Biomedical Engineering: Applications, Basis and Communications

To prevent the respiratory diseases in an air ways, a defense mechanism based on mucus transport by the moving cilia plays an important role. The mucus transport is affected by the thermal radiation, chemical reaction that changes the physics of fluid due to nanoparticles and thickness of mucus, also different problems in respiratory tract may occur due to the mucus efficacy. In this study, it is observed that the mucus transport can be controlled by the magnetic field that is produced by the drug delivery of nanoparticles, thermal radiation due to temperature difference, porous medium due to respiratory infection, and diffusion of the nanoparticles (chemical reaction) due to the magnetic drug delivery. In this model, flow of Jeffrey nanofluid through the ciliated tube resembles with the mucus flow in a wind pipe. The movement of the mucus is observed by the momentum, energy and concentration equation in the presence of body forces due to magnetic field, heat source due to radiation, Darcy’s resistance due to infection and chemical reaction due to the concentration of nanoparticles. Mathematical model of this study forms a complex system of partial differential equations under the low Reynolds number and long wavelength approximation. The nonlinear set of partial differential equations is solved by the Homotopy perturbation method and software “Mathematica,” results are found for velocity, temperature and concentration profiles and concluded that the mucus flow decelerates due to magnetic field produced by the drug delivery of the nanoparticles but accelerates due to the viscoelastic parameter of Jeffrey fluid and Darcy’s resistance parameter due to infection. The heat transfer rate in the mucus flow rises by increasing the random motion and reduces by the radiation and energy loss. The diffusion of the nanoparticles in the mucus rises by the growing values of thermophoresis and chemical reaction parameter and reduces by the growing values of viscoelastic and Brownian motion parameter.

Improving the resolution of medical images is crucial in diagnosis, feature extraction, and data retrieval. A significant group of super-resolution algorithms is multi-frame techniques. However, they are not appropriate to medical data since they need several frames of the same scene, which bring a high risk of radiation or require a considerable acquisition time. We propose a new data augmentation technique and employ it in a multi-frame image reconstruction algorithm to improve the resolution of pathologic liver CT images. The input to our algorithm is a 3D CT-scan of the abdominal region. Neighboring slices are considered to increase the resolution of a single slice. Augmented slices are prepared using the nearby slices and the interpolation approach. The new data is aligned to the original slice, and it is used as an augmented version of the data. Then, a multi-frame scheme is utilized to reconstruct the high-resolution image. Our method’s novelty is to remove the need for multiple scans of a patent to employ multi-resolution techniques in medical applications. The results reveal that the proposed method is superior to conventional interpolation methods and available augmentation techniques. Compared to the tricubic interpolation, the proposed method improved the PSNR by 3.1. Concerning conventional augmentation techniques, it enhanced the SSIM measure by 0.06. The proposed algorithm improved the SSIM by 0.11 compared to traditional interpolation techniques and 0.1 for recent researches. Therefore, a multi-frame super-resolution technique has the potential to reconstruct medical data better.

An ocular disease that affects the elderly is Age-related Macular Degeneration (AMD). Because of the aging population in society, AMD incidence is increasing; early diagnosis is vital to avoid vision loss in the elderly. It is a challenging process to organize a comprehensive eye screening system for detecting AMD. This paper proposes a novel Double Scale Convolutional Neural Network (DSCNN) architecture for an accurate AMD diagnosis. The architecture proposed is a DSCNN with six convolutional layers for classifying AMD or normal images. The double-scale convolution layer enables many local structures to be generated with two different filter sizes. In this proposed network, the sigmoid function is used as the classifier. The proposed CNN network is trained on the Mendeley data set and tested on four data sets, namely Mendeley, OCTID, Duke, SD-OCT Noor data set, and achieved an accuracy of 99.46%, 98.08%, 96.66%, and 94.89% respectively. The comparison with alternative methods provided results showing the efficacy of the proposed algorithm in detecting AMD. Although the proposed model is trained only on the Mendeley data set, it achieved good detection accuracy when evaluated with other data sets. This indicates the proposed model’s ability to classify AMD/Normal images from different data sets. Comparison with other approaches produced results that exhibit the efficiency of the proposed algorithm in detecting AMD. The proposed architecture can be applied in the rapid screening of the eye for the early detection of AMD. Due to less complexity and fewer learnable parameters, the proposed CNN can be implemented in real-time.

Corona virus (CoV) is a group of viruses with non-bifurcated, single-stranded, and positive-sense RNA genomes. Apart from infecting several economically significant vertebrates (such as pigs and chickens), it is reported in the recent literature that six main types of CoVs infect the human hosts and cause lung infections. In animals, CoVs cause several diseases, including pneumonia, gastrointestinal tract, and central nervous system diseases. In humans, the CoVs work as respiratory tract diseases, and the new CoVs can penetrate the barrier between other species and humans and can cause a high mortality rate. In the course of this study, a novel approach to networking, based on the density-dependent differential equations, is adopted for the precise explanation of the propagation of the virus and the effect of quarantine on it. An infectious disease model with a time delay is suggested based on the conventional infectious disease model. To describe the viral infection period and treatment time, the time differential is used. Using the epidemic data released in real-time, the minimum error is obtained firstly through the inversion of the numerical simulation parameter; then we simulate the development pattern of the epidemic according to the dynamics system; finally, the effectiveness of quarantine steps is compared and analyzed. With the help of a discrete model, the transformations are documented in detail that is difficult to evaluate numerically. The provided numerical results are in close agreement with the experimental findings. The modeling of Petri nets (PNs) used has proven to be a successful method. The current research strategy can help the public to gain awareness of the disease spread, which is highly desired.

