In this article, a study of various medical imaging modalities and survey of computer aided detection (CAD) approaches used for the detection of breast cancer are presented. Early breast cancer detection, is important as it can increase the rate of diagnosis, cure and survival of the affected women. Medical imaging techniques can detect early signs of breast cancer. They are also used to diagnose, stage breast cancer and guide treatments. This article explains the imaging procedure, analysis of images, benefits and limitations of standard medical imaging techniques such as mammography, ultrasound, magnetic resonance imaging, and thermography. Also some of modern imaging techniques are discussed here. Comparative study of mammography, ultrasound, thermography and magnetic resonance imaging describes the benefits of thermography in early detection of breast cancer. CAD provides a fast, reliable, and cost-effective second opinion to support doctors with the diagnostic management of breast cancer. This article reviews various CAD algorithms developed and studied for mammography, ultrasound and thermography. From the literature review, it is observed that accuracy of detection of breast abnormalities using CAD are decided by many factors, such as size of database, image quality, segmentation of region of interest, extracted features and classifiers used, etc.

It is challenging to maintain normoglycemic range of glucose concentration in type I diabetic patients. In this study H` control is applied for insulin delivery to prevent the hyperglycemic levels in a type I diabetic patient. From a ?control theory point of view, the blood glucose regulation problem is reformulated as a tracking one. A glucose ?tolerance curve (GTC) validated from several patients is used as reference model. Intra- and inter-patient ?variability poses a ?challenging task to control blood glucose concentration in diabetic patients. We develop a data based ?robust ?controller to control blood glucose concentration in type I diabetic (TIDM) patients in the presence of meal ?disturbances under patient-model mismatch. Simulation studies are performed on the diabetic patient model under feedback control. It is seen that the proposed control strategy is able to control blood glucose concentration well within the acceptable limits and also compensate for slow parametric drifts.

This study elaborates on the design of artificial pancreas using model predictive control algorithm for a comprehensive physiological model such as the Sorensen model, which regulates the blood glucose and can have a longer control time in normal glycaemic region. The main objective of the proposed algorithm is to eliminate the risk of hyper and hypoglycaemia and have a precise infusion of hormones: insulin and glucagon. A single model predictive controller is developed to control the bihormones, insulin, and glucagon for such a development unmeasured disturbance is considered for a random time. The simulation result for the proposed algorithm performed good regulation lowering the hypoglycaemia risk and maintaining the glucose level within the normal glycaemic range. To validate the performance of the tracking of output and setpoint, average tracking error is used and 4.4 mg/dl results are obtained while compared with standard value (14.3 mg/dl).