Biomedical Engineering: Applications, Basis and Communications最新文献

{"title":"COMPARISON OF MACHINE LEARNING TECHNIQUES FOR PREDICTING NLR PROTEINS","authors":"Nadia, Ekta Gandotra, Narendra Kumar","doi":"10.4015/s1016237222500508","DOIUrl":"https://doi.org/10.4015/s1016237222500508","url":null,"abstract":"The nucleotide-binding domain leucine-rich repeat-containing (NLR) proteins plays significant role in the intestinal tissue repair and innate immunity. It recently added to the members of innate immunity effectors molecules. It also plays an essential role in intestinal microbiota and recently emerged as a crucial hit for developing ulcerative colitis (UC) and colitis-associated cancer (CAC). A machine learning-based approach for predicting NLR proteins has been developed. In this study, we present a comparison of three supervised machine learning algorithms. Using ProtR and POSSUM Packages, the features are extracted for the dataset used in this work. The models are trained with the input compositional features generated using dipeptide composition, amino acid composition, etc., as well as Position Specific Scoring Matrix (PSSM) based compositions. The dataset consists of 390 proteins for the negative and positive datasets. The five-fold cross-validation (CV) is used to optimize Sequential Minimal Optimization (SMO) library of Support Vector Machine (LIBSVM) and Random Forest (RF) parameters, and the best model was selected. The proposed work performs rationally well with an accuracy of 90.91% and 93.94% for RF as the best classifier for the Amino Acid Composition (AAC) and PSE_PSSM-based model. We believe that this method is a reliable, rapid and useful prediction method for NLR Protein.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82987582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CRITIQUE OF DESIGN CHALLENGE OF FLYING ROBOTS","authors":"R. Maity, Ruby Mishra, P. Pattnaik","doi":"10.4015/s1016237222300024","DOIUrl":"https://doi.org/10.4015/s1016237222300024","url":null,"abstract":"Flying robots popularly known as drones or UAVs are emerging technologies of the current era. A significant amount of research work has been undertaken in this area in the last few years. Considering the current scenario where aerial vehicles are taking a major part of the market it is important to have an effective and robust design of flying robots. This paper aims to examine the categories of flying robots based on the features that include a range from petite to large and its body structure, wing designs, tail design, propulsion system, and gripper mechanisms along with the associated materials and manufacturing techniques. Again the work is intended to review the respective challenges faced by each category. Mostly the challenges faced by flying robots are design challenges, material selection, and fabrication challenges which are discussed in the paper. In this paper, we have summarized various designs of flying robots developed to date as well as we have focused on major features to be taken care of while designing flying robots. This paper has tried to focus on different design aspects and challenges faced by flying robots so that further research can be carried out to develop effective flying robots in the future.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"15 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85042097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"THE USE OF ALLOGENIC COLLAGEN GEL IN NASOLABIAL FOLD TREATMENT: AN EXPERIMENTAL ASSESSMENT","authors":"M. Mehrabani, M. Farahvash, Reza Samanipour, S. Tabatabaee, Adel Marzban, J. Rahmati, A. Tavakoli","doi":"10.4015/s1016237222500090","DOIUrl":"https://doi.org/10.4015/s1016237222500090","url":null,"abstract":"The influence of facial appearance on people’s mental confidence is well-known in the modern life. The aim of this study was to evaluate the clinical efficacy of human-derived collagen gel injection on nasolabial folds and reveal a safe and cost-reasonable candidate for aesthetic and therapeutic cases. This assessment was a quasi-experimental interventional study on patients referred to the plastic surgery clinic of Imam Hospital in 2016–2017 who intended to treat nasolabial folds with outpatient methods and rapid recovery time regardless of age and gender restrictions. Allogenic collagen was injected at the site of nasolabial folds and the durability of the fillers was evaluated by the researcher, the neutral examiner and the participants based on the wrinkle severity rating scale (WSRS) and the global aesthetic improvement scale (GAIS). In terms of severity of nasolabial folds before intervention, the mean and severe state comprised 37 (69.81%) and 16 (30.19%) patients, respectively. The majority of subjects (more than 80%) in both assessments (the examiner and the researcher) demonstrated the improvement of the folds. The agreement between the two evaluators was relatively approximate ([Formula: see text] and [Formula: see text]). Regardless of the evaluation group, the trend of changes was statistically significant ([Formula: see text]). Eventually, the duration of the filler efficacy was estimated to be 4–6 months. The allogenic collagen filler is recommended as an almost safe and cost-effective agent for nasolabial fold treatment in short to medium periods in case of low risk of the transmission of contamination and no need for allergic testing.