  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.
