International Journal of Electrical and Computer Engineering (IJECE) Vol. 14, No. 4, August 2024, pp. 4005~4017 ISSN: 2088-8708, DOI: 10.11591/ijece.v14i4.pp4005-4017  4005 Journal homepage: http://ijece.iaescore.com Optimizing glaucoma diagnosis using fundus and optical coherence tomography image fusion based on multi-modal convolutional neural network approach Nanditha Krishna 1,2 , Nagamani Kenchappa 3 1 Department of Electronics and Telecommunication Engineering, RV College of Engineering, affiliated to Visvesvaraya Technological University, Belagavi, India 2 Department of Medical Electronics Engineering, Dayananda Sagar College of Engineering, Bengaluru, India 3 Department of Electronics and Telecommunication Engineering, RV College of Engineering, Bengaluru, India Article Info ABSTRACT Article history: Received Feb 2, 2024 Revised Mar 20, 2024 Accepted Apr 2, 2024 A novel approach that combines segmented fundus images (FIs) and optical coherence tomography image (OCTIs) are presented here, by incorporating deep learning network (DLN) techniques, to address the imperative need for advanced diagnostic algorithms in detecting and classifying glaucoma. By combining these two images, glaucoma diagnoses are made to improve the accuracy with more reliability. Multi modal convolutional neural networks (MMCNNs) are proposed for automatically extracting discriminatory features from both segmented FIs and OCTIs, allowing for comprehensive ocular analysis. A significant improvement in glaucoma diagnosis is achieved through segmentation of both FIs and OCTIs, ensuring robustness generalization to diverse clinical scenarios, DLN models are trained on datasets encompassing a wide range of glaucoma cases. The integrated approach outperforms individual modalities in terms of early detection of glaucoma and accurate classification. This method demonstrates promising potential in early glaucoma detection due to its effectiveness. By combining segmented features from both FIs and OCTIs through MMCNNs, improved efficiency in diagnosing predominant ocular glaucoma disorder is achieved compared to existing methods. Within the scope of this research, GoogLeNet (GN) is applied to independently classify glaucoma (uni-modal) in segmented FIs and OCTIs, providing a basis for comparison with the evaluation of MMCNNs. Keywords: Deep learning network Fundus images Glaucoma Multi modal convolutional neural network Optical coherence tomography images This is an open access article under the CC BY-SA license. Corresponding Author: Nanditha Krishna Department of Electronics and Telecommunication Engineering, RV College of Engineering, Bengaluru, affiliated to Visvesvaraya Technological University Belagavi, Karnataka, India Email: nanditha13@gmail.com 1. INTRODUCTION Glaucoma, characterized by its progressive nature, poses a risk of permanent vision impairment if not identified and managed promptly. Recognizing and preventing glaucoma early is essential in addressing this severe eye ailment, which can lead to vision loss and irreversible blindness. Traditional diagnostic approaches for glaucoma are time-consuming, subject to human error, and inefficient, emphasizing the need for automated diagnosis to improve precision and streamline the process. Multiple research articles have been published, each proposing different approaches to detect glaucoma. Introduces deep learning network (DLN) technology for early glaucoma detection, utilizing U-Net combined with transfer learning frameworks for