Neuroscience informatics最新文献

{"title":"Localization of stroke lesion in MRI images using object detection techniques: A comprehensive review","authors":"Sangeeta Rani ,&nbsp;Bhupesh Kumar Singh ,&nbsp;Deepika Koundal ,&nbsp;Vijay Anant Athavale","doi":"10.1016/j.neuri.2022.100070","DOIUrl":"10.1016/j.neuri.2022.100070","url":null,"abstract":"<div><p>Stroke is one of the lethal diseases that has significant negative impact on an individual's life. To diagnose stroke, MRI images play an important role. A large number of images are being produced day by day such as MRI (Medical Resonance Imaging), CT (Computed Tomography) X-Ray images and many more. Machine Learning algorithms are less efficient and time-consuming in localization of such medical images. Object detection using deep learning can reduce the efforts and time required in screening and evaluation of these images. In the proposed paper, several approaches such as RCNN (Region-based Convolutional Neural-Network), Fast R-CNN (Fast Region-based Convolutional Neural Network), Faster R-CNN (Faster Region-based Convolutional Neural Network with Region proposal Network), YOLO (You Only Look Once), SSD (Single-Shot Multibox Detector) and Efficient-Det are listed which can be used for stroke localization and classification. Comparison of RCNN, Fast R-CNN, Faster R-CNN, YOLO, SSD and Efficient-Det with accuracy are also present in this paper. A Chart of the Data Set available for object detection is also considered in this paper. By The maP (Mean-Average Precision) and the accuracy of every single method, it is identified that the speed and accuracy need to poise.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"2 3","pages":"Article 100070"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772528622000322/pdfft?md5=e1a69dda66981985c201a7b69bffb0d9&pid=1-s2.0-S2772528622000322-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47952626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimedia-based emerging technologies and data analytics for Neuroscience as a Service (NaaS)","authors":"Mohammad Shabaz ,&nbsp;Ashutosh Sharma ,&nbsp;Shams Al Ajrawi ,&nbsp;Vania Vieira Estrela","doi":"10.1016/j.neuri.2022.100067","DOIUrl":"10.1016/j.neuri.2022.100067","url":null,"abstract":"","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"2 3","pages":"Article 100067"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772528622000292/pdfft?md5=3f8ba5761d53489896d32d76f5d40427&pid=1-s2.0-S2772528622000292-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"55312787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hemodynamic response function (HRF) as a novel brain marker: Applications in subjective cognitive decline (SCD)","authors":"Liang Lu,&nbsp;Guangfei Li,&nbsp;Zeyu Song,&nbsp;Zhao Zhang,&nbsp;Xiaoying Tang","doi":"10.1016/j.neuri.2022.100093","DOIUrl":"10.1016/j.neuri.2022.100093","url":null,"abstract":"<div><h3>Objective</h3><p>Subjective cognitive decline (SCD) is the first clinical manifestation of the Alzheimer's disease (AD) continuum. Hemodynamic response function (HRF) carries information related to brain pathology and function. The shape of the HRF can be described by three parameters: response height (RH), time-to-peak (TTP), and full-width at half-max (FWHM). We proposed and explored our two hypotheses. Hypothesis 1: HRF was pathologically related to SCD: compared with healthy controls (HC), patients with SCD show HRF aberrations. Hypothesis 2: HRF could be employed as a novel marker of brain imaging for the classification of SCD.</p></div><div><h3>Methods</h3><p>We used resting-state functional magnetic resonance imaging (fMRI) data and performed deconvolution to investigate the HRF parameters in 54 individuals with SCD and 64 HC. Statistical two-sample t tests were performed to investigate between-group differences in HRF parameters. Finally, we used logistic regression to construct a binary classification of SCD and HC.</p></div><div><h3>Results</h3><p>We found altered HRF parameters in the SCD group compared to HC. In the brain regions with altered HRF, we found that RH and FWHM decreased in the SCD group compared to HC, while TTP increased in the SCD group. From the binary logistic regression, we found that the classification accuracy of SCD and HC was 94.07%.</p></div><div><h3>Conclusion</h3><p>The study demonstrated altered HRF parameters in patients with SCD, which could be used as a novel marker of brain function for the classification of SCD.