Challenges and Future Scopes in Current Applications of Deep Learning in Human Cancer Diagnostics
C. S. Vidhya, M. Loganathan, R. Meenatchi · 2023
In cancer diagnostics, deep learning has proven to be astonishingly accurate in evaluating images. Cancer—a complex and multifaceted disease—has thousands of genetic and epigenetic changes. Deep learning is used to detect genetic abnormalities in cancer diagnosis and therapy. The Artificial Intelligence (AI) machine learning acts as a human brain to process data, recognize images, objects and languages, and improve drug development. Artificial neural networks (ANNs) are used to process data for cancer detection. This chapter brings out how AI-based assistance could help oncologists to give an accurate therapy by combining biology and AI. Machine Learning is a subset of AI in which algorithms are built on the basis of neural networks. Deep learning uses an ANN to process data, including medical images, to mimic human neural architecture. In order to decode the molecular start of cancer, clinical oncology research is now more focused on understanding the intricate biological architecture of cancer cell proliferation. Generalize AI, super AI, and tight AI are the three types of AI. With the evolution of AI technology, attempts have been made to construct robots that can perceive biological changes by pulling real-time data and comparing it to data from a population pool for accurate clinical interpretation of cancer test results.