Evaluating Different Image Segmentation Techniques for Improved Otoscope Image Diagnosis

Image processing has evolved significantly, allowing image processing techniques to be used for automated image analysis and object recognition. Automated image analysis has dramatically improved image diagnosis and interpretation by reliably recognising and extracting quantitative information from processed images. Segmentation, which involves digitally improving or changing images to simplify the recognising of essential data, is an important part of image processing. As a result, assessing the performance of segmentation algorithms has become essential in achieving the right outcomes. This study aims to analyse several image segmentation approaches for tympanic membranes (TM) in oto scope images in order to detect the Otitis Media (OM) region on the ear, which indicates the presence of middle ear irritation or infection. In this side-by-side comparison, the accuracy, recall, precision, dice coefficient, and F1-score of U- Net, Attention U-Net, and Residual Attention U-Net are analysed. The results show that Residual Attention U-Net outperforms U-Net and Attention U-Net, and that it is the most successful segmentation approach. This study emphasises the potential of computer-aided technology for medical data monitoring and evaluation.