CLASSIFICATION AND LOCALIZATION OF MULTI-TYPE ABNORMALITIES ON CHEST X-RAYS IMAGES USING CNN AND SVM ALGORITHM
Keywords:
Chest X-ray, CNN, SVM, abnormality detection, medical imaging, deep learning, classification, localization.Abstract
Chest X-ray (CXR) imaging plays a crucial role in the early detection and diagnosis of various pulmonary abnormalities. Automated classification and localization of multiple types of abnormalities in CXR images can significantly enhance diagnostic accuracy and assist radiologists in clinical decision-making. In this study, we propose a hybrid deep learning approach that integrates Convolutional Neural Networks (CNN) for feature extraction and Support Vector Machines (SVM) for classification to improve the detection of multi-type abnormalities in chest X-rays. The proposed model is trained on a large dataset of labeled CXR images and utilizes a region-based approach to localize abnormalities. The CNN component extracts deep hierarchical features, while the SVM classifier enhances robustness in distinguishing normal and abnormal cases.
References
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