Hybrid generative model for grading the severity of diabetic retinopathy images

ABSTRACT

One of the common eye conditions affecting patients with diabetes is diabetic retinopathy (DR). It is characterised by the progressive impairment to the blood vessels with the increase of glucose level in the blood. The grading efficiency still finds challenging because of the existence of intra-class variations and imbalanced data distributions on the retinal images. Traditional machine learning techniques utilise hand-engineered features for classification of the affected retinal images. As convolutional neural network produces better image classification accuracy in many medical images, this work utilises the CNN-based feature extraction method. This feature has been used to build Gaussian mixture model (GMM) for each class that maps the CNN features to log-likelihood dimensional vector spaces. Since the Gaussian mixture model can be realised as a mixture of both parametric and nonparametric density models and has their flexibility in capturing different data distributions, probabilistic outputs, interpretability, efficient parameter estimation, and robustness to outliers, the proposed model aimed to obtain and provide a smooth approximation of the underlying distribution of features for training the model. Then these vector spaces are trained by the SVM classifier. Experimental results illustrate the efficacy of the proposed model with accuracy 86.3% and 89.1%, respectively.

KEYWORDS:

• Retinal images
• CNN feature extraction
• support vector machine
• Gaussian mixture model

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Notes on contributors

R. Bhuvaneswari

R. Bhuvaneswari (Member, IEEE) received the Ph.D. degree from Anna University. She is currently an Assistant Professor with the Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, India. She has 18 years of teaching experience in the field of engineering. She has authored over many publications on international journals and international conferences and co-authored a book on computer graphics. Her research interests include machine learning and deep learning for image processing applications.

M. Diviya

M.Diviya received the M.E . degree from Anna University. Currently pursuing Ph.D in Vellore Institute of Technology, Chennai. She is currently an Assistant Professor with the Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, India. She has 7 years of teaching experience in the field of engineering. She has authored over many publications on international journals and international conferences and book chapters. Her research interests include machine learning and deep learning for image processing,text processing applications.

M. Subramanian

Subramanian M received a BE degree in Mechanical Engineering from 2008, and he obtained ME degrees in computer aided design and engineering design in 2011 and 2013, respectively. He is pursuing his PhD degree from Anna University, Chennai, Tamilnadu, India in the field of material science and engineering. Currently, he serves as an assistant professor in mechanical engineering department at St.Joseph’s College of Engineering, affiliated to Anna University, Chennai, Tamilnadu, India. His research focusses on material science and metallurgy, machining science, machine learning, image processing and optimization techniques.

Ramya Maranan

Ramya Maranan is an accomplished researcher working in the Department of Research and Innovation at Saveetha School of Engineering, SIMATS in Chennai, India. With a passion for pushing the boundaries of knowledge and driving innovation, Ramya plays a vital role in advancing the research activities of the institution. Ramya’s work primarily revolves around conducting research and development activities within their area of specialization. They are involved in designing and executing experiments, collecting and analyzing data, and disseminating their findings through scholarly publications. Ramya’s dedication to research demonstrates its commitment to advancing scientific understanding and promoting technological advancements. Their work has the potential to create a positive impact on society and contribute to the overall academic and scientific community.

R Josphineleela

R. Josphineleela has received her Ph.D(Computer Science Engineering) from Sathyabama University, India in 2013. She has completed her M. E (computer science and Engineering) in sathyabama University. She has more than 20 years’ experience in the field of Computer Science and she is currently working as a Professor in the department of Information Technology at Panimalar Institute of Technology. She has published more than 50 papers in national and international level. Her research is in the field of Image processing, Neural Network, Artificial Intelligence, Biomedical Imaging and Soft Computing etc. She has received a Distinguished Professor award from Computer Society of India and received Best Project award from Dr.Kalam Educational Trust for Tribal University, Best Teacher Award from IEAE. She got Best Paper award from Computer Society of Ind she has received “Certificate of Appreciation” for contributing as a Proctor in “IEEEXtreme 12.0” Programming. She has received “In appreciation for fostering an ecosystem bridging Government, Industry and Academia award” from “India Innovation challenge design contest 2018 from DST & Texas Instrument”.