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- research-articleJanuary 2022
Deep Learning-Based Tool for Automatic Feature Marking, Cropping, Visualization, and Classification of Chest Radiographs
DSMLAI '21': Proceedings of the International Conference on Data Science, Machine Learning and Artificial IntelligencePages 20–25https://doi.org/10.1145/3484824.3484922The 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 ...
- research-articleApril 2020
An adversarial approach for the robust classification of pneumonia from chest radiographs
CHIL '20: Proceedings of the ACM Conference on Health, Inference, and LearningPages 69–79https://doi.org/10.1145/3368555.3384458While deep learning has shown promise in the domain of disease classification from medical images, models based on state-of-the-art convolutional neural network architectures often exhibit performance loss due to dataset shift. Models trained using data ...
- ArticleAugust 2009
Lung Segmentation in Chest Radiographs by Means of Gaussian Kernel-Based FCM with Spatial Constraints
FSKD '09: Proceedings of the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03Pages 428–432https://doi.org/10.1109/FSKD.2009.811A Gaussian kernel-based fuzzy clustering algorithm with spatial constraints for automatic segmentation of lung field in chest radiographs is proposed in this paper. The algorithm is realized by modifying the objective function in the conventional fuzzy ...
- ArticleOctober 2001
Directional Edge Registration for Temporal Chest Image Substraction
In this study, we used a directional filtering techniqueto accurately register the ribs on temporal chest radiographs.Rib registration was the primarily technical objective in thiswork. In order to accurately extract the ribs, we developed adirectional ...