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Paper Title: A Survey: Identification of Different Thoracic Disease Using Convolutional Neural Network
Authors Name: Richa Tiwari , Monika Verma , Sumit Kumar Sar
Unique Id: IJRTI2111005
Published In: Volume 6 Issue 11, November-2021
Abstract: 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.
Keywords: CNN, Deep Learning, Thoracic Disease, X-ray image, Automation, Machine Learning, DenseNet, K-nn, SVM, ResNet.
Cite Article: "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
Downloads: 00023127
Publication Details: Published Paper ID: IJRTI2111005
Registration ID:181646
Published In: Volume 6 Issue 11, November-2021
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ISSN Number: 2456 - 3315
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