Abstract
The tremendous research into the medical health system provides a lot of opportunities for computing systems to come up with new ideas. These advancements are critical to the well-organized deployments of remedial systems that aid in the automated identification of health-related issues. The majority of essential health research is available on cancer prediction, which comes in a variety of forms and can impact different regions of the body. Pancreatic cancer is one of the most commonly afflicted cancers that are expected to be incurable. Once diagnosed, it cannot be treated properly. Machine learning and neural networks are providing promising findings for accurate pancreatic image segmentation in real-time early detection these days. The deep learning-based Hadoop distributed convolutional neural network (HdiHCNN) and Hadoop distributed recurrent neural network (HdiRNN) are used in this study to identify pancreatic tumors. The tentative results show that the suggested technique can improve the classifier’s performance for early identification of pancreatic cancer.
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Reddy, P.S., Chandrasekar, M. (2022). Distributed File System on Medical Data Using Machine Learning Techniques for Healthcare Surveillance. In: Pandian, A.P., Palanisamy, R., Narayanan, M., Senjyu, T. (eds) Proceedings of Third International Conference on Intelligent Computing, Information and Control Systems. Advances in Intelligent Systems and Computing, vol 1415. Springer, Singapore. https://doi.org/10.1007/978-981-16-7330-6_64
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