Pretrained DcAlexnet Cardiac Diseases Classification on Cognitive Multi-Lead Ultrasound Dataset

The DcAlexNet CNN deep learning classifier can easily track patterns in medical images (brain, heart, spinal cord and etc.) precisely. According to WHO (world health organization) every year 5 billion people are affecting heart diseases and heart-attacks. Heart abnormalities sometimes tends to death; therefore, an efficient medical image pre-processor and deep learning classifier is needed for diagnosis. So that in this research work multi-class DcAlexNet classifier, RRS-HSB segment-filter has been implemented. The RRS (Restrictive Random segmentation) and GHSB (Gaussian Hue saturation brightness filtration) modules are fused to get multi-level feature. The training process has been incorporated to EchoNet dataset and testing process can be verified to real time samples. The segmented features as well as filtered feature are loaded into weighted .CSV file. The following features are classified tends to get predicting abnormalities in heart ultra sound image. The pretrained DcAlexNet CNN model is loading to EchoNet 1 lakh samples using 165 layers such as normalized layer, dense layer, flatten layer, max pooling layer and ReLu layer. The computer aided design with corresponding CNN layers has been finding hidden sample over to get heart abnormality location. The experimental results in terms of Dice score 98.89%, Accuracy 99.455, precision 99.23%, recall 98.34%, F-1 score 98.92%, CC 99.27%, and sensitivity 99.34% had been attained. The attained performance metrics are competed with present technologies and outperformance the application accuracy on heart diagnosis

Pretrained DcAlexnet Cardiac Diseases Classification on Cognitive Multi-Lead Ultrasound Dataset

The DcAlexNet CNN deep learning classifier can easily track patterns in medical images (brain, heart, spinal cord and etc.) precisely. According to WHO (world health organization) every year 5 billion people are affecting heart diseases and heart-attacks. Heart abnormalities sometimes tends to death; therefore, an efficient medical image pre-processor and deep learning classifier is needed for diagnosis. So that in this research work multi-class DcAlexNet classifier, RRS-HSB segment-filter has been implemented. The RRS (Restrictive Random segmentation) and GHSB (Gaussian Hue saturation brightness filtration) modules are fused to get multi-level feature. The training process has been incorporated to EchoNet dataset and testing process can be verified to real time samples. The segmented features as well as filtered feature are loaded into weighted .CSV file. The following features are classified tends to get predicting abnormalities in heart ultra sound image. The pretrained DcAlexNet CNN model is loading to EchoNet 1 lakh samples using 165 layers such as normalized layer, dense layer, flatten layer, max pooling layer and ReLu layer. The computer aided design with corresponding CNN layers has been finding hidden sample over to get heart abnormality location. The experimental results in terms of Dice score 98.89%, Accuracy 99.455, precision 99.23%, recall 98.34%, F-1 score 98.92%, CC 99.27%, and sensitivity 99.34% had been attained. The attained performance metrics are competed with present technologies and outperformance the application accuracy on heart diagnosis

Heterozygosity for Fibrinogen Results in Efficient Resolution of Kidney Ischemia Reperfusion Injury

Fibrinogen (Fg) has been recognized to play a central role in coagulation, inflammation and tissue regeneration. Several studies have used Fg deficient mice (Fg−/−) in comparison with heterozygous mice (Fg+/−) to point the proinflammatory role of Fg in diverse pathological conditions and disease states. Although Fg+/− mice are considered ‘normal’, plasma Fg is reduced to ∼75% of the normal circulating levels present in wild type mice (Fg+/+). We report that this reduction in Fg protein production in the Fg+/− mice is enough to protect them from kidney ischemia reperfusion injury (IRI) as assessed by tubular injury, kidney dysfunction, necrosis, apoptosis and inflammatory immune cell infiltration. Mechanistically, we observed binding of Fg to ICAM-1 in kidney tissues of Fg+/+ mice at 24 h following IRI as compared to a complete absence of binding observed in the Fg+/− and Fg−/− mice. Raf-1 and ERK were highly activated as evident by significantly higher phosphorylation in the Fg+/+ kidneys at 24 h following IRI as compared to Fg+/− and Fg−/− mice kidneys. On the other hand Cyclin D1 and pRb, indicating higher cell proliferation, were significantly increased in the Fg+/− and Fg−/− as compared to Fg+/+ kidneys. These data suggest that Fg heterozygosity allows maintenance of a critical balance of Fg that enables regression of initial injury and promotes faster resolution of kidney damage

Highly transparent and reproducible nanocrystalline ZnO and AZO thin films grown by room temperature pulsed-laser deposition on flexible zeonor plastic substrates

