COVID-19 CHEST CT IMAGE SEGMENTATION NETWORK BY MULTI SCALE FUSION AND ENHANCEMENT OPERATIONS

COVID-19 CHEST CT IMAGE SEGMENTATION NETWORK BY MULTI SCALE  FUSION AND ENHANCEMENT OPERATIONS

Er.J.Janani

Dept. of Computer Science

VPMM College of Engineering and Technology,

 Krishnankovil, Virudhunagar., India

jananijayagobi08@gmail.com

Mr.R.Rajkumar.,M.E,MISTE

Associate Professor: dept. of Computer Science

VPMM College of Engineering and Technology,

Krishnankovil, Virudhunagar., India

hr.rajkumar11@gmail.com

ABSTRACT-A deep learning research is the availability of training data sets. With a limited number of publicly available COVID-19 chest X-ray images, the generalization and robustness of deep learning models to detect COVID-19 cases developed based on these images are questionable. Segmentation of pneumonia lesions from Lung CT images has become vital for diagnosing the disease and evaluating the severity of the patients during the COVID-19 pandemic. Several Deep Learning based systems have been proposed for this task. However, some low-contrast abnormal zones in CT images make the task challenging. which will be used as the testing data set. We used a deep learning model based on the convolutional neural network architecture, which was pre trained to recognize objects from a million of images and then retrained to detect abnormality in chest X-ray images.

Keywords—Deep Learning, COVID-19, pneumonia

prediction,  Evolution, Intelligent System.