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Detection of Necrosis in Mice Liver Tissue Using Deep Convolutional Neural Network

机译:深度卷积神经网络在小鼠肝组织中坏死的检测

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Acute Hepatic Necrosis is an early sign of liver dysfunction. Liver dysfunction is one of the major reasons for increasing death rate. Accurate diagnosis in less time, along with proper medication, show a ray of hope in controlling the aggravation of the situation. To overcome the unfavorable effects of harmful drugs, medicinal plant extract has become major thrust area nowadays. This research work has presented a way to show the improvements in mice liver tissue after applying the designated composition of the plant extract. And the performance is measured with our designed deep convolutional neural network (CNN) architecture along with the preprocessing techniques that has shown to be competent to classify microscopic images of mice hepatic tissues. Considering a small database of 30 images, we introduced a preprocessing stage which included the dividing of the original microscopic images to small patches. The accuracy of the classification results using the proposed CNN based classifier was 99.33%.
机译:急性肝坏死是肝功能异常的早期迹象。肝功能障碍是死亡率增加的主要原因之一。在更短的时间内进行准确的诊断以及适当的药物治疗,对控制局势的恶化显示出一线希望。为了克服有害药物的不利影响,药用植物提取物已成为当今的主要推销领域。这项研究工作提出了一种方法,该方法显示了使用指定植物提取物成分后小鼠肝脏组织的改善。通过我们设计的深度卷积神经网络(CNN)架构以及预处理技术对性能进行测量,该预处理技术已被证明能够对小鼠肝组织的显微图像进行分类。考虑到一个包含30张图像的小型数据库,我们引入了一个预处理阶段,其中包括将原始显微图像划分为小块。使用提出的基于CNN的分类器进行分类的准确性为99.33%。

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