Abstract-The well-known Gleason grading method for an H&E prostatic carcinoma tissue image uses morphological features of histology patterns within a tissue slide to classify it into 5 grades. We have developed an automated gland segmentation and classification method that will be used for automated Gleason grading of a prostatic carcinoma tissue image. We demonstrate the performance of the proposed classification system for a three-class classification problem (benign, grade 3 carcinoma and grade 4 carcinoma) on a dataset containing 78 tissue images and achieve a classification accuracy of 88.84% . In comparison to the other segmentation-based methods, our approach combines the similarity of morphological patterns associated with a grade with the domain knowledge such as the appearance of nuclei and blue mucin for the grading task.

No abstract

No abstract

Articles you may be interested inOrigin of electrical signals for plasma etching end point detection: Comparison of end point signals and electron density J. Vac. Sci. Technol. A 30, 051303 (2012); 10.1116/1.4737615 Prediction of silicon oxynitride plasma etching using a generalized regression neural network J. Appl. Phys. 98, 034912 (2005); 10.1063/1.2001155Qualitative modeling of silica plasma etching using neural network A popular technique for ending a plasma etch process is to monitor the optical emissions from reaction species for gross changes. This at first appears to be a simple real-time edge detection task. However, differences in plasma chemistry across runs, reactor chambers, and wafer patterns combine to make this control strategy quite problematic. Moreover, as exposed wafer areas continue to shrink, distinct end points become increasingly harder to identify. This new approach is simple, robust, and flexible. The application user trains elementary pattern detectors, or neurons, on a few representative process end point shapes. Software algorithms process the training end point data to find a range of resolutions for pattern recognition. The application then automatically builds a pattern detection network that tolerates signal noise, adapts to changing end point shape, and precisely registers process end points. The neural network end point detector has been successfully tested on a wide variety of data gathered over a period of many months. The application is currently calling end point in real time on several oxide etch processes at a major wafer fabrication facility.

No abstract