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.
翻译:这项研究的目的是提出一种替代和混合解决办法,从从有麻风病和完好的肠胃动物身上采集的病理学图像中诊断该疾病。详细而言,混合方法的基础是利用图像处理和深层学习来取得更好的结果。从病理学图像中可靠地检测疾病被认为是医学图像处理中的一个公开问题,需要开发替代解决办法。在这方面,在与Burdur Mehmet Akif Ersoy大学、兽医医学院和病理学系联合研究中收集了520个病理学图像。人工检测和解释这些图像需要专门知识和大量处理时间。为此,兽医,特别是新招聘的医生,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。本研究的拟议解决办法是同时使用CLACHE方法和图像处理方法。在预处理后,通过VGG-16结构对进化神经网络进行分类来诊断。人工检测和解释这些图像需要大量处理时间。为此,兽医,特别是新招聘的医生,在开发这种疾病的检测和治疗方法时,非常需要用原始的98种方法来进行数据处理方法。