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Classification of milk duct carcinoma in the scans of diagnostic specimens is an important medical problem. Before the classification is performed, the regions of milk ducts which will be the regions of interest (ROI) should be detected. One of the approaches to such detection is to segment the image into ROIs and the remaining regions. The segmentation by clusterization with the classical K-means...
The paper presents an application of transfer learning using convolutional neural network (CNN) in recognition of the drill state on the basis of hole images drilled in the laminated chipboard. Three classes are recognized: red, yellow and green, which correspond with 3 stages of drill state. Red class indicates the drill, which is worn out and should be replaced immediately in drilling process. Yellow...
This paper presents an automatic algorithm to recognize the condition of drills on the basis of analysis of the drilling hole images. The algorithm includes the image preprocessing leading to extraction of the diagnostic features, which are used as the input attributes for the classification system. The condition of drill is classified into two groups: the useful one (the sharp enough state) and worn...
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