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"43 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90696768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New corrected QT formula optimal for different heart rates: A comparative study","authors":"S. Karimulla, Arya Bhardwaj, V. Sangareddi, J. Sivaraman","doi":"10.4015/s1016237222500430","DOIUrl":"https://doi.org/10.4015/s1016237222500430","url":null,"abstract":"","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"58 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89459989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Taylor-IIWO: Taylor Improved Invasive Weed Optimization enabled Deep Quantum Neural Network for brain tumor segmentation and classification","authors":"A. Ratna Raju, S. Pabboju, R. Rajeswara Rao","doi":"10.4015/s1016237222500442","DOIUrl":"https://doi.org/10.4015/s1016237222500442","url":null,"abstract":"","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"13 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83183581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PERFORMANCE ENHANCEMENT OF DEEP NEURAL NETWORK BASED AUTOMATIC VOICE DISORDER DETECTION SYSTEM WITH DATA AUGMENTATION — DETECTION OF LEUKOPLAKIA: A CASE STUDY","authors":"D. K. Thennal, Vrinda V. Nair, R. Indudharan, D. Gopinath","doi":"10.4015/s1016237222500417","DOIUrl":"https://doi.org/10.4015/s1016237222500417","url":null,"abstract":"Laryngeal pathologies resulting in voice disorders are normally diagnosed using invasive methods such as rigid laryngoscopy, flexible nasopharyngo-laryngoscopy and stroboscopy, which are expensive, time-consuming and often inconvenient to patients. Automatic Voice Disorder Detection (AVDD) systems are used for non-invasive screening to give an indicative direction to the physician as a preliminary diagnosis. Deep neural networks, known for their superior discrimination capabilities, can be used for AVDD Systems, provided there are sufficient samples for training. The most popular datasets used for developing AVDD systems lack sufficient samples in several pathological categories. Leukoplakia — a premalignant lesion, which may progress to carcinoma unless detected early — is one such pathology. Data augmentation is a technique used in deep learning environments to increase the size of the training datasets which lack sufficient samples for effective data analysis and classification. This study aims at investigating the performance enhancement of a deep learning-based AVDD system through a novel time domain data augmentation technique named ‘TempAug’. This method segments each data sample into short voice segments, so as to get multiple data from each sample, thereby generating a larger database (augmented database) for training a deep learning model. A deep neural network model, Long Short-Term Memory (LSTM) with Short Term Fourier Transform (STFT) coefficients as input features for classification, was used in this study for the detection of the voice disorder Leukoplakia. A series of experiments were done to investigate the effect of data augmentation and to find the optimum duration for segmentation. Based on experimental results, a detection strategy was developed and evaluated using an AVDD system, which gave an accuracy of 81.25%. The percentage increase in accuracy was found to be 46.9% with respect to the accuracy obtained for unaugmented data.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"10 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78589073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"D-DAGNet: AN IMPROVED HYBRID DEEP NETWORK FOR AUTOMATED CLASSIFICATION OF GLAUCOMA FROM OCT IMAGES","authors":"A. P. Sunija, V. Gopi, Adithya K. Krishna","doi":"10.4015/s1016237222500429","DOIUrl":"https://doi.org/10.4015/s1016237222500429","url":null,"abstract":"The introduction of Optical Coherence Tomography (OCT) in ophthalmology has resulted in significant progress in the early detection of glaucoma. Traditional approaches to identifying retinal diseases comprise an analysis of medical history and manual assessment of retinal images. Manual diagnosis is time-consuming and requires considerable human expertise, without which, errors could be costly to human sight. The use of artificial intelligence such as machine learning techniques in image analysis has been gaining ground in recent years for accurate, fast and cost-effective diagnosis from retinal images. This work proposes a Directed Acyclic Graph (DAG) network that combines Depthwise Convolution (DC) to decisively recognize early-stage retinal glaucoma from OCT images. The proposed method leverages the benefits of both depthwise convolution and DAG. The Convolutional Neural Network (CNN) information obtained in the proposed architecture is processed as per the partial order over the nodes. The Grad-CAM method is adopted to quantify and visualize normal and glaucomatous OCT heatmaps to improve diagnostic interpretability. The experiments were performed on LFH_Glaucoma dataset composed of 1105 glaucoma and 1049 healthy OCT scans. The proposed faster hybrid Depthwise-Directed Acyclic Graph Network (D-DAGNet) achieved an accuracy of 0.9995, precision of 0.9989, recall of 1.0, F1-score of 0.9994 and AUC of 0.9995 with only 0.0047 M learnable parameters. Hybrid D-DAGNet enhances network training efficacy and significantly reduces learnable parameters required for identification of the features of interest. The proposed network overcomes the problems of overfitting and performance degradation due to accretion of layers in the deep network, and is thus useful for real-time identification of glaucoma features from retinal OCT images.