Deep Learning-Based Diagnosis Method of Emergency Colorectal Pathology.

Chen Wang,Ning Zhang

doi:10.1155/2021/3927828

Chen Wang, Ning Zhang

Open Access

https://doi.org/10.1155/2021/3927828

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Abstract

One of the most common malignant tumors of the digestive tract is emergency colorectal cancer. In recent years, both morbidity and mortality rates, particularly in our country, are getting higher and higher. At present, diagnosis of colorectal cancer, specifically in the emergency department of a hospital, is based on the doctor's pathological diagnosis, and it is heavily dependent on the doctor's clinical experience. The doctor's workload is heavy, and misdiagnosis events occur from time to time. Therefore, computer-aided diagnosis technology is desperately needed for colorectal pathological images to assist pathologists in reducing their workload, improve the efficiency of diagnosis, and eliminate misdiagnosis. To address these issues, a gland segmentation of emergency colorectal pathology images and diagnosis of benign and malignant pathology is presented in this paper. Initially, a multifeatured auxiliary diagnosis is designed to enable diagnosis of benign and malignant diagnosis of emergency colorectal pathology. The proposed algorithm constructs an SVM-enabled pathological diagnosis model which is based on contour, color, and texture features. Additionally, their combination is used for pathological benign and malignant pathological diagnosis of two types of data sets D1 (original pathological image dataset) and D2 (dataset that has undergone glandular segmentation) diagnosis. Experimental results show that the proposed pathological diagnosis model has higher diagnostic accuracy on D2. Among these datasets, SVM based on the multifeature fusion of contour and texture achieved the highest diagnostic accuracy rate, i.e., 83.75%, which confirms that traditional image processing methods have limitations. Diagnosing benign and malignant colorectal pathology in an emergency is more difficult and must be treated on a priority basis. Finally, an emergency colorectal pathology diagnosis method, which is based on deep convolutional neural networks such as CIFAR and VGG, is proposed. After configuring and training process of the two networks, trained CIFAR and VGG network models are applied to the diagnosis of both datasets, i.e., D1 and D2, respectively.

Highlights

According to the Global Cancer Report data, which was released by the International Cancer Institute, the number of new cancer cases and mortality rates in the world were increasing in 2012 whereas nearly half of the new cancer cases occurred in Asia and most of these were in China [1]
In the diagnosis process of emergency colorectal cancer, the diagnosis method of pathologists is time-consuming and highly subjective and easy to cause misdiagnosis events, which is very unfavorable to the treatment of patients
Erefore, there is an urgent need to study the computeraided diagnosis technology of colorectal pathological images to provide doctors with accurate auxiliary analysis results along with speeding up the treatment process. To address these problems of emergency colorectal pathological diagnosis, a deep learning-based hybrid method, which is based on gland segmentation method and two pathological diagnosis methods, is presented. e main contributions of this paper are as follows

Summary

Introduction

According to the Global Cancer Report data, which was released by the International Cancer Institute, the number of new cancer cases and mortality rates in the world were increasing in 2012 whereas nearly half of the new cancer cases occurred in Asia and most of these were in China [1]. E activation function in the traditional neural network structure generally uses the Sigmoid system such as Logistic-Sigmoid and Tanh-Sigmoid. We have observed that expressions and images in which Sigmoid and Tanh functions are the activation functions of the deep convolutional neural network have obvious defects; that is, these are both saturated nonlinear functions. Compared with Sigmoid and Tanh, ReLU and Softplus have a relatively wide excitation boundary, which is closer to the activation model of human brain neurons. Because ReLU has the characteristics of unilateral inhibition, this makes the output of some neurons be 0 during the training process, making the network sparse, thereby reducing the interdependence between parameters, which can reduce overfitting to a certain extent. Softplus does not have sparseness; the ReLU function is more in line with the actual activation model of biological neurons

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Conclusion