EAR PATHOLOGIES USING DEEP LEARNING ON OTOSCOPIC IMAGES

In this study, the performance of different deep learning architectures is comparatively analyzed for the classification of ear pathologies based on otoscopic images. The dataset included four basic classes: chronic otitis media, ear wax obstruction, myringosclerosis and normal ear structure. The images were normalized at 224×224-pixel resolution and made suitable for the model, and classification was performed using CNN, CNN-LSTM, DenseNet121, ResNet50 and EfficientNet architectures. During the training and validation phases, performance metrics such as accuracy, F1 score, precision, recall and loss values were calculated, and the class discrimination power of the models was evaluated with ROC curves and complexity matrices. According to the results, CNN+LSTM and DenseNet121 architectures showed the best performance with over 94% accuracy and high F1 score in both training and validation sets. Some transfer learning-based architectures such as EfficientNet and ResNet50 showed low generalization performance. This study demonstrates the effectiveness of deep learning-based models for computerized diagnosis of intra-ear diseases and provides an important basis for decision support systems to be developed in this field.