Pneumonia Based Covid-19 Classification using CNN and LSTM

Abstract

Abstract: Pneumonia causes inflammation of the air sacs in either one or both lungs. There may be fluid or pus (purulent material) filling the air sacs, causing breathing problems, a fever, chills, and a cough with pus or phlegm. A variety of microorganisms, including bacteria, viruses, and fungi, can cause pneumonia, but a modern virus called COVID-19 has spread over the world and is currently infecting millions of people. Numerous countries are struggling with a shortage of testing supplies, vaccines, and other resources as a result of the large and sudden rise in cases. To expedite the testing process, scientists from all over the world have worked to create new methods for identifying the virus. We present in this project a hybrid deep learning model based on a convolutional neural network (CNN) and gated long short term memory (LSTM) to identify the viral illness from chest X-rays because it is extremely difficult to tell whether a patient who is admitted to the hospital is suffering from pneumonia or COVID-19 because both of them share the same symptom (CXRs). The suggested model employs a CNN to extract features and an LSTM as a classifier. Three batches of 7470 CXR images were used to train the model (COVID-19, Pneumonia, and Normal). The accuracy rate of the proposed model is 97%. These findings show how deep learning may considerably enhance X-ray image processing for patient COVID-19's early diagnosis. These indicators may make it possible to lessen the disease's impact. This strategy, in our opinion, can better assist doctors in making an early diagnosis