Assabie, Yaregal (PhD)Haftu, Kibrom2021-07-292023-11-292021-07-292023-11-292021-02-25http://etd.aau.edu.et/handle/123456789/27479Cardiorespiratory diseases are recognized as serious, worldwide public health concerns that have remained among the leading cause of death globally. These diseases range from common colds to life-threatening bacterial pneumonia that affects anyone, anywhere. Timely, accurate, and effective detection is critical, especially for infectious diseases to prevent the condition from becoming too severe. Deep learning a subfield of machine learning played a central role to detect cardiorespiratory diseases from X-ray images using transfer learning techniques. These approaches allow radiologists (or other health professionals) to extract relevant features that are undetectable to the naked eye or difficult to spot. However, there are still longstanding issues related to the application of deep learning for transfer learning. Difficulties arise when transferring knowledge from one domain into another task that could hurt model performance. We, therefore, proposed a non-parametric Bayesian statistical model to investigate the effectiveness of transfer learning on X-ray images. The proposed model comprises two main components: The deep transfer learning and the detection component. The first component leverages unlabeled X-ray images using unsupervised variational autoencoder to EfficientNet architecture. Its purpose is to extract robust features for detection. The detection component further fine-tunes these features using an end-to-end supervised algorithm rather than current approaches that use a single feature space represented by the last fully connected layer of the convolutional neural network across all conditions. At this stage, we conducted a hyperparameter search for different parameters. Moreover, we developed an offline data augmentation algorithm to address the class imbalance problem that emerged from the dataset. In doing so, we expand the training data from 75,682 to 108,708 X-ray images The proposed system was implemented on Google Cloud Platform using an open-source deep learning library called PyTorch. The National Institutes of Health dataset was used to train, validate, and test the proposed model. The dataset is comprising 112, 120 images from nearly 31,000 patients. We also evaluated the performance of our approaches using Tikur Anbessa Specialized Hospital X-ray images. We have achieved an 88.01% AUC score that outperforms the existing state-of-the-art approaches.enDeep LearningCardiorespiratory Disease DetectionDeep Transfer LearningCnnVariational AutoencoderX-Ray ImagesUnsupervised AlgorithmLatent FactorAiThesis