Zeonor plastics are highly versatile due to exceptional optical and mechanical properties which make them the choice material in many novel applications. For potential use in flexible transparent optoelectronic applications, we have investigated Zeonor plastics as flexible substrates for the deposition of highly transparent ZnO and AZO thin films. Films were prepared by pulsed laser deposition at room temperature in oxygen ambient pressures of 75, 150 and 300 mTorr. The growth rate, surface morphology, hydrophobicity and the structural, optical and electrical properties of as grown films with thicknesses∼65–420 nm were recorded for the three oxygen pressures. The growth rates were found to be highly linear both as a function of film thickness and oxygen pressure, indicating high reproducibility. All the films were optically smooth, hydrophobic and nanostructured with lateral grain shapes of∼150 nm wide. This was found compatible with the deposition of condensed nanoclusters, formed in the ablation plume, on a cold and amorphous substrate. Films were nanocrystalline (wurtzite structure), c-axis oriented, with average crystallite size∼22 nm for ZnO and∼16 nm for AZO. In-plane compressive stress values of 2–3 GPa for ZnO films and 0.5 GPa forAZO films were found. Films also displayed high transmission greater than 95% in some cases, in the 400–800 nmwavelength range. The low temperature photoluminescence spectra of all the ZnO and AZO films showed intense near band edge emission. A considerable spread from semi-insulating to n-type conductive was observed for the films, with resistivity∼103 Ω cm and Hall mobility in 4–14 cm2 V−1 s−1 range, showing marked dependences on film thickness and oxygen pressure. Applications in the fields of microfluidic devices and flexible electronics for these ZnO and AZO films are suggested

Statistical analysis of spinal cord injury severity detection on high dimensional MRI data

Staggered Segmenting on the programmed spinal rope form is a vital advance for evaluating spinal line decay in different infections. Outlining dark issue (GM) and white issue (WM) is additionally helpful for measuring GM decay or for extricating multiparametric MRI measurements into WMs tracts. Spinal line division in clinical research isn't as created as cerebrum division, anyway with the considerable change of MR groupings adjusted to spinal line MR examinations, the field of spinal rope MR division has progressed extraordinarily inside the most recent decade. Division strategies with variable exactness and level of multifaceted nature have been produced. In this paper, we talked about a portion of the current strategies for line and WM/GM division, including power based, surface-based, and picture based and staggered based techniques. We likewise give suggestions to approving spinal rope division systems, as it is essential to comprehend the inborn qualities of the strategies and to assess their execution and constraints. In conclusion, we represent a few applications in the solid and neurotic spinal string. In this task, an Automatic Spinal Cord Injury (SCI) is identified utilizing a staggered division technique

Pseudo Trained YOLO R_CNN Model for Weapon Detection with a Real-Time Kaggle Dataset

The Recurrent Convolutional Neural Networks (RCNN) based deep learning models has been classified image patterns and deep features through layer architecture. In this world every country doesn’t encouraging violence, so that indirectly nations prohibiting usages of weapons to common people. This study proposes a novel YoLo Faster R-CNN based weapon detection algorithm for unusual weapon object detection. The proposed YoLo V3 R-CNN computer vision application can rapidly find weapons carried by people and highlighted through bounding-box-intimation. The work plan of this research is divided into two stages, at 1st stage pre-processing has been called to Faster R-CNN segmentation. The 2nd stage has been training the dataset as well as extracting 8-features (image_id, detection score, pixels-intensity, resolution, Aspect-ratio, PSNR, CC, SSIM) into .csv file. The labeling can be performed to RCNN-YoLo method such that getting real-time objects detection (Unusual things). The Confusion matrix has been generating performance measures in terms of accuracy 97.12%, SSIM 0.99, sensitivity 97.23%, and throughput 94.23% had been attained which are outperformance methodology

Pseudo Trained YOLO R_CNN Model for Weapon Detection with a Real-Time Kaggle Dataset

The Recurrent Convolutional Neural Networks (RCNN) based deep learning models has been classified image patterns and deep features through layer architecture. In this world every country doesn’t encouraging violence, so that indirectly nations prohibiting usages of weapons to common people. This study proposes a novel YoLo Faster R-CNN based weapon detection algorithm for unusual weapon object detection. The proposed YoLo V3 R-CNN computer vision application can rapidly find weapons carried by people and highlighted through bounding-box-intimation. The work plan of this research is divided into two stages, at 1st stage pre-processing has been called to Faster R-CNN segmentation. The 2nd stage has been training the dataset as well as extracting 8-features (image_id, detection score, pixels-intensity, resolution, Aspect-ratio, PSNR, CC, SSIM) into .csv file. The labeling can be performed to RCNN-YoLo method such that getting real-time objects detection (Unusual things). The Confusion matrix has been generating performance measures in terms of accuracy 97.12%, SSIM 0.99, sensitivity 97.23%, and throughput 94.23% had been attained which are outperformance methodology

Crystalline ZnO/amorphous ZnO core/shell nanorods: self-organized growth, structure, and novel luminescence

We have used pulsed-laser deposition, following a specific sequence of heating and cooling phases, to grow ZnO nanorods on ZnO buffer/Si (100) substrates, in a 600 mT oxygen ambient, without catalyst. In these conditions, the nanorods preferentially self-organize in the form of vertically aligned, core/shell structures. X-ray diffraction analyses, obtained from 2θ-ω and pole figure scans, shows a crystalline (wurtzite) ZnO deposit with uniform c-axis orientation normal to the substrate. Field emission SEM, TEM, HR-TEM and selective area electron diffraction (SAED) studies revealed that the nanorods have a crystalline core and an amorphous shell. The low-temperature (13 K) photoluminescence featured a strong I6 (3.36 eV) line emission, structured green band emission and a hitherto unreported broad emission at 3.331 eV. Further studies on the 3.331 eV band showed the involvement of deeply-bound excitonic constituents in a single electron-hole recombination. The body of structural data suggests that the 3.331 eV emission can be linked to the range of defects associated with the unique crystalline ZnO/amorphous ZnO core/shell structure of the nanorods. The relevance of the work is discussed in the context of the current production methods of core/shell nanorods and their domains of application