Image processing lumbar spine to highlight the location of the herniated disc

Low back pain is a common disease. A common cause of this problem is a herniated disc in the lumbar spine. Lumbar disc herniation represents the displacement of the disc (annular fibrosis or medullary nuclei). While most cases, the pain will disappear in a few days to a few weeks; however, it can last for three months or more. Detection and diagnosis are the two most important tasks in a computer-aided diagnostic system. In this article, we use images taken from the results of the MRI imaging of the patient. Through the use of image inversion to highlight the position of degenerative discs. This result wishes to provide a simple and inexpensive diagnostic image processing method to help doctors quickly determine the degree of disc herniation, the status of lumbar discs, they can give the appropriate treatment to the patient.

A New Approach for Accurate Identification of Allele State Based on Real-Time PCR for Biomedical Tasks

Genotyping of single nucleotide polymorphisms (SNPs) is an important task in medicine, veterinary medicine and biology. Precise differentiation of SNPs can be challenging. Methods based onTaqman can lead to false positive results due to nonspecific annealing of the probe. The aim of this research was to develop a new approach for the accurate differentiation of SNPs based on real-time PCR with Taqmanprobes and their rivals.The rivals competed with the Taqmanprobes for annealing to the site. The rivals blocked the nonspecific allele so that the Taqmanprobe could not anneal to it. Thus,the Taqmanprobe only detected specific alleles.This approach madeit possible to fine-tune the diagnostic system by selecting the ratio of Taqmanprobes and rivals (in non-equimolar amounts too).The new approach was tested on several diagonally significant SNPs in veterinary medicine.Using Taqman probes and rival probes showed a significantly greater specificity and efficiency in the determination of both homozygotes and heterozygotes than when conventional systems based only on Taqmanwere used. Keywords: SNP, allele identification, real-time PCR, fluorescent dye

Classification of rat mammary carcinoma with large scale in vivo microwave measurements

Mammary carcinoma, breast cancer, is the most commonly diagnosed cancer type among women. Therefore, potential new technologies for the diagnosis and treatment of the disease are being investigated. One promising technique is microwave applications designed to exploit the inherent dielectric property discrepancy between the malignant and normal tissues. In theory, the anomalies can be characterized by simply measuring the dielectric properties. However, the current measurement technique is error-prone and a single measurement is not accurate enough to detect anomalies with high confidence. This work proposes to classify the rat mammary carcinoma, based on collected large-scale in vivo S$$_{11}$$ 11 measurements and corresponding tissue dielectric properties with a circular diffraction antenna. The tissues were classified with high accuracy in a reproducible way by leveraging a learning-based linear classifier. Moreover, the most discriminative S$$_{11}$$ 11 measurement was identified, and to our surprise, using the discriminative measurement along with a linear classifier an 86.92% accuracy was achieved. These findings suggest that a narrow band microwave circuitry can support the antenna enabling a low-cost automated microwave diagnostic system.

Smart Heart Disease Prediction System with IoT and Fog Computing Sectors Enabled by Cascaded Deep Learning Model

Chronic illnesses like chronic respiratory disease, cancer, heart disease, and diabetes are threats to humans around the world. Among them, heart disease with disparate features or symptoms complicates diagnosis. Because of the emergence of smart wearable gadgets, fog computing and “Internet of Things” (IoT) solutions have become necessary for diagnosis. The proposed model integrates Edge-Fog-Cloud computing for the accurate and fast delivery of outcomes. The hardware components collect data from different patients. The heart feature extraction from signals is done to get significant features. Furthermore, the feature extraction of other attributes is also gathered. All these features are gathered and subjected to the diagnostic system using an Optimized Cascaded Convolution Neural Network (CCNN). Here, the hyperparameters of CCNN are optimized by the Galactic Swarm Optimization (GSO). Through the performance analysis, the precision of the suggested GSO-CCNN is 3.7%, 3.7%, 3.6%, 7.6%, 67.9%, 48.4%, 33%, 10.9%, and 7.6% more advanced than PSO-CCNN, GWO-CCNN, WOA-CCNN, DHOA-CCNN, DNN, RNN, LSTM, CNN, and CCNN, respectively. Thus, the comparative analysis of the suggested system ensures its efficiency over the conventional models.

Upgrade of an integrated Langmuir probe system on the closed divertor target plates in the HL-2A tokamak

A newly designed divertor Langmuir probe diagnostic system has been installed in a rare closed divertor of the HL-2A tokamak and steadily operated for the study of divertor physics involved edge-localized mode (ELM) mitigation, detachment and redistribution of heat flux, etc. Two sets of probe arrays including 274 probe tips were placed at two ports (approximately 180° separated toroidally), and the spatial and temporal resolutions of this measurement system could reach 6 mm and 1 s, respectively. A novel design of the ceramic isolation ring can ensure reliable electrical insulation property between the graphite tip and the copper substrate plate where plasma impurities and the dust are deposited into the gaps for a long experimental time. Meanwhile, the condition monitoring and mode conversion between single and triple probe of the probe system could be conveniently implemented via a remote control station. The preliminary experimental result shows that the divertor Langmuir probe system is capable of measuring the high spatiotemporal parameters involved the plasma density, electron temperature, particle flux as well as heat flux during the ELMy H-mode discharges.

A Sensor-Based Perspective in Early-Stage Parkinson’s Disease: Current State and the Need for Machine Learning Processes

Parkinson’s disease (PD) is a progressive neurodegenerative disorder associated with dysfunction of dopaminergic neurons in the brain, lack of dopamine and the formation of abnormal Lewy body protein particles. PD is an idiopathic disease of the nervous system, characterized by motor and nonmotor manifestations without a discrete onset of symptoms until a substantial loss of neurons has already occurred, enabling early diagnosis very challenging. Sensor-based platforms have gained much attention in clinical practice screening various biological signals simultaneously and allowing researchers to quickly receive a huge number of biomarkers for diagnostic and prognostic purposes. The integration of machine learning into medical systems provides the potential for optimization of data collection, disease prediction through classification of symptoms and can strongly support data-driven clinical decisions. This work attempts to examine some of the facts and current situation of sensor-based approaches in PD diagnosis and discusses ensemble techniques using sensor-based data for developing machine learning models for personalized risk prediction. Additionally, a biosensing platform combined with clinical data processing and appropriate software is proposed in order to implement a complete diagnostic system for PD monitoring.

Estimation and Mitigation of Unknown Airplane Installation Effects on GPA Diagnostics

In gas turbines used for airplane propulsion, the number of sensors are kept at a minimum for accurate control and safe operation. Additionally, when data are communicated between the airplane main computer and the various subsystems, different systems may have different constraints and requirements regarding what data transmit. Early in the design process, these parameters are relatively easy to change, compared to a mature product. If the gas turbine diagnostic system is not considered early in the design process, it may lead to diagnostic functions having to operate with reduced amount of data. In this paper, a scenario where the diagnostic function cannot obtain airplane installation effects is considered. The installation effects in question is air intake pressure loss (pressure recovery), bleed flow and shaft power extraction. A framework is presented where the unknown installation effects are estimated based on available data through surrogate models, which is incorporated into the diagnostic framework. The method has been evaluated for a low-bypass turbofan with two different sensor suites. It has also been evaluated for two different diagnostic schemes, both determined and underdetermined. Results show that, compared to assuming a best-guess constant-bleed and shaft power, the proposed method reduce the RMS in health parameter estimation from 26% up to 80% for the selected health parameters. At the same time, the proposed method show the same degradation pattern as if the installation effects were known.