Microscopic Blood Smear Segmentation and Classification Using Deep Contour Aware CNN and Extreme Machine Learning

机译：使用深度轮廓意识的CNN和极限机器学习微观血液涂片分割和分类

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Recent advancement in genomics technologies has opened a new realm for early detection of diseases that shows potential to overcome the drawbacks of manual detection technologies. In this work, we have presented efficient contour aware segmentation approach based based on fully conventional network whereas for classification we have used extreme machine learning based on CNN features extracted from each segmented cell. We have evaluated system performance based on segmentation and classification on publicly available dataset. Experiment was conducted on 64000 blood cells and dataset is divided into 80% for training and 20% for testing. Segmentation results are compared with the manual segmentation and found that proposed approach provided with 98.12% and 98.16% for RBC and WBC respectively whereas classification accuracy is shown on publicly available dataset 94.71% and 98.68% for RBC & its abnormalities detection and WBC respectively.

机译：基因组学技术的最新进步开辟了一种新的领域，用于早期检测显示克服手动检测技术缺点的潜力。在这项工作中，我们提出了基于完全传统网络的基于完全传统网络的高效轮廓感知分割方法，而对于从每个分段单元中提取的CNN特征，我们使用了极限机器学习。我们根据公共数据集的分段和分类评估了系统性能。实验在64000血细胞上进行，数据集分为80 ％，以进行训练和20％的测试。将分段结果与手动分割进行比较，发现分别为RBC和WBC提供了98.12 ％和98.16 ％的建议方法，而分类准确性在公共数据集94.71 ％和98.68 ％上显示RBC及其异常检测和其异常检测WBC分别。

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《IEEE Conference on Computer Vision and Pattern Recognition Workshops》|2017年|1 v.|共7页
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Muhammad Imran Razzak; Saeeda Naz;
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中图分类 TP391.41;
关键词
Blood; Image segmentation; Feature extraction; Shape; Diseases; Image color analysis; Microscopy;

机译：血液;图像分割;特征提取;形状;疾病;图像颜色分析;显微镜;

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2. Image segmentation and classification of white blood cells with the extreme learning machine and the fast relevance vector machine [J] . Ravikumar S. Artificial cells, nanomedicine, and biotechnology. . 2016,第1a4期

机译：用极限学习机和快速相关矢量机对白细胞进行图像分割和分类
3. Image segmentation and classification of white blood cells with the extreme learning machine and the fast relevance vector machine [J] . S. Ravikumar Artificial cells, nanomedicine, and biotechnology. . 2016,第3期

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4. Microscopic Blood Smear Segmentation and Classification Using Deep Contour Aware CNN and Extreme Machine Learning [C] . Muhammad Imran Razzak, Saeeda Naz IEEE Conference on Computer Vision and Pattern Recognition Workshops . 2017

机译：使用深度轮廓感知CNN和极限机器学习的微观血液涂片分割和分类
5. Deep Learning of Unified Region, Edge, and Contour Models for Automated Image Segmentation [D] . Hatamizadeh, Ali. 2020

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Microscopic Blood Smear Segmentation and Classification Using Deep Contour Aware CNN and Extreme Machine Learning

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