Abstract Background and objective The diagnosis of intestinal parasitosis disease relies on physiological symptoms and stool examination. Often, few specialists are available, and manual stool exam is slow, prone to error, and can cause eye fatigue. Our aim was to design and implement a medical expert system that would be automated and helpful for diagnosis of human intestinal parasitosis. Methods The system was developed based on a decision algorithm. A knowledge base was constructed through information gleaned from books and physicians with information pertaining to the disease. The user interacts with the system by answering questions. The symptoms information collected led to a microscopic examination of stools, which was run on the system to detect parasites. The paradigm for automated microscopic examination of stools consisted of a combined distance regularized level set evolution, automatically initialized by a circular Hough transform, and a trained neuro-fuzzy classifier. The neuro-fuzzy classifier was trained for analysis of twenty human intestinal parasites. Results We combined the reasoning scheme of diagnosis and the automated clinical exam of stools in the same system. The parasites found in microscopic imagery confirmed the suspicious disease. The final recommendation of diagnosis was then completed, with appropriate proposed therapy. The system was evaluated with sixty cases of infection, and compared to the diagnosis of two expert doctors; we obtained fifty eight correct diagnoses, corresponding to a 96.6% accuracy. Conclusions The proposed system is automated, since the parameters of segmentation, feature extraction and classification are set to be computationally guided by the type of suspicious parasite. The system is potentially an important contribution for medical healthcare assistance.