论文标题
使用深卷积神经网络进行预处理的组织病理学图像分类的重要性
Importance of Preprocessing in Histopathology Image Classification Using Deep Convolutional Neural Network
论文作者
论文摘要
这项研究的目的是提出一种一种替代性和混合解决方案方法，用于从旁结核和完整肠道的动物中从组织病理学图像中诊断疾病。详细说明，混合方法基于使用图像处理和深度学习以获得更好的结果。来自组织病理学图像的可靠疾病检测被称为医学图像处理中的一个开放问题，需要开发替代解决方案。在这种情况下，在Burdur Mehmet Akif ersoy大学，兽医学院和病理学系的联合研究中收集了520张组织病理学图像。手动检测和解释这些图像需要专业知识和很多处理时间。因此，兽医，尤其是新招募的医生，在开发这种疾病的检测和治疗方法时非常需要成像和计算机视觉系统。本研究中提出的解决方案方法是一起使用Clahe方法和图像处理。在此预处理后，诊断是通过对VGG-16体系结构进行的卷积神经网络SUP进行分类来进行的。此方法使用完全原始的数据集图像。将两种类型的系统应用于评估参数。尽管在没有数据预处理的情况下分类的方法中的F1分数为93％，但使用Clahe方法进行了预处理的方法是98％。
The aim of this study is to propose an alternative and hybrid solution method for diagnosing the disease from histopathology images taken from animals with paratuberculosis and intact intestine. In detail, the hybrid method is based on using both image processing and deep learning for better results. Reliable disease detection from histo-pathology images is known as an open problem in medical image processing and alternative solutions need to be developed. In this context, 520 histopathology images were collected in a joint study with Burdur Mehmet Akif Ersoy University, Faculty of Veterinary Medicine, and Department of Pathology. Manually detecting and interpreting these images requires expertise and a lot of processing time. For this reason, veterinarians, especially newly recruited physicians, have a great need for imaging and computer vision systems in the development of detection and treatment methods for this disease. The proposed solution method in this study is to use the CLAHE method and image processing together. After this preprocessing, the diagnosis is made by classifying a convolutional neural network sup-ported by the VGG-16 architecture. This method uses completely original dataset images. Two types of systems were applied for the evaluation parameters. While the F1 Score was 93% in the method classified without data preprocessing, it was 98% in the method that was preprocessed with the CLAHE method.
