Diagnosing Covid-19 using Image Processing and Machine Learning

Abstract

Image Processing has been a fond field for giving elaborated visual data to process the image data to simplify it for human for illustration for machine concept. With image processing you can have better solution for digital images. Training a machine to do something by providing it with certain training data is known as machine learning which may include here image processing. Machine learning have architectures, loss function, models and many other approaches that is used to determine and provide better image processing It is usually applied for image enhancement, restoration and morphing (inserting one’s style of painting on an image). The objective is to provide a conceptual transfer learning framework by using image classification with the help of learning models, to support the detection of COVID-19 imaging modalities included CT scan and X-Ray. We will be going to make a custom Dataset and the Data Loader in the PyTorch. Then will train a ResNet-18 model for Image Classification performance. In end we will create a Convolutional Neural Networks and then we will be able to train it to analyze Chest X-Ray scans with honestly high accuracy. We will train the model using the ResNet-18 till the accuracy will be 0.95 or 95% in condition till then will stop the training where performance satisfied. So finally we given the 6 images and created a model and took 6 images from test set and put it in the training model and do the prediction and set the accuracy to the limit. Until the accuracy is not fulfilled the training will happen in the work.