Pneumonia and COVID-19 Detection on Chest X-Ray Images using Improved CNN
Arshina Momin1, Khushbu Patil2, Meghana Joshi3, Parimal Dhake4 and Prof. Pranjali Ghode5
Department of Computer Engineering
Genba Sopanrao Moze College of Engineering, Balewadi, Pune-45
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Abstract -The extreme lung conditions pneumonia and Covid-19, tend to affect one or both lungs, frequently brought on via viruses, fungus, or bacteria. Primarily based at the x-rays we've, we can be able to identify this lung infection. Chest X-rays dataset is taken from Kaggle which incorporate numerous x-rays photographs distinguished by means of 3 classes "Pneumonia", "Covid-19" and "Normal". Our aim is developing a deep learning model which can detect the lung disorder. In this project we are using a deep learning model Improved CNN with backbone architecture Densenet-121.Within the healthcare area, ailment detection is essential because early identification and specific analysis can appreciably beautify affected person outcomes. But, conventional methods to illness detection can be exertions-in depth, pricey, and liable to mistakes. Deep learning has emerged as a viable solution to these problems. Deep learning algorithm can aid healthcare workers in detecting COVID-19 with minimal processing of chest X-ray images. In this study, 3-class datasets were created which included COVID-19, pneumonia and normal images obtained from open sources. COVID-19 and viral pneumonia CXR images contain similar features which are challenging for the radiologist to interpret. However, the CNN model can easily learn the features in just a few epochs of training and classify the images correctly. The high accuracies obtained suggest that the deep learning models could find something distinctive in the CXR images and that makes the deep networks capable of distinguishing the images correctly. These trained models can effectively reduce the workload of medical practitioners and increase the accuracy and efficiency of COVID-19 diagnosis.
Keywords: Deep Learning, Healthcare, CNN, Densenet121, Covid-19.
