## 1 Introduction  
The prevalence of visual impairment is higher among older people, and the significant causes include glaucoma, age-related macular degeneration, and diabetic retinopathy [1, 2]. The global average life expectancy has increased, and the risk of visual impairment is expected to increase accordingly [3]. Therefore, eye checkups are essential to maintain good vision and quality of life. In Japan, eye checkups are performed at a frequency of 16.2% by the local governments [4], and all eye checkups mostly use fundus photography. The use of optical coherence tomography (OCT) [5] has become widespread globally, as it is more accurate in detecting retinal disease than fundus photography [6]. Therefore, OCT is expected to be introduced into standard eye checkups. As the population requiring eye checkups increases, concern has grown regarding the corresponding burden on clinicians. Each day, a single clinician must check the findings of hundreds of in-person visits [4], thereby requiring an automatic classification algorithm to reduce clinicians' burden. Furthermore, automatic screening is helpful when non-ophthalmologists are involved in health checkups, which is often the case due to a shortage of ophthalmologists in some areas [7,8]. Machine learning (ML) has recently become one of the main techniques in automated image recognition [9]. Recent studies have shown that the ML model can automatically identify retinal disease using OCT images [10,11] and has a slightly higher diagnostic performance than the human eye [11,12]. The classification accuracy of the ML model is about 70-95% [10,11,13,14,15]. These studies suggest that ML is a useful tool for identifying ocular diseases and for reducing the burden on ophthalmologists. The development speed of ML is quite fast, and new models are proposed yearly to improve discriminability [9,16,17,18,19]. Generally, newer models are better for image classification tasks. However, those newer models are not necessarily better when performing transfer learning using OCT images [20]. The convolutional neural network (CNN) is a machine learning technique that provides superior image classification. The CNN model is evaluated in both the number of parameters in the network and the accuracy of the model. We selected three convolutional neural network models--ResNet-152 [16], DenseNet-201 [18], and EfficientNet-B7 [19]--to classify \"Abnormal\" or \"Normal\" OCT images in this study. ResNet-152 has a parameter of 60 million, and its Imagenet Top 1 accuracy is 77.8%. DenseNet-201 reduces the number of parameters to 1/3 of ResNet-152 (20 million) and maintains an Imagenet Top 1 accuracy close to that of ResNet-152 (77.42%). EfficientNet-B7 is close to ResNet-152 in the number of parameters (66 million), and has a significantly higher Imagenet Top 1 accuracy (84.3%) than ResNet-152. A note of caution for developing ML models of medical images is that dataset shifting can occur: the accuracy of ML models is lower in the actual clinic than in the laboratory [21,22]. This is because high-quality images are used in the development of the ML model and can differ because of differences in the data distribution (e.g., contrast, luminance, and image quality) obtained in clinical settings. Eye checkups often prioritize speed and minimize image processing (e.g., the number of images is decreased). Thus, the data distribution differs between the training datasets in eye checkups and those in clinics, which decreases prediction performance [23]. In order to manage dataset shifting, the software has been developed to evaluate the image quality after image acquisition and retake the image if the image quality is poor [24]. However, if retakes are required, the examination takes more than twice as long, making it unsuitable for health checkups where the examination speed is critical.