Computer based iridology scanning system

Iridology is a form of complementary medicine based on examining the pattern, color and other properties of the iris to determine information about the patient's systemic health. Today, many physicians use this form of analysis in conjunction with other healthcare techniques to better understand the healthcare needs of patients. However, this examination and description is very subjective and depends on the experience of the doctors. It is also a time consuming and exhausting process for physicians. In this context, in order to eliminate subjectivity in examination and identification and to reveal a more objective definition, a deep learning and image processing-based method is proposed in this study for the diagnosis of diabetes by using iridology card from iris images. In the proposed method, the iris boundaries were found and the pancreas region shown on the iridology card was removed from the iris fully automatically. With the image processing steps applied, an area related to the pancreas (diabetes) was found on the iris and automatic segmentation was made from the eye image. Afterwards, these images were applied to convolutional neural networks to diagnose diabetes and compared with different convolutional neural network architectures. As a result, the proposed method with VGG-16 architecture and automatic segmentation of the area of the pancreatic region was found to be more successful with 80% Accuracy, 100% Sensitivity, 71.42% Precision, 60% Specificity and 83.33% F1 Score performance metrics.