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"2 3","pages":"Article 100093"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772528622000553/pdfft?md5=a8e4efc380e399a3b99488a9e888cedd&pid=1-s2.0-S2772528622000553-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49305747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficacy of video educational program on interception of urinary tract infection and neurological stress among teenage girls: An uncontrolled experimental study","authors":"Usha Rani Kandula ,&nbsp;Daisy Philip ,&nbsp;Sunitha Mathew ,&nbsp;Anusha Subin ,&nbsp;Godphy AA ,&nbsp;Nidhi Alex ,&nbsp;Renju B","doi":"10.1016/j.neuri.2021.100026","DOIUrl":"10.1016/j.neuri.2021.100026","url":null,"abstract":"<div><p><strong>Background:</strong> Nowadays, there is a lot more emphasis on promoting health, wellbeing, and self-care including stress management strategies. Health is regarded as a natural extension of a wellness-oriented lifestyle. The objectives are to measure knowledge, evaluate the efficacy of a video education program, and examine the relationship between before and after-existing knowledge measurement and specified socio factors on Urinary tract infections (UTI) and neurological stress in teenage girls.</p><p><strong>Materials and methods:</strong> This study employed an uncontrolled experimental study design. Initially, the mean and standard deviation of before and after-existing knowledge were determined. The ‘t’ test was applied to compare the variance between the before-existing and after-existing knowledge measurements of teenage girls on UTI and neurological stress, to find the efficacy of a video education program on eliminating urinary tract infection and neurological stress in teenage girls. Finally, the Chi-square model is used to measure the relationship between before-existing knowledge measurements and social characteristics.</p><p><strong>Results and interpretation:</strong> The analyzed data found that the teenage girls' mean after-existing knowledge measurement was 33.46% times greater than their mean before-existing knowledge measurement of 24.6%. According to the findings, there is no strong relationship between teenage girls before-existing knowledge measurement and selected socio-demographic factors.</p><p><strong>Conclusion:</strong> According to the study's findings, there is a critical need for healthcare providers to educate teenage girls about the interception of UTI prevalence and neurological stress management strategies inorder to avoid UTI among teenage girls.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"2 3","pages":"Article 100026"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772528621000261/pdfft?md5=dcd439adfa7f797f116d8aab765f85fd&pid=1-s2.0-S2772528621000261-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42686542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of optimal brain tissue using dynamic region growing and fuzzy min-max neural network in brain magnetic resonance images","authors":"Sunil L. Bangare","doi":"10.1016/j.neuri.2021.100019","DOIUrl":"10.1016/j.neuri.2021.100019","url":null,"abstract":"<div><p>On an MRI scan of the brain, the boundary between endocrine tissues is highly convoluted and irregular. Outdated segmentation algorithms face a severe test. Machine learning as a new sort of learning Here, researchers categorize normal and abnormal tissue using the fuzzy min-max neural network approach, which helps classify normal and abnormal tissues such as GM, CSF, WM, OCS, and OSS. This classification helps to explain the fuzzy min-max neural network method. Osseous Spongy Substance, SCALP, and Osseous Compact Substance are all MRI-classified as aberrant tissue in these tissues. Denoising and improving images can be accomplished using the Gabor filtering technique. Using the filtering method, the tumour component will be accurately identified during the segmentation operation. A dynamically changed region growing approach may be applied to a picture by modifying the Modified Region Growing method's two thresholds. This helps to raise Modified Region Growing's upper and lower bounds. Once the Region Growth is accomplished, the edges may be observed using the Modified Region Growing segmented image's Edge Detection approach. After removing the texture, an entropy-based method may be used to abstract the colour information. After the Dynamic Modified Region Growing phase findings have been merged with those from the texture feature generation phase, a distance comparison within regions is performed to combine comparable areas in the region merging phase. After tissues have been identified, a Fuzzy Min-Max Neural Network may be utilised to categorise them.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"2 3","pages":"Article 100019"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772528621000194/pdfft?md5=56555191e774d9f1f7bc7498e6b47bab&pid=1-s2.0-S2772528621000194-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42784850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistical valuation of cognitive load level hemodynamics from functional near-infrared spectroscopy signals","authors":"Farzana Khanam ,&nbsp;A.B.M. Aowlad Hossain ,&nbsp;Mohiuddin Ahmad","doi":"10.1016/j.neuri.2022.100042","DOIUrl":"10.1016/j.neuri.2022.100042","url":null,"abstract":"<div><p>Human cognitive load level assessment is a challenging issue in the field of functional brain imaging. This work aims to study different cognitive load levels statistically from brain