Utilization Of Artificial Intelligence In Image-Based Medical Diagnosis
DOI:
https://doi.org/10.37676/jki.v2i1.567Keywords:
Artificial Intelligence, Medical Diagnosis, Deep Learning, CNN, Medical ImagesAbstract
Artificial Intelligence (AI) has become one of the most revolutionary technologies in the medical field, especially in image-based diagnostics. This research aims to explore the utilization of AI, particularly through deep learning and Convolutional Neural Networks (CNN), in improving the accuracy and efficiency of medical diagnosis systems. This research uses a literature study approach and prototype implementation to analyze how AI can support doctors in making faster and more informed clinical decisions. The results show that AI is able to improve the accuracy of diagnoses as well as reduce human errors in medical image analysis.
References
Chung, A. W., & Hsu, C. S. (2021). Applications of convolutional neural networks in medical imaging: A review. Frontiers in Medicine, 8, 760292.
Dhungel, N., & Carneiro, G. (2017). A deep learning approach for detecting pneumonia from chest X-rays. Medical Image Analysis, 42, 109-116.
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.
Ghafoorian, M., Karssemeijer, N., & Sánchez, C. I. (2017). Transfer learning for domain adaptation in MRI: Application in brain lesion segmentation. IEEE Transactions on Medical Imaging, 36(5), 1104-1115.
Kermany, D. S., Goldbaum, M., & Cai, W. (2018). Identifying medical diagnoses and treatable diseases by image-based deep learning. JAMA, 320(3), 256-268.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (pp. 1097-1105).
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Li, X., Zhang, Q., & Wang, L. (2019). Deep learning for medical image analysis. Journal of Medical Imaging and Health Informatics, 9(2), 279-287.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & van der Laak, J. A. W. M. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88.
Moeskops, P., Viergever, M. A., & Mendrik, A. M. (2016). Automatic segmentation of MR brain images with a convolutional neural network. IEEE Transactions on Medical Imaging, 35(5), 1252-1261.
Ni, D., Wu, G., & Xu, X. (2018). A survey of deep learning methods in medical imaging. Computers in Biology and Medicine, 101, 93-106.
Pereira, S., Pinto, A., & Alves, V. (2016). Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Transactions on Medical Imaging, 35(5), 1240-1251.
Rajpurkar, P., Irvin, J., & Zhang, J. (2017). CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv preprint arXiv:1711.05225.
Razzak, M. I., Naz, R., & Zaheer, S. (2018). Deep learning for medical image processing: Overview, challenges and the future. Classification in BioApps, 157-176.
Shen, D., Wu, G., & Suk, H. I. (2017). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19, 221-248.
Wang, J., Wang, L., & Zhang, L. (2020). A comprehensive review on deep learning-based medical image analysis. Computers in Biology and Medicine, 122, 103823.
Yang, G., Yang, L., & Huang, Y. (2020). Review of deep learning in medical imaging. Health Information Science and Systems, 8(1), 14.
Yang, W., & Zhao, J. (2020). Advances in deep learning methods for medical imaging. Journal of Biomedical Informatics, 104, 103400.
Zhang, L., Zheng, Y., & Wang, S. (2018). Deep learning for medical image analysis. Computational Intelligence and Neuroscience, 2018, 1-9.
Zhou, S. K., Greenspan, H., Davatzikos, C., Duncan, J. S., van Ginneken, B., Madabhushi, A., ... & Rueckert, D. (2019). A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises. Proceedings of the IEEE, 108(1), 98-110.
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