Optimized FPGA Implementation of the Thyroid Hormone Secretion Mechanism Using CAD Tools.

The goal of this paper is to implement the secretion mechanism of the Thyroid Hormone (TH) based on bio-mathematical differential eqs. (DE) on an FPGA chip. Hardware Descriptive Language (HDL) is used to develop a behavioral model of the mechanism derived from the DE. The Thyroid Hormone secretion mechanism is simulated with the interaction of the related stimulating and inhibiting hormones. Synthesis of the simulation is done with the aid of CAD tools and downloaded on a Field Programmable Gate Arrays (FPGAs) Chip. The chip output shows identical behavior to that of the designed algorithm through simulation. It is concluded that the chip mimics the Thyroid Hormone secretion mechanism. The chip, operating in real-time, is computer-independent stand-alone system.

Multiclass tumor identification using combined texture and statistical features.

Early detection and diagnosis of brain tumors are essential for early intervention and eventually successful treatment plans leading to either a full recovery or an increase in the patient lifespan. However, diagnosis of brain tumors is not an easy task since it requires highly skilled professionals, making this procedure both costly and time-consuming. The diagnosis process relying on MR images gets even harder in the presence of similar objects in terms of their density, size, and shape. No matter how skilled professionals are, their task is still prone to human error. The main aim of this work is to propose a system that can automatically classify and diagnose glioma brain tumors into one of the four tumor types: (1) necrosis, (2) edema, (3) enhancing, and (4) non-enhancing. In this paper, we propose a combined texture discrete wavelet transform (DWT) and statistical features based on the first- and second-order features for the accurate classification and diagnosis of multiclass glioma tumors. Four well-known classifiers, namely, support vector machines (SVM), random forest (RF), multilayer perceptron (MLP), and naïve Bayes (NB), are used for classification. The BraTS 2018 dataset is used for the experiments, and with the combined DWT and statistical features, the RF classifier achieved the highest average accuracy whether for separated modalities or combined modalities. The highest average accuracy of 89.59% and 90.28% for HGG and LGG, respectively, was reported in this paper. It has also been observed that the proposed method outperforms similar existing methods reported in the extant literature.

Intelligent Security and Privacy of Electronic Health Records Using Biometric Images.

BACKGROUND: In the presence of Cloud Environment and the migration of Electronic Health Systems and records to the Cloud, patient privacy has become an emergent problem for healthcare institutions. Government bylaws, electronic health documentation, and innovative internet health services generate numerous security issues for healthcare conformity and information security groups. To deal with these issues, healthcare institutes must protect essential IT infrastructure from unauthorized use by insiders and hackers. The Cloud Computing archetype allows for EHealth methods that improve the features and functionality of systems on the cloud. On the other hand, sending patients' medical information and records to the Cloud entails a number of risks in the protection and privacy of the health records during the communication process. AIM: In this paper, a solution is proposed for the security of Electronic Health Records (EHRs) in cloud environment during the process of sending the data to the cloud. In addition, the proposed method uses biometric images that allow for unified patient identification across cloud-based EHRs and across medical institutions. METHOD: To protect the privacy of patients' information and streamline the migration process, a watermarking-based method is proposed for health care providers to ensure that patients' data are only accessible to authorized personnel. Patients' information, such as name, id, symptoms, diseases, and previous history, is secured in biometric images of patients as an encrypted watermark. RESULTS: Quality and impeccability analysis and robustness were performed to test the proposed method. The PSNR values show that the proposed method produced excellent results. CONCLUSION: The robustness and impressibility of the proposed method were tested by subjecting the watermarked images to different simulated attacks. The watermarks were largely impermeable to varied and repeated attacks.