Deep Learning-Based Diagnosis Method of Emergency Colorectal Pathology.

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.