Can deep learning models be integrated with other diagnostic tools to improve the accuracy and reliability of cancer identification?
As far as I know, yes, deep learning models can be integrated with other diagnostic tools to enhance the accuracy and reliability of cancer identification. Combining the power of deep learning with traditional diagnostic methods and other advanced technologies can lead to more robust and precise cancer detection and diagnosis. For example, deep learning models can be integrated with radiological tools, such as CT scans, MRI, X-rays, and ultrasound, to assist radiologists in detecting and characterizing tumors. For example, a deep learning model can analyze medical images to identify abnormalities or assist in the segmentation and quantification of tumor regions.
I recommend the following articles for more information:
- Esteva, A., Robicquet, A., Ramsundar, B. et al. A guide to deep learning in healthcare. Nat Med 25, 24–29 (2019). https://doi.org/10.1038/s41591-018-0316-z
- Mittal, S., Hasija, Y. (2020). Applications of Deep Learning in Healthcare and Biomedicine. In: Dash, S., Acharya, B., Mittal, M., Abraham, A., Kelemen, A. (eds) Deep Learning Techniques for Biomedical and Health Informatics. Studies in Big Data, vol 68. Springer, Cham. https://doi.org/10.1007/978-3-030-33966-1_4
- Zhu, Wan, Longxiang Xie, Jianye Han, and Xiangqian Guo. 2020. "The Application of Deep Learning in Cancer Prognosis Prediction" Cancers 12, no. 3: 603. https://doi.org/10.3390/cancers12030603
Dear Tajinder Kumar Saini ,
Deep learning has succeeded greatly in medical image-based cancer diagnosis.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10377683/
Regards,
Shafagat
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