Prediction and analysis of Covid-19 using the Deep Learning Models

The difficulty of diagnosing several lung disorders, including atelectasis, cardiomegaly, lung cancer, and COVID-19, is a challenging problem and needs to be addressed. These conditions exhibit some symptoms and demand advanced medical imaging process, thorough clinical assessments, and innovative procedures for accurate diagnosis. The shortage of qualified radiologists further makes the problem more complex to deal with. COVID-19 in particular has resulted in a remarkable number of fatalities around the world. Children below the age of 5 and individuals over 65 are more likely to be affected by lung disorders. It is very hard to diagnose and manage COVID-19 absolutely, but it can be identified earlier by employing computer-aided diagnosis (CAD) technologies to make timely diagnosis. Currently, radiologists adopt technologies, which are driven by artificial intelligence. By using them, medical imaging data, such as chest X-rays and CT scans, can be investigated to identify patterns to diagnose the severity of the virus. This expedites the diagnostic process and enhances accuracy, facilitating more timely and precise medical interventions. The efficiency of artificial intelligence in processing large datasets can directly help healthcare professionals in making diagnosis quicker and more accurate. The objective of the work in this paper is to design and implement deep learning model classifiers, which will effectively categorize the patterns found in the X-rays and CT scans.

Three techniques for categorization are exploited to propose an entirely new hybrid convolutional neural network (CNN) model in this context. MRI and CT image categorization in the first classification method employ Fully Connected (FC) layers. The weights are calculated and tuned for training the algorithm over multiple periods to deliver the maximum precision for classification. The most accurate MRI and CT image characteristics are studied, and deep learning model classifiers are deployed to categorize the patterns found in the images using classification techniques. MRI and CT images are clustered effectively based on the novel classification process, which produces a mixture of the suggested classifiers. The clustering strategy ensures a higher level of sophistication to the categorization process by assembling diverse classifiers, each with its unique strength, useful to create a more robust system.

The experimental results suggested the collective classifier for the prediction of COVID-19 which makes use of ResNet, VGG16, Xception, and Inception V3 predictive models that are robust and proved superior performance in terms of accuracy, precision, specificity, and sensitivity evaluation metrics.

Early prediction of lung diseases can indeed facilitate targeted treatment and improve outcomes for patients. It is exciting to observe that applying state-of-the-art predictive technologies in medical imaging technologies improves our ability to understand and manage diseases like COVID-19. The new findings established that the inception method could accurately predict and envision the process of lung lesion in COVID-19 patients in the prior stage of ailment, which enables health professionals to predict the severity of COVID-19 and make adequate treatment.