Clinical Decision Support Systems (CDSS) provide an efficient way to diagnose the presence of diseases such as breast cancer using ultrasound images (USIs). Globally, breast cancer is one of the major causes of increased mortality rates among women. Computer-Aided Diagnosis (CAD) models are widely employed in the detection and classification of tumors in USIs. The CAD systems are designed in such a way that they provide recommendations to help radiologists in diagnosing breast tumors and, furthermore, in disease prognosis. The accuracy of the classification process is decided by the quality of images and the radiologist’s experience. The design of Deep Learning (DL) models is found to be effective in the classification of breast cancer. In the current study, an Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification (EDLCDS-BCDC) technique was developed using USIs. The proposed EDLCDS-BCDC technique was intended to identify the existence of breast cancer using USIs. In this technique, USIs initially undergo pre-processing through two stages, namely wiener filtering and contrast enhancement. Furthermore, Chaotic Krill Herd Algorithm (CKHA) is applied with Kapur’s entropy (KE) for the image segmentation process. In addition, an ensemble of three deep learning models, VGG-16, VGG-19, and SqueezeNet, is used for feature extraction. Finally, Cat Swarm Optimization (CSO) with the Multilayer Perceptron (MLP) model is utilized to classify the images based on whether breast cancer exists or not. A wide range of simulations were carried out on benchmark databases and the extensive results highlight the better outcomes of the proposed EDLCDS-BCDC technique over recent methods.

Whole-grain cereal breakfast consumption has been associated with beneficial effects on glucose and insulin metabolism as well as satiety. Pearl millet is a popular ancient grain variety that can be grown in hot, dry regions. However, little is known about its health effects. The present study investigated the effect of a pearl millet porridge (PMP) compared with a well-known Scottish oats porridge (SOP) on glycaemic, gastrointestinal, hormonal and appetitive responses. In a randomised, two-way crossover trial, twenty-six healthy participants consumed two isoenergetic/isovolumetric PMP or SOP breakfast meals, served with a drink of water. Blood samples for glucose, insulin, glucagon-like peptide 1, glucose-dependent insulinotropic polypeptide (GIP), peptide YY, gastric volumes and appetite ratings were collected 2 h postprandially, followed by an ad libitum meal and food intake records for the remainder of the day. The incremental AUC (iAUC2h) for blood glucose was not significantly different between the porridges (P > 0·05). The iAUC2h for gastric volume was larger for PMP compared with SOP (P = 0·045). The iAUC2h for GIP concentration was significantly lower for PMP compared with SOP (P = 0·001). Other hormones and appetite responses were similar between meals. In conclusion, the present study reports, for the first time, data on glycaemic and physiological responses to a pearl millet breakfast, showing that this ancient grain could represent a sustainable alternative with health-promoting characteristics comparable with oats. GIP is an incretin hormone linked to TAG absorption in adipose tissue; therefore, the lower GIP response for PMP may be an added health benefit.