A PULMONARY LOBE BASED COVID DISEASE PREDICTION USING DEEP LEARNING TECHNIQUES
DOI:
https://doi.org/10.63682/jns.v14i14S.3426Keywords:
COVID prediction, pulmonary lobe, image processing, Convolutional neural network, deep learningAbstract
Corona virus disease 2019 (COVID-19) is an infectious disease triggered by severe acute respiratory syndrome corona virus 2 (SARS-CoV-2). Since the disease has spread all over the globe in enormous numbers and is declared a pandemic. Although radiological imaging is not recommended for diagnostics as the patient arrives in the clinic, a chest X-ray is often useful to monitor treatment outcomes and co morbidities in seriously ill patients. The detection of COVID-19 from chest X-ray and its differentiation from lung diseases with identical opacities is a puzzling task that relies on the availability of expert radiologists. Recently, several researchers have reported the use of AI-based tools in solving image classification problems in healthcare, based on training with X-ray images, CT scans, and histopathology images. Deep learning is an extremely powerful tool for learning complex, cognitive problems, and the frequency of their use and evaluation in different problems is increasing. In the present study, we have made use of a deep learning algorithm using the convolutional neural network (CNN) that can efficiently detect COVID-19 from CT-scan images. And also implement Multi-class CNN to identify the multiple lung diseases such as Pneumonia, tuberculosis. Experimental results shows that the proposed system provide improved accuracy in disease prediction and also provide the diagnosis information about analysed diseases
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