• Mr.K.Nagaraju
• Dr. Tryambak A. Hiwarkar

Corona Virus Infectious Disease (COVID-19) is an infectious disease. The disease of COVID-19 came to the earth in the beginning of 2019. It is breaking out all over the world and affecting a huge number of people all over the world. The World Health Organization (WHO) has declared COVID-19 a pandemic. Doctors' diagnoses. The corona virus (COVID-19) has become one of the most serious and acute diseases in recent times that has spread throughout the world. An automated disease detection framework helps clinicians diagnose disease and provides an accurate, consistent and rapid response and reduces mortality. Therefore, to prevent the spread of COVID-19, an automatic detection system should be adopted as the fastest diagnostic option. This paper aims to present hybrid deep learning approaches for convolutional neural network (CNN) and long-term memory (LSTM) to automatically predict COVID-19 from X-ray images. In this system, CNN is used for deep feature extraction and LSTM is used for detection using the extracted feature. The dataset for this system uses a set of 500 X-ray images, including 200 images of COVID-19. Experimental results show that the proposed system achieved 97% accuracy, 95% specificity, and 98% sensitivity. The system achieved the desired results.

COVID-19, Deep learning, Chest X-ray, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Images

"A DEEP LEARNING HYBRID APPROACH FOR THE DETECTION OF COVID-19 FROM X-RAY IMAGES USING CNN-LSTM NETWORK MODELS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.d886-d897, September-2023, Available :http://www.jetir.org/papers/JETIR2309396.pdf

"A DEEP LEARNING HYBRID APPROACH FOR THE DETECTION OF COVID-19 FROM X-RAY IMAGES USING CNN-LSTM NETWORK MODELS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppd886-d897, September-2023, Available at : http://www.jetir.org/papers/JETIR2309396.pdf