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Intracranial hemorrhage (ICH) is a dangerous condition that needs prompt diagnosis and treatment. Computed tomography (CT) images are employed in examination of individuals with ICH, which produces better results and cost‐effective than MRI. The existing convolutional neural network (CNN) models are unable to consider inter‐pixel dependency, which leads to false predictions while considering the input CT Images. In this study, we implemented an efficient model of a stack of bidirectional gated recurrent unit (Bi‐GRU) with a bidirectional long short‐term memory (Bi‐LSTM) based CNN to improve detection accuracy in the case of 2D slices. The proposed model holds slice‐wise information by accessing the properties of both Bi‐LSTM and Bi‐GRU modules in a single unit. As a result, the model attained a testing and training accuracy of 96.2% and 93.4%, respectively, with a test loss score of 0.126. In addition, the proposed model could outperform the state‐of‐the‐art CNN in identifying brain hemorrhages. [ABSTRACT FROM AUTHOR]

A polyacrylamide polysaccharide hydrogel (PASG) containing a nonionic surfactant of the polyoxyethylene nonylphenyl ethers series (NP14) has been adapted to the fabrication of a reusable cost-effective ultrasonic tissue-mimicking phantom for real-time visualization of the thermal lesions by high intensity focused ultrasound (HIFU) irradiation. The constructed NP14 (40% in w/v) PASG is optically transparent at room temperatures, and it turns out to be opaque white as heated over the clouding points of about 55 °C and returns to its original transparent state after cooling. The acoustic property of the proposed phantom is similar to those of human liver tissues, which includes the acoustic impedance of 1.68 Mrayls, the speed of sound of 1595 ± 5 m/s, the attenuation coefficient of 0.52 ± 0.05 dB cm−1 (at 1 MHz), the backscatter coefficient of 0.21 ± 0.09 × 10−3 sr−1 cm−1 (at 1 MHz), and the nonlinear parameter B/A of 6.4 ± 0.2. The NP14-PASG was tested to assess the characteristic information (sizes, shapes, and locations) of the thermal lesions visualized when exposed to typical HIFU fields (1.1 MHz, focal pressure up to 20.1 MPa, focal intensity 4075 W/cm2). The proposed NP14-PASG is expected to replace the existing costly BSA-PASG used for more effective testing of the performance of therapeutic ultrasonic devices based on thermal mechanisms. [ABSTRACT FROM AUTHOR]

Outdoor images are having several applications including autonomous vehicles, geo‐mapping, and surveillance. It is a common phenomenon that the images captured outdoor are prone to noise, which arises due to natural and manmade extreme atmospheric conditions such as haze, fog, and smog. Importantly in autonomous vehicle navigation, it is very important to recover the ground truth image to get the better decision by the system. Estimation of the transmission map and air‐light is very crucial in recovering the ground truth image. In this study, the authors proposed a new method to estimate the transmission map based on a mean channel prior (MCP), which represents the depth map to estimate the transmission map. The authors proposed a deep neural network to identify the hazy image for the further dehazing process. In this study, the authors presented, two novel contributions, first an MCP‐based image dehazing and second, a deep neural network‐based identification of hazy images as a pre‐processing block in the proposed end to end system. The proposed deep learning network using the TensorFlow platform provided validation accuracy of 93.4% for hazy image classification. Finally, the proposed MCP‐based dehazing network showed better performance in terms of peak‐signal‐to‐noise ratio, structural similarity index, and computational time than that of existing methods. [ABSTRACT FROM AUTHOR]

The aim of this study was to perform green synthesis of silver nanoparticles (AgNPs) from the leaf extract of Desmostachya bipinnata (Dharba), a medicinally important herb which is widely used across India. Synthesized AgNPs were analyzed by UV-Visible spectroscopy, X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (EDAX). The results have confirmed that green synthesis of AgNPs leads to the fabrication of sphere-shaped particles with a diameter of 53 nm. Furthermore, these AgNPs were subjected to antioxidant and antimicrobial studies against gram-negative and gram-positive bacteria, where AgNPs at a concentration of 20 mg/mL showed highest zone of inhibition. Synthesized AgNPs were evaluated for their antioxidant activity by 1, 1-diphenyl-2-picryl hydrazyl radical (DPPH), H2O2, and superoxide inhibiting assays; increasing concentration has showed increase in scavenging ability. Cell toxicity was assessed on HepG2 cell lines, and synthesized nanoparticles at a concentration of 128 μg/mL produced significant reduction in viability of Hep cells (P < .05). The availability of Dharba throughout the year and the eco-friendly approach in the synthesis of AgNPs coupled with bioactivity has demonstrated its potential as a novel biomaterial which can be used for various biomedical applications. [ABSTRACT FROM AUTHOR]

Introduction: Multimodal medical image fusion techniques play an important role in clinical diagnosis and treatment planning. The process of combining multimodal images involves several challenges depending on the type of modality, transformation techniques, and mapping of structural and metabolic information., Methods: Artifacts can form during data acquisition, such as minor movement of patients, or data pre-processing, registration, and normalization. Unlike single-modality images, the detection of an artifact is a more challenging task in complementary fused multimodal images. Many medical image fusion techniques have been developed by various researchers, but not many have tested the robustness of their fusion approaches. The main objective of this study is to identify and classify the noise and artifacts present in the fused MRI-SPECT brain images using transfer learning by fine-tuned CNN networks. Deep neural network-based techniques are capable of detecting minor amounts of noise in images. In this study, three pre-trained convolutional neural network-based models (ResNet50, DenseNet 169, and InceptionV3) were used to detect artifacts and various noises including Gaussian, Speckle, Random, and mixed noises present in fused MRI -SPECT brain image datasets using transfer learning., Results: The five-fold stratified cross-validation (SCV) is used to evaluate the performance of networks. The obtained performance results for the pretrained DenseNet169 model for various folds were greater as compared with the rest of the models; the former had an average accuracy of five-fold of 93.8±5.8%, 98%±3.9%, 97.8±1.64%, and 93.8±5.8%, whereas InceptionNetV3 had a value of 90.6±9.8%, 98.8±1.6%, 91.4±9.74%, and 90.6±9.8%, and ResNet50 had a value of 75.8±21%.84.8±7.6%, 73.8±22%, and 75.8±21% for Gaussian, speckle, random and mixed noise, respectively., Conclusion: Based on the performance results obtained, the pre-trained DenseNet 169 model provides the highest accuracy among the other four used models.