## 1 Introduction
Despite the DL model having higher computational time and complexity than the machine learning approach, DL provides robust results, particularly in the big dataset. Several studies also reported that DL can be used to build a high-performance prediction model in many areas such as agriculture [13], manufacturing [14], and even behavioral science [15]. The various DL-based approaches are also widely used in the image analysis field. In the studies related to eye image analysis for glaucoma detection, [PERSON] et al. [16] applied eighteen layers of a convolutional neural network (CNN) on the 1426 images (589: normal and 837: glaucoma). Their method successfully attained an accuracy of 98.13%, a sensitivity of 98%, and a specificity of 98.3%. [PERSON] et al. [17] applied a convolutional neural network to the classification model starting with the segmentation of the optic disc. Their proposed method was performed on the RIM-ONE, DRIONS-DB, and DRISHTI-GS databases and achieved 100% on all metrics by using the red channel of RGB. Another convolutional neural network for glaucoma classification was proposed by [PERSON] et al. [18]. Their study performed a fully automated system based on the CNN model without using other methods. Another researcher [19] performed a transfer learning method called IMagedNet to distinguish normal and glaucoma images. Their proposed method was performed on 277 normal data samples and 170 glaucomas and achieved an accuracy of 92.3% in the training process and 80% in the testing process. [PERSON] et al. [20] applied a convolutional neural network on the ORIGA-650, RIM-ONE R3, and DRISHTI-GS datasets and achieved an area under the curve (AUC) of 0.94. According to some previous studies of both machine learning and deep learning approaches, we concluded that OD and OC were the major objects used to classify the healthy and glaucomatous eyes based on images, which means that most of the previous research works focused on the CDR and DDLS characteristics. However, traditionally, it was difficult to ensure that the optic cup was enlarged as an indication of glaucoma, because every person may have a different characteristic of the optic disc. Hence, other solutions may have better potential to increase the accuracy of glaucoma assessment. The performance of the DL-based model can also be enhanced by applying data treatment/pre-processing steps, as reported in earlier studies, in many applications, such as for human activity recognition [21], breast cancer classification [22], and dental biometrics [23]. Therefore, in this study, we suggested that applying data preprocessing will help in improving the performance of glaucoma detection/classification. Besides analyzing the optic disc and optic cup, glaucoma can also be recognized by analyzing the damage to the retinal nerve fiber layer. The progression of the damage was easier to recognize by measuring the thickness of the retinal nerve fiber layer. According to a medical report [24], analyzing the thickness of the retinal nerve fiber layer was a more objective and reliable method for assessing the possibility of glaucoma. They also said that analyzing the progress of the retinal nerve fiber layer was extremely impactful in the disease monitoring process. Regarding those facts, we proposed an effective solution for detecting glaucoma by analyzing the characteristic of the retinal nerve fiber layer. To address the research gaps, we presented the contributions including:  
1. We proposed an alternative way to detect glaucoma disease by analyzing the damage to the retinal nerve fiber layer (RNFL). Our proposed method consists of novel step-by-step preprocesses to extract RNFL features from digital fundus images. 2. We further analyze our methods by applying them in ORIGA dataset with nine pre-trained CNN and compared them with similar earlier studies about glaucoma classification which used the same dataset. 3. Finally, we developed a prototype application to categorize fundus images in order to demonstrate the viability of our proposed model.