2018 9th Cairo International Biomedical Engineering Conference (CIBEC)最新文献_第2页

Deep Ensemble Learning for Skin Lesion Classification from Dermoscopic Images 基于皮肤镜图像的皮肤病变分类的深度集成学习

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/CIBEC.2018.8641815

Ahmed H. Shahin, A. Kamal, Mustafa Elattar

引用次数: 69

[Title page] (标题页)

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/cibec.2018.8641826

引用次数: 0

Automated classification of Bacterial Images extracted from Digital Microscope via Bag of Words Model 基于词袋模型的数字显微镜细菌图像自动分类

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/CIBEC.2018.8641799

B. A. Mohamed, H. Afify

{"title":"Automated classification of Bacterial Images extracted from Digital Microscope via Bag of Words Model","authors":"B. A. Mohamed, H. Afify","doi":"10.1109/CIBEC.2018.8641799","DOIUrl":"https://doi.org/10.1109/CIBEC.2018.8641799","url":null,"abstract":"The performance recognition of bacteria cell images is an effective survey for treatment of various diseases caused by the bacteria. Many algorithms for bacteria classification are designed for the needs of analysis of large-scale microscopic image bacteria. However, the biologist interpretation is suffered from insufficient information and thus may lead to limited accuracy in the bacteria classification process. To handle this drawback, machine learning tools, and image analysis approaches tackled identification of different bacteria species for improving the clinical microbiology investigation. In the proposed study, 200 bacterial images for ten different bacteria species with 20 images for each specie are extracted from DIBaS (Digital Images of Bacteria Species dataset). This proposed framework is divided into image preprocessing phase which obtained by histogram equalization, feature extraction by Bag-of-words model and classification phase by Support Vector Machine (SVM). The main objective is to enhance the bacterial images and find the image feature descriptors from the enhanced images which allowing to classify the bacterial images. The experimental results provided an average accuracy of 97% with classifier speed for automated detection and classification of bacterial images which would greatly reduce the disease outbreaks in future researches.","PeriodicalId":407809,"journal":{"name":"2018 9th Cairo International Biomedical Engineering Conference (CIBEC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114276256","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: 15

Improved Anomaly Detection in Low-Resolution and Noisy Whole-Slide Images using Transfer Learning 基于迁移学习的低分辨率和噪声整张图像的改进异常检测

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/CIBEC.2018.8641820

Wafaa A. Al-Olofi, M. Rushdi, Muhammad Islam, A. Badawi

{"title":"Improved Anomaly Detection in Low-Resolution and Noisy Whole-Slide Images using Transfer Learning","authors":"Wafaa A. Al-Olofi, M. Rushdi, Muhammad Islam, A. Badawi","doi":"10.1109/CIBEC.2018.8641820","DOIUrl":"https://doi.org/10.1109/CIBEC.2018.8641820","url":null,"abstract":"Whole-slide imaging (WSI) is one of the most recent technologies introduced in medical pathology practices. WSI images are created using a computerized system that scans, stitches and stores pathology specimen glass slides into digital images, which provide a multi-resolution pyramid construction of a huge gigabyte size due to the need for containing a high amount of tissue details. Therefore, digital WSI brings major challenges in data storage, image analysis and transmission (e.g. telepathology and interoperability). In this paper, we propose a computer-aided diagnosis (CAD) system to detect cancer anomalies in breast lymph node WSI images under low-resolution (LR) and noise conditions. In particular, we investigate a transfer-learning approach to find the scale mappings between WSI levels using partial least-square (PLS) regression. The learned scale mappings can be used to detect anomalies in LR images and hence reduce the computational cost of anomaly detection. Then, we explore the effect of different levels of noise on detection performance. We simulated different scenarios where WSI images are contaminated with Gaussian noise and several de-noising algorithms were applied, namely de-noising with PLS, Block Matching $3D (BM3D)$ and the combination of PLS and BM3D. We show that these de-noising algorithms can help reduce the noise severity on anomaly detection. For example, for noisy images with 0.8 noise standard deviation, these three algorithms improved the LR detection accuracy from 63.50% to 93.81%, 92.73%, and 97.51%, respectively. Our results lead to useful conclusions on how to handle whole slide images under scaling and noise conditions.","PeriodicalId":407809,"journal":{"name":"2018 9th Cairo International Biomedical Engineering Conference (CIBEC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124382909","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

Imaging Intralesional Heterogeneity in Multiple Sclerosis using a T2 Filter 用T2滤光片成像多发性硬化症的病灶内异质性

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/CIBEC.2018.8641803

R. Gabr, P. Narayana

引用次数: 0

Multimodal MRI Segmentation of Brain Tissue and T2-Hyperintense White Matter Lesions in Multiple Sclerosis using Deep Convolutional Neural Networks and a Large Multi-center Image Database 基于深度卷积神经网络和大型多中心图像数据库的多发性硬化症脑组织和t2 -高强度白质病变的多模态MRI分割

