2020 Medical Technologies Congress (TIPTEKNO)最新文献_第5页

Use of Waste Salmon Bones as a Biomaterial 利用废鲑鱼骨作为生物材料

2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299226

Merve Bas, S. Dağlilar, C. Kalkandelen, O. Gunduz

{"title":"Use of Waste Salmon Bones as a Biomaterial","authors":"Merve Bas, S. Dağlilar, C. Kalkandelen, O. Gunduz","doi":"10.1109/TIPTEKNO50054.2020.9299226","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299226","url":null,"abstract":"In the presented study; Hydroxyapatite (HA) used in many areas such as filling of cavities, bone tissue treatments, chin-face, orthopedic and dental surgeries, was obtained from waste salmon fish bones. Instead of producing chemically in a laboratory environment, hard tissue waste of natural resources was used. As trace elements such as magnesium, zinc, and strontium in the structure of natural resources support bone formation, waste salmon fish bones, which are a natural source, were preferred as raw materials. Other advantages include being easy to access raw materials, cheap and environmentally friendly. HA was obtained from salmon bones by the thermal calcination method. The obtained pure salmon hydroxyapatites were sintered at different temperatures, and the effect of changing sintering temperature on the density, microhardness, compressive strength, and elasticity module in the material was investigated. Crystal phase analysis of salmon hydroxyapatite powder and thermal analysis up to a certain temperature were made. MTT cytotoxicity test was performed to measure whether the materials were toxic. This study has the potential to contribute to the development of biomaterial studies for bone repair.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134506326","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}

引用次数: 0

Cutting Effect on Classification Using Nasnet Architecture 使用Nasnet体系结构对分类的切割效应

2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299313

F. Yilmaz, Ahmet Demir

引用次数: 2

Comparison of Several Machine Learning Classifiers for Arousal Classification: A Preliminary study 几种机器学习分类器在唤醒分类中的比较:初步研究

2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299316

E. C. Erkus, V. Purutçuoğlu, F. Arı, D. Gökçay

{"title":"Comparison of Several Machine Learning Classifiers for Arousal Classification: A Preliminary study","authors":"E. C. Erkus, V. Purutçuoğlu, F. Arı, D. Gökçay","doi":"10.1109/TIPTEKNO50054.2020.9299316","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299316","url":null,"abstract":"Detection of arousal intervals, especially stress detection via a human-machine interface is a trending topic. Stress detection algorithms with high accuracy can be used in many fields such as criminal interrogations or a variety of stress-related experiments. There are many indicators of the stress on the human body, especially on the face area, such as galvanic skin response (GSR), pupil diameter, heart rate (HR), and electromyography (EMG). Hereby, the measurement of such physiological data in stressful, joyful and non-stressful cases can reveal the effects of the stress on the body signals.This preliminary study aims to compare several machine learning approaches, namely, linear discriminant analysis (LDA), k-nearest neighbour (k-NN), Naive Bayes, support vector machines (SVM) and coarse tree algorithms in a classification study. To perform the analyses, the pupil data are collected from a total of 9 subjects while the subject was watching three types of movies, independently. The classifications are performed among the labelled data with multivariate features such as mean, median, maximum to minimum difference and variance, and their univariate versions in order to observe their independent discrimination performances. Moreover, the preprocessed raw data are also used in classification, independently. Here, the movies are selected such that they include either annotated positive, negative or neutral scenes, which may indicate the stressful, joyful and non-stressful intervals, respectively. Therefore, the classification results of these algorithms for the annotated labels in each channel separately are found to observe their effectiveness in detection of arousal intervals. Hence, the main aim is to contribute to the stress detection literature by providing a comparison between both the classification algorithms, features and raw data classification.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122347523","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}

引用次数: 1

Comparison of Variational Mode Decomposition and Empirical Mode Decomposition Features for Cell Segmentation in Histopathological Images 变分模态分解与经验模态分解特征在组织病理图像细胞分割中的比较

