ODGNet: a deep learning model for automated optic disc localization and glaucoma classification using fundus images

Research Article ODGNet: a deep learning model for automated optic disc localization and glaucoma classification using fundus images Jahanzaib Latif1 · Shanshan Tu1 · Chuangbai Xiao1 · Sadaqat Ur Rehman2 · Azhar Imran3 · Yousaf Latif4 Received: 10 September 2021 / Accepted: 3 February 2022 © The Author(s) 2022  OPEN Abstract Glaucoma is one of the prevalent causes of blindness in the modern world. It is a salient chronic eye disease that leads to irreversible vision loss. The impediments of glaucoma can be restricted if it is identified at primary stages. In this paper, a novel two-phase Optic Disk localization and Glaucoma Diagnosis Network (ODGNet) has been proposed. In the first phase, a visual saliency map incorporated with shallow CNN is used for effective OD localization from the fundus images. In the second phase, the transfer learning-based pre-trained models are used for glaucoma diagnosis. The transfer learning-based models such as AlexNet, ResNet, and VGGNet incorporated with saliency maps are evaluated on five public retinal datasets (ORIGA, HRF, DRIONS-DB, DR-HAGIS, and RIM-ONE) to differentiate between normal and glaucomatous images. This study’s experimental results demonstrate that the proposed ODGNet evaluated on ORIGA for glaucoma diagnosis is the most predictive model and achieve 95.75, 94.90, 94.75, and 97.85% of accuracy, specificity, sensitivity, and area under the curve, respectively. These results indicate that the proposed OD localization method based on the saliency map and shallow CNN is robust, accurate and saves the computational cost. Keywords Glaucoma detection · Optic disk localization · Fundus images · Saliency map · Retinal diseases · Transfer learning 1 Introduction Glaucoma is one of the predominant causes of visual disability globally, which accounts for more than 12% of overall blindness [1]. Glaucoma is a salient chronic eye disease that leads to irreversible vision loss if it is not detected and cured at earlier stages. It is an enlightened optic neuropathy visible within the macula and optic disc [2]. According to the World Health Organization (WHO), glaucoma cases will rise up to 76 million by 2020 [3], which is about 3-5% global occurrence of glaucoma for 40-80 years older people. Glaucoma progressively damages the optic nerve by degenerating the nerve fibers, which causes visual impairment leading to blindness [4]. Fig. 1 shows the severity levels of glaucoma in fundus images. Glaucoma is primarily classified into two types based on the increased intraocular pressure (IOP): open-angle and angle-closure glaucoma. In both types of glaucoma, the liquid’s affluence termed as Aqueous Humor (AH) is congested and led to rising the IOP behind the eye and influencing the optic nerve head (ONH). Most of the existing studies utilized three common methods to diagnose * Shanshan Tu, ; Chuangbai Xiao, ; Sadaqat Ur Rehman, ; Azhar Imran, | 1Engineering Research Center of Intelligent Perception and Autonomous Control, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China. 2Department of Computer Science, Namal Institute, Mianwali 42250, Pakistan. 3Department of Creative Technologies, Air University, Islamabad 44000, Pakistan. 4School of Economics, Nankai University, Tianjin 300071, China. SN Applied Sciences (2022) 4:98 | https://doi.org/10.1007/s42452-022-04984-3 Vol.:(0123456789) Research Article SN Applied Sciences (2022) 4:98 | https://doi.org/10.1007/s42452-022-04984-3 Fig. 1  An example of various glaucoma severity levels: a normal disc b mild c moderate d severe glaucoma i.e., IOP measurement, visual field test, and ONH diagnosis. Early detection and continuous screening may lessen the blindness rate up to 50% [5], but manual screening is a tedious and time taking effort. Therefore, an automated method is essential for the detection of glaucoma. Computer-aided diagnosis (CAD) is a cost-effective technique for early-stage glaucoma detection in retinal fundus images. It is important to develop a CAD system for glaucoma diagnosis to assist the ophthalmologists for a better screening process. Machine learning (ML) based glaucoma diagnosis systems achieved remarkable accuracy from 90%-98% based on handcrafted features and different classifier type, as given in Table 1. [6–17]. Usually, cup-to-disc ratio (CDR) is evaluated by applying various feature extraction techniques such as wavelet transform [12, 13], thresholding [18, 19] or high order spectral transforms [15, 16] on OD images. Then these manually extracted features are fed into ML classifiers like Support Vector Machine (SVM), Artificial Neural Network (ANN), k-nearest neighbor (kNN) and Random Forests (RF), etc. Although ML-based methods attained state-of-the-art performance results, the manual feature extraction and selection are time-consuming effort and based on the ophthalmologist’s subjectivity. Recently, deep learning (DL) has emerged as the most employed field for various tasks such as image classification [20], natural language processing [21], and medical image analysis [22]. A convolutional neural network (CNN) is the class of DL, which is commonly utilized for image classification [23]. Maheshwari et al. [24] developed a glaucoma diagnosis system by employing a local binary pattern (LBP) based on data augmentation and retinal fundus images. Initially, they extracted red, green, and blue channels of the fundus images separately and then employed LBP for data augmentation of each channel. Finally, the fusionbased technique is used to combine the decisions from the corresponding CNN model. A glaucoma diagnosis system based on the optic disc and cup localization has been introduced in [25]. An ML-based system is used for the segmentation of optic disc and cup and CNN based system for glaucoma diagnosis. The proposed method Table 1  State-of-the-art ML-based techniques for Glaucoma Diagnosis Ref Approach Dataset Classifier Accuracy [6] Cross entropy-based feature extraction, Empirical wavelet transformation [7] GLCM and Haralick features [8] Radon and modified census transformation, GIST descriptor [9] OC and OD segmentation using Entropy Sampling [10] Discrete Wavelet Transform (DWT) based textural features Private 60 images, Public MIAG dataset LS-SVM 0.9833 [11] Texton and local configuration pattern (LCP) features, sequential floating feature selection (SFFS) [12] Energy-based feature extraction, wavelet transform [13] Wavelet features [14] Morphological operation [15] Texture analysis and HOS features [16] Wavelet and HOS features [17] Gabor transform and energy features Private 702 images Ensemble Learning 0.94 0.95 Probabilistic Neural Network (PNN) KNN 0.958 Private, 60 images Private, 63 images Private, 61 images Private 60 images Private 60 images Private 510 images RF, SVM KNN, SVM ANN SVM SVM SVM Vol:.(1234567890) Private, 60 images KNN Private 1000 images, Public MIAG dataset SVM Public DRIS (...truncated)