In present clinic practice of otolaryngology, otolaryngologists utilized laryngoscopy to diagnose the larynx lesion of patients preliminarily. Nevertheless, it was challenging for otolaryngologists to interpret the detailed information from laryngoscopy videos comprehensively. In this paper, we proposed Mask R-CNN deep learning algorithm to segment the regions of the vocal folds and glottal from laryngoscopy videos, and self-built algorithm to calculate measured indicators including the length and curvature of vocal folds, the angle of glottal, the area of vocal folds and glottal, and the triangle type composed of vocal folds and glottal. Moreover, in order to provide otolaryngologists critical and immediate medical information during diagnosis, we also provided visualized information, which is labeled on the laryngoscopy images to meet all the needs in clinical practice. From the result of this research, the precision of segmentation has reached a high rate of 90.4% on average. It shows that the model not only achieves great performance in segmentation, but also further proved the indicators are accurate enough to be considered in practical diagnosis. In the future, it is possible for the proposed model to be applied in more kinds of laryngoscopy analyses for more comprehensive diagnosis, which would make a positive influence toward the clinical practice of otolaryngology.

Precise mathematical modeling of the primary visual cortex (V1) is still a challenging problem. Due to the high similarity of visual system of cat and human, in this paper, we present a hybrid model to track the electrical responses of neurons that are measured by a multi-electrode array implanted in cat V1. The proposed model combines a stochastic phenomenological model with a multilayer leaky integrate-and-fire (LIF) model to predict V1 responses. Since all the existing visual cortex models do not capture the stochastic properties of synaptic changes, the proposed phenomenological model provides input currents for V1 by utilizing continuous chaotic neural equations with a quantization rule. Then a multilayer LIF model is presented to mimic the functions of lateral geniculate nucleus (LGN) and V1 neurons by their corresponding differential equations. The input current in these models is from the presynaptic neurons, which are computed using the LIF model. The LGN-V1 neuronal connections are adopted from previous studies, where the receptive fields (RFs) of LGN neurons converge onto elongated spatial structures that denote RFs of V1 neurons. The main purpose of this paper is to develop a short-term plasticity model that is more consistent with the nature of the LGN and V1 responses compared to state-of-the-art models. Previous studies have not incorporated the stochastic and quantized behaviors of neurons that in the recorded data of implemented electrodes. The experimental results show the ability of the proposed model to accurately predict spikes recorded experimentally, indicating the model outperforms the state-of-the-art method.

Lung cancer is one of the most common cancers; lung cancer is a malignant tumor that seriously threatens the lives of patients. Improving survival prediction performance is meaningful for making the treatment plans and improving the survival rates of lung cancer patients. In this paper, an approach for predicting the survival of lung cancer patients is proposed based on pathological images. First, the deep learning method is used to automatically detect lung cancer cells in pathological pictures, and features of the detected lung cancer cells are extracted. In feature selection, an extraction method of topological features is given, it reflects the relationship and distribution characteristics of lung cancer cells, and the extracted topological features are used as predictive factors for survival analysis. In this paper, the extraction methods of global topological features are mainly studied; for example, the overall association, location relationship and distribution of cells, and the global topological features of lung cancer cells are extracted through the Voronoi diagram, Delaunay triangle, and minimum spanning tree methods. Finally, the Cox–LASSO method was used to predict the survival of lung cancer patients. Experimental results show that this method can improve the efficiency and accuracy of cell detection, and there is a higher ability to predict and analyze the survival of lung cancer patients.

Heel fissures are cracks in the skin over the heels that lead to pain, discomfort and decreased confidence levels. If left untreated, they may also lead to infections and in rare cases, become life-threatening. Therefore, people with heel fissures generally try to find some remedy to relieve their symptoms. The objectives of this study are as follows: (1) To use thermal imaging to determine whether a characteristic difference in temperature exists in the heel fissure regions before and after performing heel therapy; (2) To segment the images and extract the features using k-means, GLCM and SURF methods, respectively; (3) To implement machine learning classifier for classification on normal heel and fissured heel. A number of 30 heel fissure and 30 normal subjects were considered for this study. All the candidates were from the age group of 35–55 years. Thermography was used to acquire the images of heel regions, and the thermographs were analyzed for feature extraction. Naïve Bayes, Bagging, Random Forest, LMT and Simple Logistic classifiers were used for classification of the thermograms. After heel therapy, a 2.2% and 2.6% decrease in temperature was observed in the right and left heel, respectively. The GLCM mean is increased by 6% and 4.3% in the right and left heel, respectively. A considerable decrease in variance in the fissure regions after therapy has also been observed. All three classifiers were shown to be efficient, with Nave Bayes and Bagging classifier both showing accuracy of 89%. The ROC curves have also been obtained, with an area under curve equal to 0.97.