Multi-label classification for colon cancer using histopathological images

Abstract

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.

Radiomics Evaluation of Histological Heterogeneity Using Multiscale Textures Derived From 3D Wavelet Transformation of Multispectral Images

“…Previous publications have demonstrated that combined radiomic features can offer good performance for multi-label colon cancer prediction, with a precision of 73.7% ( 41 ). However, colon cancer diagnosis and prognosis depends on the ability to discriminate between the distinct malignancy states which can exist ( 42 ), oftentimes within an individual tumor section.…”

Texture Analysis of Abnormal Cell Images for Predicting the Continuum of Colorectal Cancer

“…More recently, multilabel classification of colon cancer using histopathological images was performed using several types of features. It was concluded that combined features can offer good performance for multilabel colon cancer prediction, with a precision of 73.7% [ 32 ]. Moreover, another study has proven that colon cancer prognosis can be identified by using distinct molecular subtypes and serrated precursor lesions [ 33 ].…”

Multi‐iPPseEvo: A Multi‐label Classifier for Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into Chou′s General PseAAC via Grey System Theory

“…Considering the fact that several predictors which can deal with both single and multiple type problems have been established, much work should made in this area. For example, there are many computational methods which were proposed for predicting protein subcellular localization, distinguishing functional genomics and text categorization; sentiment classification; classifying colon cancer; recognizing protein function . Encouraged by the success of multi‐Label classification and ensemble teachnique, the present study developed a phosphorylation protein predictor, called Multi‐iPPseEvo, specialized for human proteins by improving the aforementioned shortcomings.…”