Classification of COVID–19 and Pneumonia X–ray Images Using a Transfer Learning Approach

R, Sai Kishore H; Bhargavi, M S; C, Pavan Kumar

doi:10.1109/tensymp52854.2021.9550878

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…Multitask contrastive learning can also be employed for automatic X-ray diagnosis. Innovative frameworks have been introduced to distinguish COVID-19 from other pneumonia types through X-ray analysis [8]. Strategies to enhance deep learning model detection accuracy and address complex challenges in training and testing include modifying activation functions in deep CNNs, employing transfer learning [9,10], utilising image inpainting [11,12], and applying models to tasks such as cancer diagnosis, detection [13], and classification, material discrimination [14], medical question-answering [15,16], and software engineering applications like optimizing project schedules, customer segmentation [17,18], and IoT intrusion detection [19,20].…”

Section: Introductionmentioning

confidence: 99%

Enhancing X-ray Security Image Synthesis: Advanced Generative Models and Innovative Data Augmentation Techniques

Yagoub,

Kasem,

Kang

2024

Applied Sciences

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This study addresses the field of X-ray security screening and focuses on synthesising realistic X-ray images using advanced generative models. Insufficient training data in this area pose a major challenge, which we address through innovative data augmentation techniques. We utilise the power of generative adversarial networks (GANs) and conditional GANs (cGANs), in particular the Pix2Pix and Pix2PixHD models, to investigate the generation of X-ray images from various inputs such as masks and edges. Our experiments conducted on a Korean dataset containing dangerous objects relevant to security screening show the effectiveness of these models in improving the quality and realism of image synthesis. Quantitative evaluations based on metrics such as PSNR, SSIM, LPIPS, FID, and FSIM, with scores of 19.93, 0.71, 0.12, 29.36, and 0.54, respectively, show the superiority of our strategy, especially when integrated with hybrid inputs containing both edges and masks. Overall, our results highlight the potential of advanced generative models to overcome the challenges of data scarcity in X-ray security screening and pave the way for more efficient and accurate inspection systems.

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