2020 Medical Technologies Congress (TIPTEKNO)最新文献_第6页

Assessment of cell cycle and viability of magnetic levitation assembled cellular structures 磁性悬浮组装细胞结构的细胞周期和活力评估

2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299216

Muge Anil-Inevi, Y. Unal, Sena Yaman, H. Tekin, Gulistan Mese, E. Ozcivici

引用次数: 0

Classification of Pathological and Healthy Voice Using Perceptual Wavelet Packet Decomposition and Support Vector Machine 基于感知小波包分解和支持向量机的病理和健康语音分类

2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299290

Özkan Arslan

引用次数: 0

Classification of Malignant Lymphoma Types Using Convolutional Neural Network 基于卷积神经网络的恶性淋巴瘤类型分类

2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299267

Nuh Hatipoglu, G. Bilgin

引用次数: 1

The Effect of F-response Parameters in ALS and Polio Patients f -反应参数对ALS和脊髓灰质炎患者的影响

2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299307

T. Artug

引用次数: 0

Deep Learning based Colorimetric Classification of Glucose with Au-Ag nanoparticles using Smartphone 基于深度学习的智能手机Au-Ag纳米颗粒葡萄糖比色分类

2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299296

Ö. B. Mercan, ve Volkan Kılıç

引用次数: 4

Comparison of Tissue Classification Performance by Deep Learning and Conventional Methods on Colorectal Histopathological Images 基于深度学习和传统方法的结直肠组织病理图像分类性能比较

2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299277

Z. Karhan, F. Akal

{"title":"Comparison of Tissue Classification Performance by Deep Learning and Conventional Methods on Colorectal Histopathological Images","authors":"Z. Karhan, F. Akal","doi":"10.1109/TIPTEKNO50054.2020.9299277","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299277","url":null,"abstract":"The automatic evaluation is essential for the diagnosis and treatment of the disease of pathological images. Computer-aided systems are becoming more common day by day in this area. In this study, multi-class (8 different classes) tissue types were studied in colon cancer histopathological images. Data mining algorithms are used in the diagnosis phase in the health field. As a conventional method, first of all, the properties of the images are extracted and then the texture classification process is performed with data mining algorithms. The Gray Level Co-occurrence Matrix (GLCM), Discrete Cosine Transform (DCT), Local Binary Pattern (LBP) are used in textural feature extraction. Along with these attributes, machine learning algorithms, such as k-nearest neighbors (KNN), support vector machines (SVM), random forests (RF), logistic regression (LR) were used for classification. As another method, to remove the attributes and perform classification at the same time, tissue classification was performed using deep learning (convolutional neural network) on histopathological images. Tissue classification was automated using transfer learning based on ResNet-18 architecture, one of the convolutional neural network architectures. According to the determined feature and classification algorithm, the performance rates are also given comparatively. Our experiments showed that RF classifier with LBP and GLCM features provided 82% accuracy, while the deep learning method based on ResNet-18 architecture achieved 88.5% accuracy.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"128 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":"122578883","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

Fabrication and Characterization of a Metal Oxide Semiconductor Field Effect Transistor (MOSFET)-based Micro pH Sensor 基于金属氧化物半导体场效应晶体管(MOSFET)的微pH传感器的制造与表征

2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299291

Fikri Seven, ve Mustafa Şen

引用次数: 0

Comparison of Parallel Magnetic Resonance Imaging Algorithms: PILS and SENSE 并行磁共振成像算法的比较:PILS和SENSE

2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299249

Egehan Dorum, Mazlum Unay, Onan Guren, A. Akan

引用次数: 0

Wearable respiratory rate sensor technology for diagnosis of sleep apnea 穿戴式呼吸速率传感器技术诊断睡眠呼吸暂停

2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299255

Gokturk Cinel, E. A. Tarim, H. Tekin

{"title":"Wearable respiratory rate sensor technology for diagnosis of sleep apnea","authors":"Gokturk Cinel, E. A. Tarim, H. Tekin","doi":"10.1109/TIPTEKNO50054.2020.9299255","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299255","url":null,"abstract":"Sleep apnea is a disease that occurs during sleep, which affects the daily life of patients due to the obstruction of the upper respiratory tract and decreases oxygen level in blood and it may even lead to patient death in the later stage. Monitoring the patients regularly has absolute importance to prevent patient disorders caused by sleep apnea. Wearable sensor technologies and patient tracking systems provide diagnosis, treatment, and monitoring of patients, and procure better health services in medical fields. In addition to decreasing the workload of health institutions, remote patient monitoring systems can serve continuous monitoring and determine variable symptoms of the patients. In this paper, we propose a patient monitoring system, which will be used for diagnosis and monitoring of sleep apnea by tracking the respiratory rate of patients with wearable sensor technology. The respiratory rate is detected using either an accelerometer sensor to be placed on the patients' abdomen or a temperature sensor to be placed on their noses. The proposed system offers an increase in the versatility of patient monitoring systems and offers an alternative new technology in sleep apnea diagnosis.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"15 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":"127971947","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

A Deep Learning-Based Approach to Detect Neurodegenerative Diseases 基于深度学习的神经退行性疾病检测方法

2020 Medical Technologies Congress (TIPTEKNO) Pub Date : 2020-11-19 DOI: 10.1109/TIPTEKNO50054.2020.9299257

Ç. Erdaş, E. Sümer

{"title":"A Deep Learning-Based Approach to Detect Neurodegenerative Diseases","authors":"Ç. Erdaş, E. Sümer","doi":"10.1109/TIPTEKNO50054.2020.9299257","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299257","url":null,"abstract":"Studies conducted by the World Health Organization (WHO) show that more than a billion people worldwide suffer from neurological disorders and the lack of effective diagnostic procedures affects treatment. Characterizing specific motor symptoms to facilitate their diagnosis can be useful in monitoring disease progression and effective treatment planning. Classification of highly prevalent neurodegenerative diseases (NDD) such as Parkinson’s disease (PH), Amyotrophic Lateral Sclerosis (ALS), and Huntington’s disease (HH) is of clinical importance. One of the methods used in the literature to detect these neurodegenerative diseases is gait analysis-based classification. In this study, 12 different features fed a unidimensional Convolutional Neural Network (CNN) deep learning algorithm-based model, and aims to detect PD,HD, and ALS diseases was studied.The unidimensional CNN deep learning model fed with 12 features achieved 78,92%, 84,39% and 92,09% classification accuracy for control against HH, control against PH, and control detection problems against ALS. Again, the relevant classifier produced 84.75% accuracy with the approach developed to separate all neurodegenerative disease specimens (NDD) under a single label as a class, and to distinguish these diseases against the current control.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"98 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":"132840790","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