Classification of covid patient image dataset using modified deep convolutional neural network system

Manjunathan Alagarsamy, Karthikram Anbalagan, Yuvaraja Thangavel, Jeevitha Sakkarai, Jenopaul Pauliah, Kannadhasan Suriyan


The number of people infected with the corona virus is steadily rising. Even after being treated and returned to normality, many who were impacted are still suffering from a variety of health problems. We suggest a new, more effective approach to dealing with this issue, as well as putting in place preventative measures to prevent the spread of disease. The modified convolutional neural networks (M-CNN) architecture is modified deepCNN architecture. Using existingcorona virus disease 2019(COVID-19) computerizedtomographyscan (CT scan) images, this suggested approach intends to develop a deep model for screening and forecasting the risk of disease propagation. The suggested model was trained using 1000 scan pictures from various sources, yielding a prediction accuracy of 93 percent, which is much greater than previous methods.


Convolutional neural networks; Covid; CT scan images; Disease prediction

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