Abstract
Recent advancements in Computer Vision have opened up new opportunities for addressing complex healthcare challenges, particularly in the area of lung disease diagnosis. Chest X-rays, a commonly used radiological technique, hold great potential in this regard. To leverage this potential, researchers have proposed the use of deep learning methods for building computer-aided diagnostic systems. However, the design and compression of these systems remains a challenge, as it depends heavily on the expertise of the data scientists. To address this, we propose an automated method that utilizes an evolutionary algorithm (EA) to optimize the design and compression of a convolutional neural network (CNN) for X-Ray image classification. This method is capable of accurately classifying radiography images and detecting possible chest abnormalities and infections, including COVID-19. Additionally, the method incorporates transfer learning, where a pre-trained CNN model on a large dataset of chest X-ray images is fine-tuned for the specific task of detecting COVID-19. This approach can help to reduce the amount of labeled data required for the specific task and improve the overall performance of the model. Our method has been validated through a series of experiments against relevant state-of-the-art architectures.
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Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia. Project No. (IF2/PSAU/2022/01/21577).
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Louati, H., Louati, A., Kariri, E., Bechikh, S. (2023). Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm and Transfer Learning. In: Nguyen, N.T., et al. Advances in Computational Collective Intelligence. ICCCI 2023. Communications in Computer and Information Science, vol 1864. Springer, Cham. https://doi.org/10.1007/978-3-031-41774-0_7
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