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Purpose: The diagnosis of intestinal parasitosis diseases relies on physiological symptoms and stools exam. Specialist physician are not enough and the stools exam manually done is slow, prone to error and not without negative effect on the eyes of laboratory assistant. We aim to design and implement a medical expert system helper for the diagnosis of human intestinal parasitosis diseases. Methods: The system follows a decision algorithm. The knowledge base was constructed through information coming from books and physician in charge of those diseases. The user interacts with the system by answering questions. Symptoms collected conduct to microscopic exam of stools run by the system with a priori suspected parasites. The automated microscopic exam of stools consists of a combined distance regularized level set evolution automatically initialized by circular Hough transform and a trained neuro-fuzzy classifier. The neuro-fuzzy classifier was trained for twenty human intestinal parasites considering noise and rotation. Results: we have integrated the reasoning scheme of diagnosis and automated clinical exam of stools in the same system. The parasites found in microscopic image confirm the suspicious disease. The final recommendation of diagnosis is completed with proposed appropriate therapy. We have evaluated our system on sixty case of infection with the diagnosis of two doctors and have obtained fifty eight correct diagnosis corresponding rate. Conclusions: The proposed system is automatic since the parameters of segmentation, features extraction and classification are set automatically guided by the type of suspicious parasite seek in the microscopic image. This is a notable contribution to medical healthcare assistance. DOI: 10.29011/ 2577-2260.100028

An Expert System for Assistance in Human Intestinal Parasitosis Diagnosis