19 May 2016 Automated classification of histopathology images of prostate cancer using a Bag-of-Words approach
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Abstract
The goals of this paper are (1) test the Computer Aided Classification of the prostate cancer histopathology images based on the Bag-of-Words (BoW) approach (2) evaluate the performance of the classification grade 3 and 4 of the proposed method using the results of the approach proposed by the authors Khurd et al. in [9] and (3) classify the different grades of cancer namely, grade 0, 3, 4, and 5 using the proposed approach. The system performance is assessed using 132 prostate cancer histopathology of different grades. The system performance of the SURF features are also analyzed by comparing the results with SIFT features using different cluster sizes. The results show 90.15% accuracy in detection of prostate cancer images using SURF features with 75 clusters for k-mean clustering. The results showed higher sensitivity for SURF based BoW classification compared to SIFT based BoW.
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Foram M. Sanghavi, Sos S. Agaian, "Automated classification of histopathology images of prostate cancer using a Bag-of-Words approach", Proc. SPIE 9869, Mobile Multimedia/Image Processing, Security, and Applications 2016, 98690T (19 May 2016); doi: 10.1117/12.2224389; https://doi.org/10.1117/12.2224389
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