Enhanced framework for COVID-19 prediction with computed tomography scan images using dense convolutional neural network and novel loss function. (January 2023) - Motwani, Anand; Shukla, Piyush Kumar; Pawar, Mahesh; Kumar, Manoj; Ghosh, Uttam; Alnumay, Waleed; Nayak, Soumya Ranjan
Highlights: Proposes a Dense-CNN with novel loss function based on cross-entropy for efficient convergence. Effective diagnosis with fewer 'false-negatives' to prevent further spread of the disease. Loss function effectively smooth's the sharp minima with False-Negative of 6.5% for better optimization. Prompt and effective diagnosis of COVID-19 disease to reduce the incidence of disease. Abstract: Recent studies have shown that computed tomography (CT) scan images can characterize COVID-19 disease in patients. Several deep learning (DL) methods have been proposed for diagnosis in the literature, including convolutional neural networks (CNN). But, with inefficient patient classification models, the number of 'False Negatives' can put lives at risk. The primary objective is to improve the model so that it does not reveal 'Covid' as 'Non-Covid'. This study uses Dense-CNN to categorize patients efficiently. A novel loss function based on cross-entropy has also been used to improve the CNN algorithm's convergence. The proposed model is built and tested on a recently published large dataset. Extensive study and comparison with well-known models reveal the effectiveness of the proposed method over known methods. The proposed model achieved a prediction accuracy of 93.78%, while false-negative is only 6.5%. This approach's significant advantage is accelerating the diagnosis and treatment of COVID-19. Graphical Abstract: Image, graphical abstract