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"17 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79223082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DIAGNOSIS OF BRAIN TUMOR USING MULTISCALE CONVOLUTION NEURAL NETWORK","authors":"Homayoon Yektaei, Hanieh Yektaei, Yasaman Hoseyni","doi":"10.4015/s1016237222500405","DOIUrl":"https://doi.org/10.4015/s1016237222500405","url":null,"abstract":"Nowadays, the number of patients with brain tumors is steadily increasing, diagnosis and isolation of the tumor play an important role in the process of treatment and surgery. Due to the high error of manual segmentation of the tumor, algorithms that perform this operation with less error are of great importance. Convolutional neural networks have made great progress in the field of medical imaging. The use of imaging techniques and pattern recognition in the diagnosis and automatic determination of brain tumors by MRI imaging reduces errors, human error and speeds up detection. The artificial convolutional neural network (CNN) has been widely used in the diagnosis of intelligent cancers and has significantly reduced the error rate. Therefore, in this paper, we present a new method using a combination of convolutional and multi-scale artificial neural network that has significantly increased the accuracy of tumor diagnosis. This study presents a multidisciplinary convolution neural network (MCNN) approach to classifying tumors that can be used as an important part of automated diagnosis systems for accurate cancer diagnosis. Based on the MCNN structure, which presents the MRI image to several deep convolutional neural networks of varying sizes and resolutions, the stage of extracting classical hand-made features is avoided. This approach proposes better classification rates than the classical methods. This study uses a multi-scale convolution technique to achieve a detection accuracy of 95/4%, which shows the efficiency of the proposed method.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"71 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74354074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EFFICIENT RETINAL IMAGE ENHANCEMENT USING MORPHOLOGICAL OPERATIONS","authors":"Ashanand, M. Kaur","doi":"10.4015/s1016237222500338","DOIUrl":"https://doi.org/10.4015/s1016237222500338","url":null,"abstract":"Manual analysis of retinal images is a complicated and time-consuming task for ophthalmologists. Retinal images are susceptible to non-uniform illumination, poor contrast, transmission error, and noise problems. For the detection of retinal abnormalities, an efficient technique is required that can identify the presence of retinal complications. This paper proposes a methodology to enhance retinal images that use morphological operations to improve the contrast and bring out the fine details in the suspicious region. The enhancement plays a vital role in detecting abnormalities in the retinal images. Luminance gain metric ([Formula: see text] is obtained from Gamma correction on luminous channel of [Formula: see text]*[Formula: see text]*[Formula: see text] (hue, saturation, and value) color model of retinal image to improve luminosity. The efficiency and strength of the proposed methodology are evaluated using the performance evaluation parameters peak signal to noise ratio (PSNR), mean square error (MSE), mean absolute error (MAE), feature structural similarity index metric (FSIM), structural similarity index metric (SSIM), spectral residual index metric (SRSIM), Reyligh feature similarity index metric (RFSIM), absolute mean brightness error (AMBE), root mean square error (RMSE), image quality index (IQI), and visual similarity index (VSI). It has been revealed from the results and statistical analysis using the Friedman test that the proposed method outperforms existing state-of-the-art enhancement techniques.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"19 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82713732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NOVEL FINDINGS ON CARDIORESPIRATORY ROLE SWAPPING BY ASSESSING NONLINEAR DYNAMICAL INTERACTIONS","authors":"Pei-Chen Lo","doi":"10.4015/s1016237222500399","DOIUrl":"https://doi.org/10.4015/s1016237222500399","url":null,"abstract":"This paper reports the innovative results of applying nonlinear dynamical theory to heart-rate (HR) and respiratory (RP) sequences to explore Yin–Yang (passive–active) interacting phenomenon of cardiorespiratory function. Organ systems inside human body interact mutually in the manner of swapping the active–passive role, according to Yin–Yang theory in traditional Chinese medicine. Yang as an active source rules over Yin as a passive load. Nonlinear inter-driven (NLID) factor, [Formula: see text], based on recurrence analysis is proposed in this study. NLID factor characterizes the nonlinear interactions between two nonlinear dynamical systems, cardiac and respiratory systems. NLID factor is computed by quantifying the HR and RP recurrent plots constructed from phase trajectories based on embedding theorem and phase-space reconstruction. Heterogeneous patterns of HR and RP recurrence plots in continuous-attention task reflect highly unpredictable system dynamics. Heterogeneous HR recurrence patterns are surprisingly associated with the nearly homogeneous RP recurrence plot at resting state, indicating rather weak cardiorespiratory interaction. HR recurrence in Zen exhibits notable regularity in spite of the irregular RP recurrence behaviors. NLID factor reveals prominent Yang energy delivered by RP system that dominates over the cardiac system in Zen-meditation group. Such findings draw our attention to the possibly extraordinary self-regularity power of heart (and autonomic nervous system) in the Zen-meditation state.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"32 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87047200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}