Comparison of Fine-Tuning InceptionV3 and Xception for Eye Disease Classification Based on Fundus Images - Irsyad Rafi Naufaldi; Ani Dijah Rahajoe; Eva Yulia Puspaningrum (bit-Tech, 2025)
Eye diseases represent a major global health concern that can lead to visual impairment and even blindness if not detected early. The shortage of ophthalmologists and unequal distribution of medical services highlight the need for automatic eye disease detection system increasingly essential. Therefore, the role of Artificial Intelligence (AI), particularly Deep Learning, is highly needed. This study aims to compare the performance of two CNN architectures InceptionV3 and Xception. Unlike previous studies, this paper provides a comparative Fine-Tuning analysis of two CNN models on multiclass eye disease. The approach applied is transfer learning with a fine-tuning technique on several final layers to achieve higher accuracy by optimizing pretrained models using large-scale datasets such as ImageNet. The dataset consists of 4,184 fundus images covering multiple eye disease with balanced class distribution, ensuring diversity that supports model generalization. Divided into train, valid, and test sets with a ratio of 70:15:15. The training employed Adam optimizer, a batch size of 16, a learning rate of 0.0001, and implements early stopping to prevent overfitting. The performance of the model was assessed using evaluation metrics including accuracy, precision, recall, and F1-score. Experimental results indicate that the Xception model achieved superior performance with an accuracy of 87.78%, precision of 0.89, recall of 0.88, and an F1-score of 0.88, outperforming InceptionV3 with an accuracy of 85.56%, indicates the model is reliable for preliminary diagnosis. These findings suggest that the architecture in Xception is more efficient in extracting features from limited yet complex medical datasets.