SJIF(2020): 5.702

International Journal of Advanced Research and Publications

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Predicting COVID-19 With An Approach Of Machine Learning Based On CNN Using Chest X-Ray Images

Volume 5 - Issue 3, March 2022 Edition
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Ashraful Islam, Farhan Bhuiyan, Hasibul Hasan Sakib, Tusher Debnath, Humayra Siddika, Sumiya Akther Joya
CNN, Covid-19 affected dataset, Chest Images, Python, TensorFlow.
In March 2020, The WHO (World Health Organization) announced COVID-19 as a worldwide epidemic. The Artificial Intelligence can play a vital role in different ways, such as machine learning in identifying COVID-19 patients by analyzing their chest X-Ray visually. Classifying the chest X-Ray with a new machine learning method, COVID-19 patients and non-COVID-19 patients can be identified. The new ML method can lower the development cost, also can detect & diagnose the virus in a test with large number of datasets. The ML method can be a useful tool to scan and analyze large number of the chest X-Ray as an image and also with accurate outcomes. This ML approach can work with rapid amount of data in short time and accurately from the Chest X-Ray image. With an improved Convolutional Neural Network (CNN), the X-Ray image can be segmented in fewer iterations. By analyzing and segmenting the chest X-Ray image, the detection process can be optimized with its histograms and threshold techniques. a new model based on Convolutional Neural Network (CNN) that automatically detects COVID-19 using chest images is presented. that most of the affected people have no common symptoms before checkup COVID-19. If the detection results are incorrect, the patient will not be able to understand that he or she has Covid19. The proposed model is evaluated by Python libraries namely TensorFlow and Keras.
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