Affiliations: [a]
Department of ECE, Sona college of Technology Salem, Tamilnadu, India
| [b]
Department of ECE, Panimalar Engineering College, Chennai, Tamilnadu, India
| [c]
Department of Computational Intelligence, School of computing, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India
| [d]
Department of EEE, Muthayammal Engineering College (Autonomous), Rasipuram, Tamilnadu, India
Correspondence:
[*]
Corresponding author. V. Sudha, Department of ECE, Sona college of Technology Salem, Tamilnadu, India. E-mail: [email protected].
Abstract: An intelligent segmentation and identification of edemas diseases constitutes a most important crucial ophthalmological issues since they provide important information for the diagnosis process in accordance to the disease severity. But diagnosing the different edema diseases using the OCT-images are considered to be daunting challenge among the researchers. The implementation of computational intelligence techniques such as machine learning, deep learning, bio inspired algorithms and image processing techniques may help the doctors for some extent in improving the automatic extraction and diagnosis process consequently improving patients’ life quality. But, these are liable to more errors and less performance, which requires further improvisation in designing the intelligent systems for an effective classification of edema diseases. In this context, this paper proposes the hybrid intelligent framework for the identification, segmentation and classification of three types of edemas such as using the retinal optical coherence tomography (OCT) Images. In this process, Single Feed Forward Training networks (SLFTN) are integrated with Convolutional Layers whose hyperparameters are tuned by using Lion Optimization algorithm. An intensive experimentation is carried out using the Kaggle Retinal OCT Image datasets-2020 with Tensor flow and the proposed framework is trained with the different set of 84,494 images in which performance metrics such as accuracy, sensitivity, specificity, recall and f1score are calculated. Results shows the proposed system has provided satisfactory performance, reaching the average highest accuracy of 99.9% in identifying and classifying the respectively.
