Predictive Modeling of Infection from Chest X-ray Images with ResNet34 and Deep learning CNN approach
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Abstract
The timely and precise detection of infected persons is still a major issue in the ongoing fight against the COVID-19 epidemic. With its ability to provide information on the severity and development of the illness, chest X-ray imaging has become a useful diagnostic and surveillance technique for COVID-19 pneumonia. This paper suggests a unique method for predictive modeling of COVID-19 infection from chest X-ray pictures by using the capabilities of deep learning, namely ResetNet architecture. In image classification tasks, ResNet34, a convolutional neural network (CNN) variation, has shown impressive performance, particularly in situations with little training data. Here, we report a comprehensive training and validation protocol for the ResetNet model using a large dataset of chest X-ray images that includes both positive and negative COVID-19 instances. To improve feature extraction, the dataset is preprocessed. All data size: 5856,Train size: 4684, Val size: 585, Test size: 587. To reduce overfitting and boost generalization, data augmentation methods are used. Our suggested method uses chest X-ray pictures to reliably distinguish COVID-19 patients from non-COVID-19 cases by using deep learning's discriminative capabilities. The result of model with TRAINING: Epoch: 19, Loss: 0.08099747663349896, Accuracy: 97.11and TEST: Epoch: 19, Loss: 0.08553645759820938, Accuracy: 96.76.
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