A Parallel System for Performing Colonic Tissue Classification by Means of a Genetic Algorithm

Amin, Saad; Filippas, John; Naguib, R.N.G.; Bennett, Mark K.

doi:10.1007/978-3-540-39924-7_75

Cited by 1 publication

(2 citation statements)

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“…Shuttleworth et al (2002) combined color and texture information of both high and low resolution images based on discriminate analysis method. Amin et al (2003) realized multiclassification of colon cancer images by means of genetic algorithm, but the experiment result contained some large diversities. The author believed such shortage is due to the distinct differentiation degrees of two examples.…”

Section: Related Workmentioning

confidence: 99%

“…Even though large amount of colon cancer classification relative researches are now being implemented (Altunbay et al, 2010;Amin et al, 2003;Esgiar et al, 1998;Nwoye et al, 2006;Shuttleworth et al, 2002;Xu et al, 2012), they are mostly single-label. Our problem is extraordinarily similar to those that multi-label is appropriate for, therefore we adopt this method into application.…”

Section: Related Workmentioning

confidence: 99%

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Multi-label classification for colon cancer using histopathological images

Jiao

Wang

et al. 2013

Microsc. Res. Tech.

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Colon cancer classification has a significant guidance value in clinical diagnoses and medical prognoses. The classification of colon cancers with high accuracy is the premise of efficient treatment. Our task is to build a system for colon cancer detection and classification based on slide histopathological images. Some former researches focus on single label classification. Through analyzing large amount of colon cancer images, we found that one image may contain cancer regions of multiple types. Therefore, we reformulated the task as multi-label problem. Four kinds of features (Color Histogram, Gray-Level Co-occurrence Matrix, Histogram of Oriented Gradients and Euler number) were introduced to compose our discriminative feature set, extracted from our dataset that includes six single categories and four multi-label categories. In order to evaluate the performance and make comparison with our multi-label model, three commonly used multi-classification methods were designed in our experiment including one-against-all SVM (OAA), one-against-one SVM (OAO) and multi-structure SVM. Four indicators (Precision, Recall, F-measure, and Accuracy) under 3-fold cross-validation were used to validate the performance of our approach. Experiment results show that the precision, recall and F-measure of multi-label method as 73.7%, 68.2%, and 70.8% with all features, which are higher than the other three classifiers. These results demonstrate the effectiveness and efficiency of our method on colon histopathological images analysis.

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Section: Related Workmentioning

confidence: 99%