2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299321

Omer Faruk Karaaslan, G. Bilgin

引用次数: 1

Classification of Cognitive Fatigue with EEG Signals 脑电信号对认知疲劳的分类

2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299239

A. Ekim, Önder Aydemir, Mengu Demir

{"title":"Classification of Cognitive Fatigue with EEG Signals","authors":"A. Ekim, Önder Aydemir, Mengu Demir","doi":"10.1109/TIPTEKNO50054.2020.9299239","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299239","url":null,"abstract":"Cognitive fatigue is the natural result of longtime mental effort during the execution of a high mental workload or a strenuous task. This situation often leads to decreased productivity and increased security risks. In this study, it was aimed to detect cognitive fatigue quickly and accurately, regardless of subjective data. CogBeacon dataset was used for this. Data that make up the CogBeacon dataset were collected from 19 participants in 76 sessions with the help of a 4-electrode MUSE electroencephalography (EEG) device. The collected raw EEGs were randomly separated and feature extraction was performed. Support Vector Machine (SVM) and k-Nearest Neighbor (KNN) algorithms were used in the classification process. Katz and Higuchi Fractal Dimension, standard deviation, median, variance and covariance were tested as features. When the classification was made with SVM, the education average was 93.99% and the test average was 83.14%. The average success rate increased between 4.43% and 7.40%, compared to the trials that were not used in the trials where Fractal Dimension features were used. When the classification was made with KNN, the education averange was 91.71% and the test average was 83.34%. The average success rate increased between 5.10% and 8.92% compared to the trials that were not used in the trials in which Fractal Dimension features were used.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129290656","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}

引用次数: 1

Early pleural effusion detection from respiratory diseases including COVID-19 via deep learning 基于深度学习的COVID-19等呼吸系统疾病早期胸腔积液检测

2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299300

Sertan Serte, Ali Serener

引用次数: 6

EMG based Hand Gesture Classification using Empirical Mode Decomposition Time-Series and Deep Learning 基于EMG的基于经验模式分解时间序列和深度学习的手势分类

2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299282

Deniz Hande Kisa, Mehmet Akif Ozdemir, Onan Guren, A. Akan

{"title":"EMG based Hand Gesture Classification using Empirical Mode Decomposition Time-Series and Deep Learning","authors":"Deniz Hande Kisa, Mehmet Akif Ozdemir, Onan Guren, A. Akan","doi":"10.1109/TIPTEKNO50054.2020.9299282","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299282","url":null,"abstract":"Computer systems working with artificial intelligence can recognize movements and gestures to be used for many purposes. In order to perform recognition, the electrical activity of the muscles can be utilized which is represented by electromyography (EMG) and EMG is not a stationary biological signal. EMG based movement recognition systems have an important place in distinct areas like in human-computer interactions, virtual reality, prosthesis, and hand exoskeletons. In this study, a new approach based on deep learning (DL) and Empirical Mode Decomposition (EMD) is proposed to improve the accuracy rate for recognition of hand movements in its application areas. Firstly, 4-channel surface EMG (sEMG) signals were measured while simulating 7 different hand gestures, which are extension, flexion, ulnar deviation, radial deviation, punch, open hand, and rest, from 30 subjects. After that, noiseless signals were procured utilizing filters as a result of preprocessing. Then, pre-processed signals were subjected to segmentation. Thereafter, the EMD process was applied to each segmented signal and Intrinsic Mode Functions (IMFs) were obtained. The IMFs time-series which are some kind of screen images of the first 3 IMFs have been recorded. For classification, IMFs images have given as inputs and have trained to the 101layer Convolution Neural Network (CNN) based on Residual Networks (ResNet) architecture, which is a DL model.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132637767","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}

引用次数: 10

Estimating Rotation Angle and Transformation Matrix Between Consecutive Ultrasound Images Using Deep Learning 基于深度学习的连续超声图像旋转角度和变换矩阵估计

2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299237

M. Mikaeili, H. Ş. Bilge

{"title":"Estimating Rotation Angle and Transformation Matrix Between Consecutive Ultrasound Images Using Deep Learning","authors":"M. Mikaeili, H. Ş. Bilge","doi":"10.1109/TIPTEKNO50054.2020.9299237","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299237","url":null,"abstract":"Image registration plays a crucial role in biomedical imaging, especially in image-guided surgery. Obtaining real-time images with an Ultrasound Imaging System (US) makes it possible to register them with magnetic resonance (MR) or computed tomography (CT) images and increase the accuracy of imageguided surgery. Differences in the resolution and intensity of these images motivated us to register ultrasound images with each other. Ultrasound images suffer from low contrast and resolution in comparison to other image modalities such as MR. By acknowledging the fact that the transformation matrix is the building block of the registration concept. Also, given the success of deep learning in classification, we choose to apply it to identify the angle difference and rotation matrix of three consecutive ultrasound images. This paper attempts to find the Euler angles and rotation matrix of three consecutive ultrasound images by applying a deep learning method. At the end of the study, we attain promising results when our learning rate is 0.00002 and the scaling factor is 64× 32. Furthermore, the comparison of positive and negative angles demonstrates that the overall network performs better in predicting positive angles.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124284526","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}

引用次数: 5

Smart Stethoscope 聪明的听诊器

2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299229

Mehmet Nasuhcan Türker, Yağız Can Çağan, Batuhan Yildirim, Mücahit Demirel, A. Özmen, B. Tander, Mesut Cevik

引用次数: 3

Classification of EEG Signals Recorded During Imagery of Hand Grasp Movement 手抓动作图像中脑电信号的分类

2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299228

O. Ateş, Önder Aydemir

引用次数: 0