Chapter 1: An Introduction to Artificial Intelligence for Medical Sciences
Chapter goal: This is the initial chapter. Subsequently, it encapsulates the specific context and structure of the book. Then, it states the varying medical specialties central to this book. Likewise, it properly presents independent subsets of artificial intelligence. Besides that, it unveils valuable tools for undertaking exercises; Python programming language, distribution package, and libraries. Afterward, it sufficiently acquaints you with different algorithms, including when to carry them out.
Sub-topics:
● Context of the book.
● The book's central point.
● Artificial Intelligence subsets covered in this book.
● Structure of the book.
● Tools that this book implements.
○ Python distribution package.
○ Anaconda distribution package.
○ Jupyter Notebook.
○ Python libraries.
● Encapsulating Artificial Intelligence.
● Debunking algorithms.
● Debunking supervised algorithms.
● Debunking unsupervised algorithms.
● Debunking Artificial Neural Networks.
Chapter 2: Realizing Patterns in Common Diseases with Neural Networks
Chapter goal: This chapter purportedly contains the application of artificial neural networks in modelling medical data. It properly instigates deep belief networks to model data and predicts whether a patient suffers from an ordinary disease (i.e., pneumonia and diabetes). Equally, it appraises the networks with fundamental metrics to discern the magnitude to which the networks set apart patients who suffer from the disease from those who do not.
Sub-topics:
● Classifying patients' Cardiovascular disease diagnosis outcome data by executing a deep
belief network.
● Preprocessing the Cardiovascular disease diagnosis outcome data.
● Debunking deep belief networks.
o Designing the deep belief network.
o Relu Activation function.
o Sigmoid activation function.
● Training the deep belief network.
● Outlining the deep belief networks predictions.
● Considering the deep belief network's performance.
● Classifying patients' diabetes diagnosis outcome data by executing a deep belief network.
● Outlining the deep belief networks predictions .
● Considering the deep belief network's performance.
● Conclusion.
Chapter 3: A Case for COVID-19 Identifying Hidden States and Simulation Results
Chapter goal: This chapter instigates a set of series analysis methods to uniquely discern patterns in the US COVID-19 confirmed cases. To begin with, the Gaussian Hidden Markov Model inherits the series data, models it and identifies the hidden states, including the means and covariance in those states. Subsequently, the Monte Carlo simulation method replicates US COVID-19 confirmed cases across multiple trials, thus providing us with a rich comprehending of the pattern
Chapter content:
● Debunking the Hidden Markov Model
● Descriptive analysis
● Carrying Out the Gaussian Hidden Markov Model
o Considering the Hidden States in US COVID-19 Confirmed Cases with the Gaussian
Hidden Markov Model
● Simulating US COVID-19 Confirmed Cases with the Monte Carlo Simulation Method
o US COVID-19 confirmed cases simulation results
● Conclusion
Chapter 4: Cancer Segmentation with Neural Networks
Chapter goal: This chapter typically exhibits the practical application of computer vision and
convolutional neural ne