hemodynamics. Since the functional brain activities can be evaluated by functional near-infrared spectroscopy (fNIRS), a renowned fNIRS dataset is considered for this work. The dataset contains fNIRS data of three types of <em>n</em>-back tasks (0-back, 2-back, and 3-back) of twenty-six healthy volunteers. The fNIRS signals were pre-processed and separated according to the tasks and trials. The mean changes of oxygenated hemoglobin (HbO₂) and deoxygenated hemoglobin (dHb) are calculated from each trial corresponding to the tasks and tested for significant inference among three levels utilizing analysis of variance (ANOVA). From the outcomes of the ANOVA (<span><math><mi>p</mi><mo>&lt;</mo><mn>0.005</mn></math></span>), two significant channels (AF7 (frontal) and C3h (motor)) were figured out. The significance of these two channels was further justified using the property consistency test by three different time intervals of hemodynamics inside the total task period. The latter result also explored the functional pattern of the hemodynamics of AF7 and C3h positions. Moreover, two-level cognitive load (due to easy i.e., 0-back test and hard i.e., 2-back and 3-back task) is classified using support vector machine and found classification accuracy in average 73.40%±0.076 for HbO₂ data and 71.48%±0.061 for dHb data. The study signposts the collective role played by both fNIRS signals and statistical valuation of functioning cognitive load efficacy to use fNIRS as a cognitive load assessment biomarker.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"2 3","pages":"Article 100042"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772528622000048/pdfft?md5=aa54a941fe48a7792670e1f58a0fb672&pid=1-s2.0-S2772528622000048-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41780007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating anisotropic filtering, level set methods and convolutional neural networks for fully automatic segmentation of brain tumors in magnetic resonance imaging","authors":"Mohammad Dweik ,&nbsp;Roberto Ferretti","doi":"10.1016/j.neuri.2022.100095","DOIUrl":"https://doi.org/10.1016/j.neuri.2022.100095","url":null,"abstract":"<div><p>An accurate, fully automatic detection and segmentation technique for brain tumors in magnetic resonance images (MRI) is introduced. The approach basically combines geometric active contours segmentation with a deep learning-based initialization. As a pre-processing step, an anisotropic filter is used to smooth the image; afterwards, the segmentation process takes place in two phases: the first one is based on the concept of transfer learning, where a pre-trained convolutional neural network coupled with a detector is fine-tuned using a training set of 388 T1-weighted contrast enhanced MRI images that contain a brain tumor (Meningioma); this trained network is able to automatically detect the location of the tumor by generating a bounding box with certain coordinates. The second phase takes place by using the coordinates of the bounding box to initialize the geometric active contour that iteratively evolves towards the tumor's boundaries. While most of the ingredients of this processing chain are more or less well known, the main contribution of this work is in integrating the various techniques in a novel and hopefully clever form, which could take the best of both geometric segmentation algorithms and neural networks, with a relatively light training phase. The performance of such a processing network is evaluated using a separate testing set of 97 MRI images containing the same type of brain tumor. The technique proves to be remarkably effective, with a precision of 97.92%, recall of 96.91%, F-measure of 97.41% and an average Dice similarity coefficient (<em>DSC</em>) for segmented images above 0.95.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"2 3","pages":"Article 100095"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772528622000577/pdfft?md5=70551fc15f8ab639a983b278b98e005c&pid=1-s2.0-S2772528622000577-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138279107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Face mask recognition system using CNN model","authors":"Gagandeep Kaur,&nbsp;Ritesh Sinha,&nbsp;Puneet Kumar Tiwari,&nbsp;Srijan Kumar Yadav,&nbsp;Prabhash Pandey,&nbsp;Rohit Raj,&nbsp;Anshu Vashisth,&nbsp;Manik Rakhra","doi":"10.1016/j.neuri.2021.100035","DOIUrl":"10.1016/j.neuri.2021.100035","url":null,"abstract":"<div><p>COVID-19 epidemic has swiftly disrupted our day-to-day lives affecting the international trade and movements. Wearing a face mask to protect one's face has become the new normal. In the near future, many public service providers will expect the clients to wear masks appropriately to partake of their services. Therefore, face mask detection has become a critical duty to aid worldwide civilization. This paper provides a simple way to achieve this objective utilising some fundamental Machine Learning tools as TensorFlow, Keras, OpenCV and Scikit-Learn. The suggested technique successfully recognises the face in the image or video and then determines whether or not it has a mask on it. As a surveillance job performer, it can also recognise a face together with a mask in motion as well as in a video. The technique attains excellent accuracy. We investigate optimal parameter values for the Convolutional Neural Network model (CNN) in order to identify the existence of masks accurately without generating over-fitting.