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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.

Application in the field of medical development has always been one of the most important research areas. One of these medical applications is the early prediction system for heart diseases especially; coronary artery disease (CAD) also called atherosclerosis. The need for a medical diagnosis support system is to detect atherosclerosis at the earlier stages to optimize the diagnosis, avoid the advanced cases, and reduce treatment costs. Earlier, the datasets are collected from specific medical sources and have evaluated against computer applications. In this paper, a supervised machine learning medical diagnosis support system (MDSS) for atherosclerosis prediction is presented that able to obtain and learn automatically knowledge from each patient's clinical data. Therefore, we used three Machine Learning (ML) classifiers for the proposed MDSS for atherosclerosis. Thus, this work is accomplished using databases collected from the UCI repository (Cleveland, Hungarian) and Sani Z-Alizadeh dataset. The performance metrics were computed utilizing Accuracy, Recall and Precision. Furthermore, F1-score and Matthews's correlation coefficient these measures were also calculated to greatly increase the proposed system performance. Additionally, 10-fold cross-validation methods have been used for proposed model performance evaluation that achieved 94% as the best accuracy average. Consequently, the proposed model can be used to support healthcare and facilitate largescale clinical diagnostic of atherosclerosis diseases.

The goal of this present paper is to design, analysis the influence of the inductor geometrical parameters and the effect of the metal thickness on the quality factor-Q in integrated square spiral inductor using an efficient application of the artificial bee colony (ABC) algorithm. The inductors were optimized at 2.4 GHz to determinate their major geometrical dimensions (sp, w, din…) and their number of turns, for uses in radio-frequency integrated circuits (RFICs). The optimization results are validated by the simulation using an electromagnetic simulator (ADS-Momentum). Using matlab software, the study on the impact of the effect of geometrical parameters and the effect of metal thickness, on the factor of quality-Q of spiral inductors, is shown. We first reported that it is possible to improve Q-factors further by increasing the metal thickness, and in the design of inductor; a compromise must be reached between the value of w, n, sp and din to achieve the desired quality factor-Q and other electrical parameters.

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