Iris Diagnosis – A Quantitative Non-Invasive Tool for Diabetes Detection
Iris Diagnosis is a novel approach to assess various pathological conditions based on the Iris patterns. Iris Image analysis is a non-invasive technique for determining the health condition of an individual. Correct and timely diagnosis is critical, yet is the absolute requirement of medical science. In general, current approaches fail to diagnose various diseases correctly. An attempt is being made in the current research to explore the possibility of diagnosing diabetes from the representations of the Iris. Initially the images of eye are captured using Iridoscope and a database is created with their clinical history. Various algorithms are developed to assess the quality of the Iris image, and then segmentation and feature extraction techniques such as GLCM and DCT are applied. Feature extraction plays a vital role in assessment of the individual to be diabetic or not. In order to assess the presently proposed approach, 30 patient data were acquired for which the present approach was able to detect diabetic or not with an accuracy of 83%.
- Wibawa, et. al. developed a new framework for early detection on the condition of pancreas as a cause of diabetes mellitus by real time image processing.
- Salankar, et al provided recognition rate of various features extraction methods such as Gabor Wavelet, DCT, Haar Transform, PCA, Log Gabor Wavelet based on CASIA iris database.
- Elgamal, et al developed a robust Iris matching system by using several image processing techniques.
DISADVANTAGES OF EXISTING SYSTEM:
- Existing system fail to diagnose various diseases correctly.
- Accuracy is less.
- The main purpose of this research was to apply image processing algorithms via computer using MATLAB code to the images acquired and find out whether the patient is diabetic or not.
ADVANTAGES OF PROPOSED SYSTEM:
- The proposed system achieves in diagnosing disease correctly.
- The proposed system is more accurate than the existing systems.
- System : Pentium Dual Core.
- Hard Disk : 120 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram :2 GB
- Operating system : Windows 10.
- Coding Language : MATLAB
- Tool : MATLAB 2019
Mithun B S, R Sneha, Vinay Raj K, Basavaraj Hiremath and B. Ragavendrasamy, “Iris Diagnosis – A Quantitative Non-Invasive Tool for Diabetes Detection”, 2018.