Revolutionizing Diagnosis: Cloud-Enabled Deep Learning for Lung Tumor Detection and Staging
Keywords:
Deep learning, convolution neural network, lung cancer, medical pictures.Abstract
This paper proposes a paradigm-shifting technique using cloud computing and deep learning to improve lung tumor detection and staging. The method involves collecting and preparing lung image datasets, building and training deep learning architectures and incorporating cloud resources. The system focuses on accurately categorizing tumor stages, a crucial aspect of treatment planning. The efficiency of the cloud-enabled deep learning system outperforms conventional approaches and demonstrates data-driven insights in medical diagnostics. Collaboration between medical practitioners, data scientists, and software engineers is essential for successful implementation in clinical settings. We suggest an intelligent lung cancer detection system that includes data integration, retrieval, historical case comparison, and comparable case inquiry. With the use of this technology, medical professionals can combine the various data sources at their disposal and use a substantial quantity of combined data as a guide while providing patient care. This system incorporates a content-based picture retrieval system for similarity comparison after a cloud-based deep learning model. Ultimately, this system is trained and tested on a few public datasets, and the findings show that it performs better than several baseline methods. After that, the similar case discovery is assessed, and every similarity is greater than 0.95. This cloud system has a positive impact on providing physicians and patients with improved access to diagnosis and treatment for lung cancer.
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