Deep learning based chronic kidney disease detection through iris
Kidney is an important organ in human body as it maintains the nutrients and fluid balance in our body. It is extremely beneficial if its dysfunctionality is diagnosed at an early stage. Iridology provides a pathway to examine the kidney disease through iris images. Therefore, in this work we proposed the Iris-based Kidney Disease Identification System (IKDIS). The IKDIS would aid in identifying abnormalities through iris images an input which would be followed by application of deep neural network model for assessment. This type of diagnostic system without involving any instruments for assessment of human body organs is much popular these days. The data of 49 patients gives promising results of IKDIS, achieving overall accuracy of 86.9% during the experiment.
In this study Iris-based Kidney Disease Identification System is proposed to diagnose dysfunctional kidneys based on machine learning algorithm and iridology. This type of alternative medical diagnostic system recognizes discrepancies in iris texture and aids the medical staff to identify the disease at early stages. In future, new CNN architecture will hopefully be developed and applied to detect kidney problem more efficiently and with high accuracy.
H A U Rehman1, C Y Lin1 and S F Su2
Published under licence by IOP Publishing Ltd
Citation H A U Rehman et al 2021 J. Phys.: Conf. Ser. 2020 012047