A novel diabetic retinopathy detection from fundus images using hybrid quantum convolutional neural network models.
Authors
Affiliations (7)
Affiliations (7)
- Department of Information Technology, K.S.R. College of Engineering (Autonomous), Tiruchengode, Namakkal, Tamil Nadu, India.
- Department of Electrical and Electronics Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu, India.
- Department of Information Technology, Maharaja Surajmal Institute of Technology, Affiliated to GGSIP University, Janakpuri, New Delhi, India.
- Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India. [email protected].
- Department of Computer Science and Engineering, RMK College of Engineering and Technology, Puduvoyal, Thiruvallur, Tamil Nadu, India.
- Faculty of Informatics and Computing, Singidunum University, Danijelova 32, Belgrade 11000, Serbia, Danijelova 32, Belgrade 11000, Serbia.
- Department of Mathematics, Saveetha School of Engineering, SIMATS Thandalam, Tamilnadu, 602105, Chennai, India.
Abstract
Diabetic retinopathy (DR) diagnosis from digital fundus images is a long-standing topic of research in medical image processing. The determination of optic disk boundaries in two-dimensional retinal images is difficult due to blurred edges, which makes this field in need of improvement. All these problems cannot be solved by a single technique. An efficient algorithm for identifying DR-related retinal changes and structure is still needed. If DR is recognized and treated in a timely manner, visual deterioration can be managed or avoided. It is based on telemedicine analysis of color fundus pictures or clinical evaluations by medical doctors. However, due to intrinsic human subjectivity, both systems are time-consuming, labor-intensive, and prone to inaccuracy. Due to their great specificity and sensitivity, automated methods capable of analyzing color fundus pictures have become important for the general deployment of DR screening. To study the existence of DR-related characteristics and to cope with the various diabetes severity diagnosis phases, a hybrid quantum convolutional neural network (HQCNN) is presented. Kaggle fundus images database is utilized to test and train the network. Finally, the presented work is compared for analyzing efficiency using the system of measurement like precision, specificity, accuracy, sensitivity, and f1 score. The proposed work obtains accuracy of 98.89%, sensitivity of 99.37%, specificity of 99.57%, precision of 98.89%, and F1 score of 97.58%.