StyleGAN-based synthetic image augmentation for multi-class otoscopy image classification

Accurate diagnosis of eardrum abnormalities is pivotal for effectively managing various ear conditions. Otoscopy, a non-invasive diagnostic procedure, provides detailed views of the ear canal and eardrum to identify pathologies such as otitis media, tympanic membrane perforations, and other abnormalities. Despite advancements, classifying otoscopy images remains challenging due to lighting variations, motion blur, and diverse abnormalities, exacerbated by limited labeled datasets and subjective assessments. A significant barrier to accurate diagnosis is the lack of an objective evaluation method for eardrums, such as machine learning techniques to classify images as normal or abnormal. Moreover, when processing otoscopy videos, traditional approaches require extensive manual expertise to select representative frames for analysis. This study investigates the efficacy of artificial image augmentation using StyleGAN3 to enhance otoscopy image classification accuracy. Augmented datasets comprising artificial and composite otoscopy images were used to train and validate a ResNet-101 model. A total of 816 artificial and 816 composite images (250-Effusion, 340-Normal, 113-Perforation, and 113-Tympanosclerosis) and 80,685 classic augmented images (24651-Effusion, 33660-Normal, 11187-Perforation, and 11187-Tympanosclerosis) were used. Results demonstrate that integrating StyleGAN3-generated synthetic images significantly improves classification performance. Our approach achieved a mean test accuracy of 0.95 ± 0.03 and an F1-score of 0.96 ± 0.02, surpassing those of traditional augmentation methods, while representing an optimistic upper-bound scenario. In contrast, without any augmentation, the accuracy and F1-score are 0.82 ± 0.03 and 0.78 ± 0.02, respectively, and with classic augmentations, they are 0.87 ± 0.02 and 0.85 ± 0.02, respectively. These findings underscore the potential of GAN-based augmentation to bolster eardrum image classification systems and address challenges in diagnostic accuracy.