Ocular disease examination of fundus images by hybriding SFCNN and rule mining algorithms

ABSTRACT

Eye is the organ of vision, which is necessary for daily routine, disorders can prevent it from working effectively. Human eye can be harmed, including age, strain, poor care, and using medications without a prescription. Choroid behind the retina are affected by retinal disorders. Small and big blood vessels compressed densely and affected by light, in order to perform multi-label categories of fundus images and properly handle the issue of interleaved overflow in fundus image lesions. We proposed Single population leapfrog optimization convolutional neural network algorithm (SFCNN) and Rule mining algorithm for self-closing computer investigation of ocular disease related to retinal images, which makes the work easier for ophthalmologists to state the state of the disease. For classification hybrid techniques SFCNN and EARMAM are proposed to predict the diseased image. When comparing with existing approach, proposed approach yields an accuracy of 98.75%, a sensitivity of 97%, and specificity of 98%.

KEYWORDS:

• Human eye
• data mining and SF-CNN algorithm
• deep learning
• fundus image
• ophthalmologist

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

J. Harikiran

Dr J. Harikiran working as Associate professor, School of CSE, VIT-AP University, Amaravathi. His research interests are microarray image processing, human activity recognition and medical image analysis and so on. He can be reached at [email protected].

B. Sai Chandana

Dr. B. Sai Chandana, working as Assistant Professor Sr.Grade 2 in the school of Computer Science and Engineering. Her research interests include hyperspectral image processing, remote sensing, medical image analysis, and activity recognition. She can be reached at [email protected].

B. Srinivasa Rao

Dr. B. Srinivasa Rao working as Associate Professor in School of Computer Science & Engineering (SCOPE), VIT-AP University. His research index of soft computing, image processing, Machine learning and deep learning. He can be reached at [email protected]

B. Raviteja

Dr. B. Raviteja, B.Tech. M.Tech, PhD. is serving as an Assistant Professor, GITAM Deemed-to-be-University, Hyderabad, Telangana. Also, he has Doctorate degree in Computer Science and Systems Engineering (Andhra University, Visakhapatnam), Master’s degrees in Information Technology (Swinburne University of Technology, Melbourne, Australia). He is having one decade of experience in academic, administration, research and innovations. He can be reached at [email protected]