Evaluating the Efficacy of Deep Learning Models for Identifying Manipulated Medical Fundus Images

(1) Background: The misuse of transformation technology using medical images is a critical problem that can endanger patients’ lives, and detecting manipulation via a deep learning model is essential to address issues of manipulated medical images that may arise in the healthcare field. (2) Methods: The dataset was divided into a real fundus dataset and a manipulated dataset. The fundus image manipulation detection model uses a deep learning model based on a Convolution Neural Network (CNN) structure that applies a concatenate operation for fast computation speed and reduced loss of input image weights. (3) Results: According to the fundus image manipulation detection model’s results, for the real data, the four lesions had average sensitivity = 0.98, precision = 1.00, and F1-score = 0.99. For the manipulated data, the average sensitivity = 1.00, precision = 0.84, and F1-score = 0.92. The average Area Under the Curve (AUC) for the four lesions was 0.988, which is relatively high. Subsequently, to confirm whether the fundus image manipulation detection model can operate effectively in actual clinical environments, the model’s outcomes were compared with those of five ophthalmologists. For real data, the four lesions showed an average sensitivity = 0.93, precision = 0.96, and F1-score = 0.95, whereas for manipulated data, the average sensitivity = 0.71, precision = 0.61, and F1-score = 0.65. The four lesions also had an average AUC = 0.822, which was lower than the results of the fundus image manipulation detection model across all metrics. (4) Conclusions: This study presents the possibility of addressing and preventing problems caused by manipulated medical images in the healthcare field. The proposed approach for detecting manipulated fundus images through a deep learning model demonstrates higher performance than that of ophthalmologists, making it an effective method.