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APPARATUS AND METHOD FOR CLASSIFICATION OF ANGIOMYOLIPOMA WIHTOUT VISIBLE FAT AND CLEAR CELL RENAL CELL CARCINOMA IN CT IMAGES USING DEEP LEARNING AND SAHPE FEATURES
APPARATUS AND METHOD FOR CLASSIFICATION OF ANGIOMYOLIPOMA WIHTOUT VISIBLE FAT AND CLEAR CELL RENAL CELL CARCINOMA IN CT IMAGES USING DEEP LEARNING AND SAHPE FEATURES
In the computed tomography image according to an embodiment of the present invention, hemangiomycoma and transparent cell renal cell carcinoma classification apparatus using deep learning features and shape features, hemangiomycoma (AMWwvf) and transparent cells that do not contain gross identification fat Extract hand-craft features (HCF) from training computed tomography images for small renal tumors (SRM), each including renal cell carcinoma (ccRCC), and hand-craft features from test computed tomography images for SRM. Hand-craft feature extraction unit for extracting the field (HCF); After generating texture image patches (TIP) from the training computed tomography images and extracting deep features (DF) using a pre-trained neural network model, texture image patches (TIP) from the test computed tomography images A deep feature extractor for extracting the deep features DF using a pre-trained neural network model after generating a); And generating a classification model by linking the hand-craft features and the deep features extracted from the training computed tomography images, and connecting the classification and the hand-craft features and the deep features extracted from the test computed tomography images. Based on the model, a small renal tumor (SRM) of the test computed tomography (SRM) includes a classification unit for classifying hemangiomyeloma or clear cell renal cell carcinoma which does not include grossly identified fat.
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