Several researchers are trying to develop different computer‐aided diagnosis system for breast cancer employing machine learning (ML) methods. The inputs to these ML algorithms are labeled histopathological images which have complex visual patterns. So, it is difficult to identify quality features for cancer diagnosis. The pre‐trained Convolutional Neural Networks (CNNs) have recently emerged as an unsupervised feature extractor. However, a limited investigation has been done for breast cancer recognition using histopathology images with CNN as a feature extractor. This work investigates ten different pre‐trained CNNs for extracting the features from breast cancer histopathology images. The breast cancer histopathological images are obtained from publicly available BreakHis dataset. The classification models for the different feature sets, which are obtained using different pre‐trained CNNs in consideration, are developed using a linear support vector machine. The proposed method outperforms the other state of art methods for cancer detection, which can be observed from the results obtained.
Histopathology is considered as the gold standard for diagnosing breast cancer. Traditional machine learning (ML) algorithm provides a promising performance for cancer diagnosis if the training dataset is balanced. Nevertheless, if the training dataset is imbalanced the performance of the ML model is skewed toward the majority class. It may pose a problem for the pathologist because if the benign sample is misclassified as malignant, then a pathologist could make a misjudgment about the diagnosis. A limited investigation has been done in literature for solving the class imbalance problem in computer‐aided diagnosis (CAD) of breast cancer using histopathology. This work proposes a hybrid ML model to solve the class imbalance problem. The proposed model employs pretrained ResNet50 and the kernelized weighted extreme learning machine for CAD of breast cancer using histopathology. The breast cancer histopathological images are obtained from publicly available BreakHis and BisQue datasets. The proposed method achieved a reasonable performance for the classification of the minority as well as the majority class instances. In comparison, the proposed approach outperforms the state‐of‐the‐art ML models implemented in previous studies using the same training‐testing folds of the publicly accessible BreakHis dataset.
Three-week-old male Wistar rats, when maintained for 13 weeks on diets containing 19.3 wt% even or odd medium chain triglycerides (MCT) admixed with 4.9 wt% sunflowerseed oil (SFO), consumed comparable amounts of food in comparison with rats fed 24.2 wt% SFO. The growth of rats fed even MCT was better than those fed odd MCT but it was comparable to the growth of rats kept on SFO diet. Excretion of ketones in urine during the entire experimental period was observed in both MCT groups but more rats in the odd MCT group had ketonuria, which was probably why the blood alkali reserve in this latter group was lower. The data on total protein and albumin in plasma and glutamine oxaloacetic acid transaminase in urine indicated no metabolic derangement. The hematological data were also within physiological limits in the three groups. The histopathological study of the kidneys and liver of the MCT groups revealed no degenerative changes which might explain an increase in their relative weights. The other organs displayed no abnormalities.
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