Differentiating Crohn’s disease from intestinal tuberculosis using a fusion correlation neural network

Abstract

Crohn’s disease (CD) and intestinal tuberculosis (ITB) have many similarities and overlaps in their clinical manifestations and medical imaging features, which makes it difficult to accurately differentiate CD from ITB. Random forests have unique advantages in discovering specific indicators and measuring relevance. By fusing the attributes and the correlation information between the sample and training set, we propose a fusion correlation neural network (FCNN) to diagnose CD and ITB patients. A total of 287 cases with fifty-nine indicators were collected from the Second Xiangya Hospital of Central South University. An FCNN allows one to explain the feature selection and learning classification rules in neural networks. The experiments show that perianal fistulas, skin and mucosal lesions, intestinal perforation, comb sign, and cobblestone appearance tend to be associated with CD; and ascites show a higher association with intestinal tuberculosis. These conclusions are consistent with the experience of clinicians. Finally, the age, disease course, white blood cell, hemoglobin, platelets, C-reactive protein, and T-SPOT of patients were used to train the FCNN. The average accuracy of the FCNN exceeds 91%, showing better performance than existing linear regression standards and other machine learning models.