Publications by authors named "Sungyeup Kim"
This study evaluates the efficacy of several Convolutional Neural Network (CNN) models for the classification of hearing loss in patients using preprocessed auditory brainstem response (ABR) image data. Specifically, we employed six CNN architectures-VGG16, VGG19, DenseNet121, DenseNet-201, AlexNet, and InceptionV3-to differentiate between patients with hearing loss and those with normal hearing. A dataset comprising 7990 preprocessed ABR images was utilized to assess the performance and accuracy of these models.
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- The paper discusses a deep learning method that utilizes transfer learning to classify lung diseases from chest X-ray images, aiming to enhance the accuracy and efficiency of computer-aided diagnostic systems.
- The proposed method employs an end-to-end learning approach using the EfficientNet v2-M model to directly analyze raw chest X-ray images for identifying diseases.
- Experiments on two different data sets (NIH and Cheonan Soonchunhyang University Hospital) demonstrated promising results, with accuracy rates around 82% and high specificity, especially for tuberculosis detection, showcasing the method's effectiveness in lung disease diagnosis.