| dc.description.abstract | In the past few years, deep learning (DL) has emerged to give big impacts in different areas of healthcare. The application of deep learning approaches in healthcare aims to design a system that can assist diagnosis of diseases and automate the analysis of medical images to help treatment planning. DL methods perform adequately in image recognition, nevertheless medical images show unprecedented challenges. Implementing computer-aided techniques in iris image processing and connecting iridology with Traditional Chinese Medicine (TCM) is a challenging area of research in digital image processing and artificial intelligence. This thesis focuses on how to deal with the challenging problems in iridology: (1) how to develop a DL-based Computer-Aided Diagnosis (CAD) methodology to automate the iridology applications; (2) how to deal with the problem of class imbalance in dataset, (3) how to enhance the image resolution to a scale which enables the deep learning technique in later stage. Consequently, it causes a daunting task to train a deep learning model to recognize specific patterns. For the first problem, the proposed work combined the computer vision techniques based on the iris recognition framework and image classification approaches using convolutional neural networks to make a new approach in the healthcare profession. The dataset is highly imbalanced due to scarcity of the abnormal classes such as images of eyes with glasses, oversized and undersized pupils, and misaligned iris locations … etc. Such abnormal cases will cause the failure of iris segmentation and mask estimation, which will lead to the failure of iris recognition as well as iridology classification. To address the class imbalance problem and generate more rare cases of iris, we propose a data augmentation method that uses the Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP) to generate minority iris images, which saves extensive labor costs for rare data collection. In digital imaging, image resolution is a primary factor for the progress of various image processing technologies. If the resolution is low, then it is hard to perform iridology and iris recognition. In order to enhance the image resolution for a better classification rate, we propose DDA-SRGAN, a single image super-resolution (SISR) algorithm, based on the mask-attention mechanism used in Generative Adversarial Network (GAN). | en_US |