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"2 3","pages":"Article 100035"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8656214/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10774166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic review of smart health monitoring using deep learning and Artificial intelligence","authors":"A.V.L.N. Sujith ,&nbsp;Guna Sekhar Sajja ,&nbsp;V. Mahalakshmi ,&nbsp;Shibili Nuhmani ,&nbsp;B. Prasanalakshmi","doi":"10.1016/j.neuri.2021.100028","DOIUrl":"10.1016/j.neuri.2021.100028","url":null,"abstract":"<div><p>In the rapidly growing world of technology and evolution, the outbreak and emergences diseases have become a critical issue. Precaution, prevention and controlling the diseases by technology has become the major challenge for healthcare professionals and health care industries. Maintaining a healthy lifestyle has become impossible in the busy work schedules. Smart health monitoring system is the solution to the above poses challenges. The recent revolution of industry 5.0 and 5G has led to development of smart cum cost effective sensors which help in real time health monitoring or individuals. The SHM has led to fast, cost effective, and reliable health monitoring services from remote locations which was not possible with traditional health care systems. The integration of blockchain framework improved data security and data privacy of confidential data of patient to prevent the data misuse against patients. Involvement of Deep Learning and Machine learning to analyze health data to achieve multiple targets has helped attain preventive healthcare and fatality management in patients. This has helped in the early detection of chronic diseases which was not possible recently. To make the services more cost effective and real time, the integration of cloud computing and cloud storage has been implemented. The work presents the systematic review of SHM along with recent advancements in SHM with existing challenges.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"2 3","pages":"Article 100028"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772528621000285/pdfft?md5=addb8b9f13fb4bde6834f358c2e78c16&pid=1-s2.0-S2772528621000285-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42723347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An efficient way of text-based emotion analysis from social media using LRA-DNN","authors":"Nilesh Shelke ,&nbsp;Sushovan Chaudhury ,&nbsp;Sudakshina Chakrabarti ,&nbsp;Sunil L. Bangare ,&nbsp;G. Yogapriya ,&nbsp;Pratibha Pandey","doi":"10.1016/j.neuri.2022.100048","DOIUrl":"10.1016/j.neuri.2022.100048","url":null,"abstract":"<div><p>Text devices are effectively and heavily used for interactions these days. Emotion extraction from the text has derived huge importance and is upcoming area of research in Natural Language Processing. Recognition of emotions from text has high practical utilities for quality improvement like in Human-Computer Interaction, recommendation systems, online education, data mining and so on. However, there are the issues of irrelevant feature extraction during emotion extraction from text. It causes mis-prediction of emotion. To overcome such challenges, this paper proposes a Leaky Relu activated Deep Neural Network (LRA-DNN). The proposed model comes under four categories, such as pre-processing, feature extraction, ranking and classification. The collected data from the dataset are pre-processed for data cleansing, appropriate features are extracted from the pre-processed data, relevant ranks are assigned for each extracted feature in the ranking phase and finally, the data are classified and accurate output is obtained from the classification phase. Publically available datasets are used in this research to compare the results obtained by the proposed LRA-DNN with the previous state-of-art algorithms. The outcomes indicated that the proposed LRA-DNN obtains the highest accuracy, sensitivity, and specificity at the rate of 94.77%, 92.23%, and 95.91% respectively which is promising compared to the existing ANN, DNN and CNN methods. It also efficiently reduces the mis-prediction and misclassification error.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"2 3","pages":"Article 100048"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772528622000103/pdfft?md5=725bdebc3880c3709a226c97d9af5b9b&pid=1-s2.0-S2772528622000103-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41988000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}