• HARSHA CHAITANYA ATTADA

The novel corona virus disease 2019 (COVID-19) constitutes a public health emergency globally. The number of infected people and deaths are proliferating every day, which is putting tremendous pressure on our social and healthcare system. Rapid detection of COVID-19 cases is a significant step to fight against this virus as well as release pressure off the healthcare system. One of the critical factors behind the rapid spread of COVID-19 pandemic is a lengthy clinical testing time. The imaging tool, such as Chest X-ray (CXR), can speed up the identification process. Therefore, our objective is to develop an automated CAD system for the detection of COVID-19 samples from healthy and pneumonia cases using CXR images. Due to the scarcity of the COVID-19 benchmark dataset, we have employed deep transfer learning techniques, where we examined 15 different pre-trained CNN models to find the most suitable one for this task. A total of 860 images (260 COVID-19 cases, 300 healthy and 300 pneumonia cases) have been employed to investigate the performance of the proposed algorithm, where 70% images of each class are accepted for training, 15% is used for validation, and rest is for testing. It is observed that the VGG19 obtains the highest classification accuracy of 89.3% with an average precision, recall, and F1 score of 0.90, 0.89, and 0.90, respectively. This study demonstrates the effectiveness of deep transfer learning techniques for the identification of COVID-19 cases using CXR

COVID-19, Chest X-Ray Image, transfer learning, image identification

"IDENTIFICATION OF COVID-19 SAMPLES FROM CHEST X-RAY IMAGES USING DEEP LEARNING: A COMPARISON OF TRANSFER LEARNING APPROACHES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 11, page no.b418-b426, November-2021, Available :http://www.jetir.org/papers/JETIR2111153.pdf

"IDENTIFICATION OF COVID-19 SAMPLES FROM CHEST X-RAY IMAGES USING DEEP LEARNING: A COMPARISON OF TRANSFER LEARNING APPROACHES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 11, page no. ppb418-b426, November-2021, Available at : http://www.jetir.org/papers/JETIR2111153.pdf