dimitrios; Kumar, Mohit; Kaur, Guneet; Kaur, Rupinder; Arya, Rajesh; Wander, Gurleen; Wander, Praneet; Wander, Gurpreet Singh; Parlikad, Ajith Kumar; Gill, Sukhpal Singh; Uhlig, Steve

doi:10.1016/j.tbench.2023.100119

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…The technique finds an unusual pattern in the input data [ 93 ]. On the other hand, semi-supervised learning can work with both labeled and unlabeled data [ 11 ]. This strategy can operate on massive amounts of data due to the applicability of labeled and unlabeled data, even though labeled data are limited.…”

Section: Machine Learningmentioning

confidence: 99%

“…ML can diagnose lung disorders using images from medical or radiological procedures [ 10 ]. ML, a subfield of artificial intelligence (AI), tries to make computers learn from data [ 11 ]. Consequently, ML offers an automated framework that may be utilized to detect or anticipate lung illnesses in their earliest stages compared to manual methods [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

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A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review

Kumar,

Kumar

et al. 2024

BMC Med Imaging

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Background Lung diseases, both infectious and non-infectious, are the most prevalent cause of mortality overall in the world. Medical research has identified pneumonia, lung cancer, and Corona Virus Disease 2019 (COVID-19) as prominent lung diseases prioritized over others. Imaging modalities, including X-rays, computer tomography (CT) scans, magnetic resonance imaging (MRIs), positron emission tomography (PET) scans, and others, are primarily employed in medical assessments because they provide computed data that can be utilized as input datasets for computer-assisted diagnostic systems. Imaging datasets are used to develop and evaluate machine learning (ML) methods to analyze and predict prominent lung diseases. Objective This review analyzes ML paradigms, imaging modalities' utilization, and recent developments for prominent lung diseases. Furthermore, the research also explores various datasets available publically that are being used for prominent lung diseases. Methods The well-known databases of academic studies that have been subjected to peer review, namely ScienceDirect, arXiv, IEEE Xplore, MDPI, and many more, were used for the search of relevant articles. Applied keywords and combinations used to search procedures with primary considerations for review, such as pneumonia, lung cancer, COVID-19, various imaging modalities, ML, convolutional neural networks (CNNs), transfer learning, and ensemble learning. Results This research finding indicates that X-ray datasets are preferred for detecting pneumonia, while CT scan datasets are predominantly favored for detecting lung cancer. Furthermore, in COVID-19 detection, X-ray datasets are prioritized over CT scan datasets. The analysis reveals that X-rays and CT scans have surpassed all other imaging techniques. It has been observed that using CNNs yields a high degree of accuracy and practicability in identifying prominent lung diseases. Transfer learning and ensemble learning are complementary techniques to CNNs to facilitate analysis. Furthermore, accuracy is the most favored metric for assessment.

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Section: Machine Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review

Kumar,

Kumar

et al. 2024

BMC Med Imaging

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show abstract

“…Although ML has previously been used in healthcare to automate hospital systems, recently, it has also been utilised in the diagnosis, early detection, and monitoring of diseases [3][4][5]. In recent years, there have been several successful applications of AI in various medical conditions, such as the diagnosis of LA fibrillation and evaluation of prognosis in COVID-19 [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Can Machine Learning Assist in Diagnosis of Primary Immune Thrombocytopenia? A Feasibility Study

Miah,

Kollias,

Pedone

et al. 2024

Diagnostics

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Primary Immune Thrombocytopenia (ITP) is a rare autoimmune disease characterised by the immune-mediated destruction of peripheral blood platelets in patients leading to low platelet counts and bleeding. The diagnosis and effective management of ITP are challenging because there is no established test to confirm the disease and no biomarker with which one can predict the response to treatment and outcome. In this work, we conduct a feasibility study to check if machine learning can be applied effectively for the diagnosis of ITP using routine blood tests and demographic data in a non-acute outpatient setting. Various ML models, including Logistic Regression, Support Vector Machine, k-Nearest Neighbor, Decision Tree and Random Forest, were applied to data from the UK Adult ITP Registry and a general haematology clinic. Two different approaches were investigated: a demographic-unaware and a demographic-aware one. We conduct extensive experiments to evaluate the predictive performance of these models and approaches, as well as their bias. The results revealed that Decision Tree and Random Forest models were both superior and fair, achieving nearly perfect predictive and fairness scores, with platelet count identified as the most significant variable. Models not provided with demographic information performed better in terms of predictive accuracy but showed lower fairness scores, illustrating a trade-off between predictive performance and fairness.

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Edge AI: A Taxonomy, Systematic Review and Future Directions

Gill,

Golec,

et al. 2024

Cluster Comput

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CoviDetector: A transfer learning-based semi supervised approach to detect Covid-19 using CXR images

Cited by 6 publications

References 30 publications

A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review

A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review

Can Machine Learning Assist in Diagnosis of Primary Immune Thrombocytopenia? A Feasibility Study

Edge AI: A Taxonomy, Systematic Review and Future Directions

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