Importance of Preprocessing in Histopathology Image Classification Using Deep Convolutional Neural Network

TL;DR

This study demonstrates that combining image preprocessing with CLAHE and deep learning using VGG-16 significantly improves the accuracy of diagnosing paratuberculosis in histopathology images.

Contribution

It introduces a hybrid approach integrating image preprocessing and deep learning for better disease detection in histopathology images.

Findings

Preprocessing with CLAHE increased F1 score from 93% to 98%.

Hybrid method enhances diagnostic accuracy.

Original dataset used for training and evaluation.

Abstract

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.