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Full Description
Disruptive Trends in Computer Aided Diagnosis collates novel techniques and methodologies in the domain of content based image classification and deep learning/machine learning techniques to design efficient computer aided diagnosis architecture. It is aimed to highlight new challenges and probable solutions in the domain of computer aided diagnosis to leverage balancing of sustainable ecology.
The volume focuses on designing efficient algorithms for proposing CAD systems to mitigate the challenges of critical illnesses at an early stage. State-of-the-art novel methods are explored for envisaging automated diagnosis systems thereby overriding the limitations due to lack of training data, sample annotation, region of interest identification, proper segmentation and so on. The assorted techniques addresses the challenges encountered in existing systems thereby facilitating accurate patient healthcare and diagnosis.
Features:
An integrated interdisciplinary approach to address complex computer aided diagnosis problems and limitations.
Elucidates a rich summary of the state-of-the-art tools and techniques related to automated detection and diagnosis of life threatening diseases including pandemics.
Machine learning and deep learning methodologies on evolving accurate and precise early detection and medical diagnosis systems.
Information presented in an accessible way for students, researchers and medical practitioners.
The volume would come to the benefit of both post-graduate students and aspiring researchers in the field of medical informatics, computer science and electronics and communication engineering. In addition, the volume is also intended to serve as a guiding factor for the medical practitioners and radiologists in accurate diagnosis of diseases.
Contents
1. Evolution of Computer Aided Diagnosis: The Inception and Progress
2. Computer Aided Diagnosis for a Sustainable World
3. Applications of Computer Aided Diagnosis Techniques for a Sustainable World
4. Applications of Generative Adversarial Network on Computer Aided Diagnosis
5. A Critical Review of Machine Learning Techniques for Diagnosing the Corona Virus Disease (COVID- 19)
6. Cardiac Health Assessment Using ANN in Diabetic Population
7. Efficient, Accurate and Early Detection of Myocardial Infarction Using Machine Learning
8. Diagnostics and Decision Support for Cardiovascular System: A Tool Based on PPG Signature
9. ARIMA Prediction Model Based Forecasting for COVID- 19 Infected and Recovered Cases
10. Conclusion