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/CIBEC.2018.8641800

P. Narayana, Ivan Coronado, M. Robinson, Sheeba J. Sujit, S. Datta, Xiaojun Sun, F. Lublin, J. Wolinsky, R. Gabr

{"title":"Multimodal MRI Segmentation of Brain Tissue and T2-Hyperintense White Matter Lesions in Multiple Sclerosis using Deep Convolutional Neural Networks and a Large Multi-center Image Database","authors":"P. Narayana, Ivan Coronado, M. Robinson, Sheeba J. Sujit, S. Datta, Xiaojun Sun, F. Lublin, J. Wolinsky, R. Gabr","doi":"10.1109/CIBEC.2018.8641800","DOIUrl":"https://doi.org/10.1109/CIBEC.2018.8641800","url":null,"abstract":"Multiple sclerosis (MS) is a demyelinating disease that affects the central nervous system (CNS) and is characterized by the presence of CNS lesions. Volumetric measures of tissues, including lesions, on magnetic resonance imaging (MRI) play key roles in the clinical management and treatment evaluation of MS patient. Recent advances in deep learning (DL) show promising results for automated medical image segmentation. In this work, we used deep convolutional neural networks (CNNs) for brain tissue classification on MRI acquired from MS patients in a large multi-center clinical trial. Multi-channel MRI data that included T1-weighted, dual-echo fast spin echo, and fluid-attenuated inversion recovery images were acquired on these patients. The pre-processed images (following co-registration, skull stripping, bias field correction, intensity normalization, and de-noising) served as the input to the CNN for tissue classification. The network was trained using expert-validated segmentation. Quantitative assessment showed high Dice similarity coefficients between the CNN and the validated segmentation, with DSC values of 0.94 for white matter and grey matter, 0.97 for cerebrospinal fluid, and 0.85 for T2 hyperintense lesions. These results suggest that deep neural networks can successfully segment brain tissues, which is crucial for reliable assessment of tissue volumes in MS.","PeriodicalId":407809,"journal":{"name":"2018 9th Cairo International Biomedical Engineering Conference (CIBEC)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114994387","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 Novel Full Hydrodynamic Focusing Design in Flow Cytometer 一种新颖的流式细胞仪全流体动力聚焦设计

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/CIBEC.2018.8641823

Mina A. Nagi, Nourhan A. Elbeheiry, R. Afify, Homan M. Shershira

引用次数: 0

Adaptive Fuzzy C-Means Algorithm using the Hybrid Spatial Information for Medical Image Segmentation 基于混合空间信息的自适应模糊c均值算法用于医学图像分割

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/CIBEC.2018.8641801

G. Gendy

引用次数: 1

Skin Cancer Classification using Deep Learning and Transfer Learning 使用深度学习和迁移学习的皮肤癌分类

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/CIBEC.2018.8641762

K. Hosny, M. A. Kassem, Mohamed M. Foaud

{"title":"Skin Cancer Classification using Deep Learning and Transfer Learning","authors":"K. Hosny, M. A. Kassem, Mohamed M. Foaud","doi":"10.1109/CIBEC.2018.8641762","DOIUrl":"https://doi.org/10.1109/CIBEC.2018.8641762","url":null,"abstract":"Skin cancer, specially melanoma is one of most deadly diseases. In the color images of skin, there is a high similarity between different skin lesion like melanoma and nevus, which increase the difficulty of the detection and diagnosis. A reliable automated system for skin lesion classification is essential for early detection to save effort, time and human life. In this paper, an automated skin lesion classification method is proposed. In this method, a pre-trained deep learning network and transfer learning are utilized. In addition to fine-tuning and data augmentation, the transfer learning is applied to AlexNet by replacing the last layer by a softmax to classify three different lesions (melanoma, common nevus and atypical nevus). The proposed model is trained and tested using the ph2 dataset. The well-known quantative measures, accuracy, sensitivity, specificity, and precision are used in evaluating the performance of the proposed method where the obtained values of these measures are 98.61%, 98.33%, 98.93%, and 97.73%, respectively. The performance of the proposed method is compared with the existing methods where the classification rate of the proposed method outperformed the performance of the existing methods.","PeriodicalId":407809,"journal":{"name":"2018 9th Cairo International Biomedical Engineering Conference (CIBEC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132806200","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: 122

Patient-dependent Freezing of Gait Detection using Signals from Multi-accelerometer Sensors in Parkinson’s Disease 基于多加速度传感器信号的帕金森病患者依赖冻结步态检测

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/CIBEC.2018.8641809

A. Ashour, A. El-Attar, N. Dey, M. M. A. El-Naby, Hatem Abd El-Kader

{"title":"Patient-dependent Freezing of Gait Detection using Signals from Multi-accelerometer Sensors in Parkinson’s Disease","authors":"A. Ashour, A. El-Attar, N. Dey, M. M. A. El-Naby, Hatem Abd El-Kader","doi":"10.1109/CIBEC.2018.8641809","DOIUrl":"https://doi.org/10.1109/CIBEC.2018.8641809","url":null,"abstract":"The position and number of the on-body wearable sensors affects significantly the acquired signal, which sequentially has a direct influence on the patient’s diagnosis. The patients of Parkinson’s disease (PD) suffer from freezing of the gait (FOG) in the form of episodes. In this paper, the choice of the acceleration sensors’ location, which measures the patient’s movement for monitoring the PD patient, was introduced using several episodes to develop a patient-dependent model for FOG detection. The proposed classification using the linear support vector machine (SVM) based FOG detection was applied to the ranked features using infinite feature selection (IFS) method to distinguish between the freezing and no-freezing events. A comparative study between the proposed IFS based detection model and the use of Eigenvector feature selection was conducted showing the same features ranking performance of the extracted features from all acceleration signals from the multi-sensors. However, the results established the superiority of the proposed patient-dependent model using IFS ranked features for FOG detection, which can be used to improve the PD monitoring systems accuracy.","PeriodicalId":407809,"journal":{"name":"2018 9th Cairo International Biomedical Engineering Conference (CIBEC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116068919","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