In the last decade, healthcare systems have played an effective role in improving medical services by monitoring and diagnosing patients' health remotely. These systems, either in hospitals or in other health centers, have experienced significant growth with emerging technologies. They are becoming of great interest to many countries worldwide nowadays. Portable healthcare monitoring systems (HMS) depend on internet of things (IoT) technology due to its effectiveness and reliability in several sectors, as well as in the sector of telemedicine. This paper proposes a portable healthcare system in an IoT environment controllable via a smartphone application that aims to facilitate utilization. This proposed system can track physiological indicators of a patient's body as well as the environmental conditions where the patient lives in real-time and auto-manage databases. Moreover, this paper touched on a comparison between three servers, concerning data transfer speeds from the proposed system into the servers.

Brain tumor segmentation is the process of separating the tumor from normal brain tissues; in clinical routine, it provides useful information for diagnosis and treatment planning. However, it is still a challenging task due to the irregular form and confusing boundaries of tumors. Tumor cells thermally represent a heat source; their temperature is high compared to normal brain cells. The main aim of the present paper is to demonstrate that thermal information of brain tumors can be used to reduce false positive and false negative results of segmentation performed in MRI images. Pennes bioheat equation was solved numerically using the finite difference method to simulate the temperature distribution in the brain; Gaussian noises of ±2% were added to the simulated temperatures. Canny edge detector was used to detect tumor contours from the calculated thermal map, as the calculated temperature showed a large gradient in tumor contours. The proposed method is compared to Chan–Vese based level set segmentation method applied to T1 contrast-enhanced and Flair MRI images of brains containing tumors with ground truth. The method is tested in four different phantom patients by considering different tumor volumes and locations and 50 synthetic patients taken from BRATS 2012 and BRATS 2013. The obtained results in all patients showed significant improvement using the proposed method compared to segmentation by level set method with an average of 0.8% of the tumor area and 2.48% of healthy tissue was differentiated using thermal images only. We conclude that tumor contours delineation based on tumor temperature changes can be exploited to reinforce and enhance segmentation algorithms in MRI diagnostic.

In this paper, we propose a parallel algorithm for data classification, and its application for Magnetic Resonance Images (MRI) segmentation. The studied classification method is the well-known c-means method. The use of the parallel architecture in the classification domain is introduced in order to improve the complexities of the corresponding algorithms, so that they will be considered as a pre-processing procedure. The proposed algorithm is assigned to be implemented on a parallel machine, which is the reconfigurable mesh computer (RMC). The image of size (mxn) to be processed must be stored on the RMC of the same size, one pixel per processing element (PE).

atherosclerosis disease is the major reason that increases the mortality rate around the world. This ailment diagnosis becomes difficult due to the inattention of initial symptoms. Thus, there is a need to improve the prediction accuracy of cardiovascular diseases in order to minimize treatment costs and to avoid critical cases. This contribution is articulated to define a medical decision support system (MDSS) of atherosclerosis disease. This system is able to give heart disease prediction using the patient's clinical data. This MDSS is based on machine learning techniques such as K-medoids and k-means clustering for classification, Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN) for prediction the presence and the absence of Atherosclerosis disease. The system is validated on the Cleveland heart diseases database. This data is split into training and testing data with 13 features for each patient. We evaluated the proposed system using many similarity measures such as Matthews's correlation coefficient (MCC). We reached 96% accuracy and 0.92 MCC, significantly outperforms currently published research.