Deep Learning Framework for Cancer Diagnosis and Treatment
Shiv Bahadur, Prashant Kumar · 2022
Background: The awareness on deep learning is rapidly increasing day by day and various researchers have explored its application in cancer diagnostics. However, there is a need of exhaustive study in this aspect for real-world medical utility. The cancer is fatal disease and a major cause of bereavement affecting millions of survives every year. Although its early diagnosis may be useful to save the lives of population. This can be facilitated by the application of deep learning (DL) through artificial intelligence (AI) and machine learning (ML) for efficient drug discovery and disease diagnosis. Aim: The present chapter focuses on the various aspects of deep learning and artificial intelligence in the diagnosis and treatment of cancer. Discussion: The key challenges in cancer management are foretelling clinical response in case of anticancer drugs in human population. Machine learning refers to complex computer algorithms, which learn from data by learning the way to map input data to generate the desired output predictions. The majority of recent significant deep learning experiments in cancer diagnosis is engaged through input of pictures in the system for the analysis. Conclusion: Several models of deep learning may be used to track cancer development for the patients undergoing treatment. In comparison to manual segmentation, various investigators found that the DL has significantly enhanced the analysis with recused chances of mistakes. Several studies showed that deep learning can be used as a potential approach for the improvement of cancer detection accuracy for a variety of cancers, including breast, colon, cervical, and lung cancers.