A Research on Deep Learning Model for Diabetic Retinopathy Detection
Improving Diabetic Retinopathy Diagnosis using Deep Learning and FastAI
Keywords:
deep learning, model, diabetic retinopathy detection, image processing, illness detection, risk assessment, FastAI, Fast AI library, Convolution Neural Network, CNN, Google Colab's GPU system, neural network, diagnosis, retina scans, algorithm, dataset, ophthalmologists, patientsAbstract
Deep learning methods based on image processing, illness detection, and risk assessment are becoming more successful in healthcare. This research article proposes a model for diabetic retinopathy diagnosis using FastAI, where the outputs are improved by utilizing the Fast AI library and less code. This Convolution Neural Network (CNN) model is successful in terms of image processing, and it was trained using Google Colab's GPU system. The neural network that has been pre-programmed will assist in obtaining a fast and accurate result. This suggested approach compares pictures of diabetic retinopathy to normal retina scans. A modified algorithm with a bigger dataset may be developed in the future to identify all phases of diabetic retinopathy. This CNN is designed to help ophthalmologists diagnose patients.References
Ian Witten, Eibe Frank & Mark Hall, A. (2011). ‘Data Mining Practical Machine Learning Tools and Techniques’, 3rdedn, Morgan Kaufmann Publishers, USA
Ishtake, S.H. & Sanap, S.A. (2013). ‘Intelligent Heart disease Prediction System using Data Mining Techniques’, International Journal of Healthcare & BioMedical Research, vol.1, no. 3, pp. 94-101
Jayshri Sonawane, S., Dharmaraj Patil, R. & Vishal Thakare, S. (2013). ‘Survey on Decision Support System For Heart Disease, International Journal of Advancements in Technology, Vol. 4, No.1, pp. 89-96
Jagjeevan Rao, L. & Pavan Kumar, N.V.S. (2012). ’Analysis of Clinical Databases Using Data Mining Techniques’, International Journal of Advanced Research in Computer Science, vol. 3, no. 3, pp. 214-216
Jianchao Han, Juan Rodriguze & Mohsen Beheshti (2008). ‘Diabetes Data Analysis and Prediction Model Discovery Using Rapid Miner’, In Proceedings of the 2nd International Conference on Future Generation Communication and Networking, vol.3, pp. 96-99
John Peter, T. & Somasundaram, K. S. (2012). ‘Study and Development of Novel Feature Selection Framework for Heart Disease Prediction’, International Journal of Scientific and Research Publications, Vol. 2, No. 10, pp. 1-7
Karthikeyani, V. & Parvin Begum, I. (2012). ‘Comparative of Data mining classification algorithm in Diabetes disease Prediction’, International Journal of Computer Applications, vol. 60, no. 12, pp. 26-31
Lakshmi, K.R., Veera Krishna, M. & Prem Kumar, S (2013). ‘Performance Comparison of Data Mining Techniques for Predicting of Heart Disease Survivability’, International Journal of Scientific and Research Publications, vol. 3, no. 6, pp. 8-17
Lashari, S.A. & Ibrahim, R. (2013). ‘Comparative Analysis of Data Mining Techniques for Medical Data Classification’, In Proceedings of 4th International Conference on Computing and Informatics’, Sarawak, Malaysia, pp. 365-370
Latha Parthiban & Subramanian, R. (2007). ‘Intelligent Heart Disease Prediction System using CANFIS and Genetic Algorithm’, International Journal of Biological and Life Sciences, Vol. 3, No. 3, pp. 157-160.
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