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Thoracic disease affects various parts of the organ around the chest at different paces, and accounts for the number of deaths in India. We have vaccines and medicines to prevent the spread of infection-causing bacteria, viruses, and fungi in the organs, but many patients still die as a result of the inability in early detection of the disease. The diagnosis of thoracic disease relies on chest x-ray images that are manually interpreted by an expert. Chest x-ray images have their own set of flaws, which can lead to inaccuracy in judging infected areas or even the presence of infections. This paper focuses on using computer-assisted techniques, different algorithms present in machine learning and deep learning pre-trained CNN models (ResNet, DenseNet, CheXNet and VGG), and classification techniques(Logistic Regression, SVM, K-nn) in the medical and healthcare field for the classification of chest x-ray and diagnosis of thoracic disease.
"A Survey: Identification of Different Thoracic Disease Using Convolutional Neural Network", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.6, Issue 11, page no.22 - 25, November-2021, Available :http://www.ijrti.org/papers/IJRTI2111005.pdf
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000204842
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator