A Study on Varieties of Computer Intelligent Models for Early Detection of Prostate Cancer
Manmath Nath Das, Niranjan Panda, Rasmita Routray · 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON) · 2021
Cancer is a disease that attacks several organs in the human body and rapidly spreads to other parts of the body. For the last few decades, cancer has become the most vulnerable illness on the planet, with the highest mortality rate due to late diagnosis. Since the 1960s, researchers have been studying computational methods for learning from cancer data, with number of advanced techniques directly applied on medical images. Depending on the organ, cancer can manifest in a number of ways. Different treatment processes can be used to remove the contaminated organ, prevent its spread, and save the person's life when these symptoms are considered. As a result, early detection of cancer has emerged as one of the most pressing issues in medical science and technology. This paper presents a review of recent work on the methods of computational intelligence for prostate cancer prediction modelling, as well as the issues that must be addressed. The paper focuses on a broad concept of computational intelligence that includes Artificial Neural Networks and Deep Learning, as well as a survey of various deep learning application areas for prostate cancer diagnosis and detection. Initially we will start with an overview of architectural principles and the use of various common deep learning technologies such as Convolutional Neural Networks (CNN), Deep Belief Networks (DBN), and auto-encoders (AE). With that, we'll move on to the study of these various algorithms for early cancer detection using technology.