Applied Mathematics & Information Sciences

Author Country (or Countries)

Saudi Arabia


The necessity for sophisticated and precise diagnostic instruments for the prompt recognition of COVID-19 patients has been highlighted by the continuing worldwide epidemic. In this regard, this study presents a unique method for accurately classifying X-ray images of chest in COVID-19 prediction by combining Neutrosophic Fuzzy Logic with a Hybrid CNN and LSTM architecture. Medical image analysis involves uncertainties and imprecise information, which is handled via Neutrosophic Fuzzy Logic. The suggested hybrid model offers a thorough comprehension of the spatial and temporal patterns in chest X-ray pictures by utilizing the advantages of CNN for feature extraction and LSTM for sequential information learning. Hybrid CNN-LSTM architecture based on Neutrosophic Fuzzy Logic is trained on an enormous set of various chest X-ray pictures, including both positive and negative instances of COVID-19 and other respiratory diseases. The proposed method is implemented using Python software. In addition to improving COVID-19 prediction accuracy, the combination of Neutrosophic Fuzzy Logic with a Hybrid CNN-LSTM structure creates a strong framework for managing uncertainty in medical image classification tasks. The proposed CNN-LSTM model with Neutrosophic Fuzzy logic shows better accuracy with 98.6% which is 4.4 % higher when compared with COVID CAPS , Bayesian CNN , Deep Feature + SVM and DCNN. This study represents a major advancement in the creation of sophisticated and trustworthy diagnostic instruments for effective healthcare administration during times of worldwide health emergencies.

Digital Object Identifier (DOI)