INTEGRATION OF MEDICAL PHOTOGRAMMETRY AND GAS NEURAL NETWORK FOR INTELLIGENT DISEASE DIAGNOSIS

Abstract

Close range photogrammetry is an image-based method of measurement that can be used to create a three-dimensional model of objects. This method is very popular due to its high speed, low cost, and non-invasiveness as a measurement tool in medicine. The main weakness of these systems is the lack of topology between points, which is especially important in the medical field. The neural gas network can learn the topology of the points associated with the 3D model of objects. Considering the capabilities mentioned regarding the photogrammetry and the neural gas network, a combination of these can be used as a diagnostic tool in such a way that in addition to considering the points of the model, neighboring points are also considered in the diagnostic process. Accordingly, in this study, we want to design a system by combining these two tools that can recognize diseases by their apparent symptoms. Moreover, the use of the neural gas network and the possibility of local and general examination of the organs increase the accuracy of diagnosis. Diagnosis of foot disease has been used as a case study in this system. The results showed that the neural gas network has a high degree of flexibility for modeling the human body compared to previous methods, and provides a better approximation. Also, the accuracy of reconstruction of the 3D model of the object is effective in the process of diagnosis and influences the level of intelligence of the system as well. Finally, in the system implemented, the results showed that the disease was correctly diagnosed in all 5 feet of the patient. Also, in 4 cases, the location of the disease was correctly detected and in one case the location of the disease was misdiagnosed.