Browsing by Author "Varma, Vivek"

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    A Hybrid Model for Analysis and Detection of Covid 19 on X Ray Images using Machine Learning and Deep learning Techniques
    (Department of Computer Science & Engineering, NITS, 2022) Varma, Vivek
    Covid-19 has presented itself as a deadly infection that can affect all sorts of living beings. The day-to-day life of humans, in all of the countries are getting hindered due to this rapidly spreading viral problem. A clinically based examination of COVID-19 affected persons revealed that most of these people had a respiratory based infection after coming into touch with this disease. Chest based x-ray and chest Computer Tomography are better effective imaging techniques for finding respiratory problems. Nonetheless, producing a x-ray is a lower cost procedure than a CT scan. Deep learning is a very successful machine learning procedure, providing valuable perspective to examine a large quantity of x-ray images, which may provide a significant impact on Coronavirus screening. This work takes x-ray images of chest from corona infected and healthy people. The images are balanced and then fed into convolutional neural network models and their performance is analysed. The accuracy of ResNeXt, Xception and InceptionV3 is checked. 15,135 x-ray scans of chest were taken as the dataset which is collected from multiple certified sources, from this 12,121 are used in training and 3032 are used in validation. The Xception model provides more accuracy (i.e., 97%) on detecting covid in the images outperforming other pre-trained models. Furthermore a concatenated model consisting of all the 3 above stated models was implemented and trained on the same data set which provided the highest overall accuracy of 98%. A second instance of the same concatenated model was created and trained on a new dataset collected from Brixia project consisting of only covid x-rays divided into 18 classes on the basis of their severity. This model was then used to assign a severity score on the covid x-ray scans providing 92% accuracy. Thus the final model being proposed is able to take the x-rays and classify them into three categories (Pneumonia, Normal, Covid) and assign a severity score to it in case of detection of Covid.