Deep Learning-Based Tool for Automatic Feature Marking, Cropping, Visualization, and Classification of Chest Radiographs.

The prime objective of this research is to develop an automatic tool 'Lung-Infection Visualizer' for marking the Region of Infection and cropping of the marked region in chest radiographs. The tool is also integrated with the feature extractor, feature visualization algorithm, and deep learning-based classifier. Thus, it facilitates the radiology experts where they can easily mark the infected region and visualize the region of infection. In this manuscript, the authors employ the template-based and Brute Force approach of feature mapping. Further, they applied the ResNet, Faster Recurrent Neural Network, XceptionNet, and VGG-16 deep learning-based classifiers for classifying the chest radiographs into bacterial pneumonia, viral pneumonia, COVID-19, and Normal classes. The authors also fine-tune the model parameters and hyperparameters for optimizing the performance of the deep learning-based models. The comparison in the performance proves that the VGG-16 model reports the highest accuracy of 90.07% and outperforms the other models on the dataset of 5,499 chest radiographs used for this research. The cropping tool is registered as Intellectual Property Rights in the name of authors with the registration number SW-14092/2021. And the title 'AutoCrop Tool'.