Diagnosis of COVID-19 using artificial intelligence based model

Scientists have shown that early diagnosis of COVID-19 and self-isolation can help curb the spread of the virus. Hence, there is a need to develop methods for early and fast diagnosis of the disease. This research focuses on the development and use of an AI based model that would help medical professionals in easy and fast detection of COVID-19 that can be found in X-ray images, CT scan images, and patient symptoms. The model would then be deployed to the web for easy accessibility. To increase the confident level of decision made by the model, different data augmentation technique was deployed to create variance to the dataset and thereby increasing the accuracy and validation of the model. This project proposes the use of Convolutional Neural Network for classification purpose of both the X-ray image of the Lung of a positive and negative pneumonia patient. Further processes were done with CT scan images of both positive and negative COVID-19 patients. This eliminated biasness of the model. Lastly, symptoms of the disease were added to the model as conditional statement convolutional neural network model. The model was able to reach an overall accuracy of 95% for Pneumonia X-ray image and 89.65% for CT scan image of unseen data (test/evaluation). Artificial intelligence based model is therefore, encouraged for better, easier and more accurate diagnosis of COVID-19. Also, the accuracy of the model can further be improved by certain feature engineering technique and adding more complex deep neural network technique known as ensembles.