Deep Learned Nucleus Features for Breast Cancer Histopathological Image Analysis based on Belief Theoretical Classifier Fusion

Machine Learning Methods for Histopathological Image Analysis: A Review

“…The latter stage extracted both global and local features of breast cancer tumors. George et al [69] proposed an approach for breast cancer diagnosis, which extracts features from nuclei based on CNNs. The methodology consists of different approaches for extracting nucleus features from HIs and select the most discriminative spatially sparse nucleus patches.…”

“…Graph-based Balazsi et al [60] 2016 Breast LBP Fukuma et al [46] 2016 Brain Object, spatial Leo et al [70] 2016 Prostate Graph-based, shape, entropy, subgraph connectivity, texture Phoulady et al [57] 2016 Uterus HOG, LBP Bruno et al [56] 2016 Breast Curvelet transform, LBP Noroozi and Zakerolhosseini [63] 2016 Skin Z-transform coefficients Niazi et al [66] 2016 Bladder Morphometric Yu et al [71] 2016 Lung Quantitative, texture Chan and Tuszynski [65] 2016 Breast Fractal dimension Kwak and Hewitt [50] 2017 Prostate Morphometric Reis et al [58] 2017 Breast BIF, LBP Mazo et al [12] 2017 Cardiac LBP, Haralick Wan et al [64] 2017 Breast Wavelet transform, Gaussian distribution, Symmetric alpha-stable Spanhol et al [67] 2017 Breast Deep Das et al [85] 2017 Oral Hu's moment, fractal dimension, entropy Pang et al [73] 2017 Lung LBP, GLCM, Tamura, SIFT, global, morphometric Kruk et al [74] 2017 Kidney Morphometric, textural, and statistical Peyret et al [55] 2018 Prostate LBP Vo et al [68] 2019 Breast Deep George et al [69] 2019 Breast Deep…”

A Review of Computer-Aided Expert Systems for Breast Cancer Diagnosis

“…The malignant type of breast tumour consists of ductal carcinoma (DC), lobular carcinoma (LC), mucinous carcinoma (MC), and papillary carcinoma (PC). This dataset is the most used dataset by many researchers for CAD breast cancer in histopathology images [ 11 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. This dataset can be obtained from (accessed on 16 March 2021).…”

“…Vo et al proposed a model called Inception-ResNet-v2 that combines CNNs of Inception and ResNet to train and extract visual features from multi-scale images to achieve both global and local features from breast tumours and feed them into a gradient boosting classifier [ 18 ]. George et al proposed an approach for breast cancer diagnosis, which extracts features from nuclei based on a pre-trained set of CNN, namely, AlexNet, ResNet-18, and ResNet-50, on random patches obtained from histology images and finally classifies them with a SVM classifier [ 29 ]. Another study by Spanhol et al proposed a method that combines a modified AlexNet and DeCAF [ 148 ] (or deep) features extraction that is based on reusing a previously trained CNN only as feature vectors, which is then used as input for a classifier trained only for the new classification task [ 149 ].…”

“…In medical imaging diagnostic tests, sensitivity and specificity parameters are widely used to evaluate performance [ 40 ]. Other commonly used metrics for diagnosis evaluation are F1-measure [ 19 , 23 , 59 , 84 , 148 , 151 ], precision [ 19 , 59 , 60 , 148 ], accuracy [ 11 , 18 , 19 , 20 , 21 , 22 , 24 , 25 , 26 , 27 , 28 , 29 , 31 , 53 , 60 , 83 , 128 , 130 , 131 ] and receiver operating characteristics (ROC). The parameters are mostly calculated depending on the true positive (TP), true negative (TN), false positive (FP), and false negative (FN), which are the numbers of pixels corresponding to the parameters.…”