Decision Making Based on Fuzzy Aggregation Operators for Medical Diagnosis from Dental X-ray images

Medical diagnosis is considered as an important step in dentistry treatment which assists clinicians to give their decision about diseases of a patient. It has been affirmed that the accuracy of medical diagnosis, which is much influenced by the clinicians' experience and knowledge, plays an important role to effective treatment therapies. In this paper, we propose a novel decision making method based on fuzzy aggregation operators for medical diagnosis from dental X-Ray images. It firstly divides a dental X-Ray image into some segments and identified equivalent diseases by a classification method called Affinity Propagation Clustering (APC+). Lastly, the most potential disease is found using fuzzy aggregation operators. The experimental validation on real dental datasets of Hanoi Medical University Hospital, Vietnam showed the superiority of the proposed method against the relevant ones in terms of accuracy.