Breast Cancer Histopathological Images Recognition Based on Low Dimensional Three-Channel Features

Abstract

Breast cancer (BC) is the primary threat to women’s health, and early diagnosis of breast cancer is imperative. Although there are many ways to diagnose breast cancer, the gold standard is still pathological examination. In this paper, a low dimensional three-channel features based breast cancer histopathological images recognition method is proposed to achieve fast and accurate breast cancer benign and malignant recognition. Three-channel features of 10 descriptors were extracted, which are gray level co-occurrence matrix on one direction (GLCM1), gray level co-occurrence matrix on four directions (GLCM4), average pixel value of each channel (APVEC), Hu invariant moment (HIM), wavelet features, Tamura, completed local binary pattern (CLBP), local binary pattern (LBP), Gabor, histogram of oriented gradient (Hog), respectively. Then support vector machine (SVM) was used to assess their performance. Experiments on BreaKHis dataset show that GLCM1, GLCM4 and APVEC achieved the recognition accuracy of 90.2%-94.97% at the image level and 89.18%-94.24% at the patient level, which is better than many state-of-the-art methods, including many deep learning frameworks. The experimental results show that the breast cancer recognition based on high dimensional features will increase the recognition time, but the recognition accuracy is not greatly improved. Three-channel features will enhance the recognizability of the image, so as to achieve higher recognition accuracy than gray-level features.

In vitroandin vivoevidences propound therapeutic potential of Lipocalin 2 in cervical carcinoma

“…Texture features of whole-slide histopathology images such as entropy, dissimilarity and contrast have been correlated in literature with clinical end-points or molecular subtypes in multiple cancers including breast 47 , oral 48 and multiple other types of cancers 49 . In this analysis, the three features - entropy, dissimilarity and contrast, presented with higher values in images from treated relative to untreated tumors (figure 3c) indicating emergence of disorder due to necrosis of cells in treated samples.…”

Breast cancer histopathological images classification based on deep semantic features and gray level co-occurrence matrix

“…In 1973, Haralick et al [ 42 ] proposed using GLCM to describe texture features. The excellent ability of GLCM in breast cancer histopathological images recognition, especially for the three-channel features of the images have been discovered in [ 5 ]. In this paper, three-channel features are considered.…”

“…Belsare et al [ 4 ] extracted features such as GLCM, graph run length matrix and Euler number for breast cancer histopathological images recognition. In our previous work [ 5 ], we explored the application of 9 feature descriptors such as GLCM in breast cancer histopathological image recognition. Anuranjeeta et al [ 6 ] proposed a breast cancer recognition method based on morphological features.…”

An improved breast cancer classification with hybrid chaotic sand cat and Remora Optimization feature selection algorithm

“…In 2021, Hao et al . [ 26 ] developed a low-dimensional three-channel feature-based technique for the identification of breast cancer pathology images. Ten descriptors, including GLCM1, GLCM4, APVEC, HIM, wavelet features, Tamura, CLBP, LBP, Gabor, and Hog, were extracted.…”