{'fraction_non_alphanumeric': 0.027912621359223302, 'fraction_numerical': 0.0024271844660194173, 'mean_word_length': 5.246212121212121, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Grey Relational Analysis based Keypoints Selection in Bag-of-Features for Histopathological Image Classification



With the expeditious development of current medical imaging technology,
the availability of histopathological images has been increased in a large number. Hence, histopathological
image classification and annotation have emerged as the prime research fields in the pathological
diagnosis and clinical practices. Several methods are available for the automation of image
classification.



 Recently, the bag-of-features appeared as a successful histopathological image classification
method. However, all the extracted keypoints in bag-of-features are not relevant and generally
have very high dimensions, which degrade the performance of a classifier. Therefore, this paper introduces
a new Grey relational analysis-based bag-of-features method to search the relevant keypoints.



The efficacy of the proposed method has been analyzed on animal diagnostics lab histopathological
image datasets having healthy and inflamed images of three organs. The average
accuracy of the proposed method is 88.3%, which is the highest among other state-of-the-art methods.



This paper introduced a new Grey relational analysis-based bag-of-features which
improves the efficiency of vector quantization step of the standard bag-of-features method. The
method used Grey relational analysis for similarity measure in vector quantization method of bag-offeatures.
The proposed method has been validated in terms of precision, recall, G-mean, F1 score,
and radar charts on three datasets, Kidney, Lung, and Spleen of ADL histopathological images.
