Название: Artificial Intelligence for Disease Diagnosis and Prognosis in Smart HealthcareАвтор: Ghita Kouadri Mostefaoui, S.M. Riazul Islam, Faisal Tariq
Издательство: CRC Press
Год: 2023
Страниц: 328
Язык: английский
Формат: pdf (true)
Размер: 25.5 MB
Artificial Intelligence (AI) in general and Machine Learning (ML) and Deep Learning (DL) in particular and related digital technologies are a couple of fledging paradigms that next-generation healthcare services are sprinting towards. These digital technologies can transform various aspects of healthcare, leveraging advances in computing and communication power. With a new spectrum of business opportunities, AI-powered healthcare services will improve the lives of patients, their families, and societies. However, the application of AI in the healthcare field requires special attention given the direct implication with human life and well-being. Rapid progress in AI leads to the possibility of exploiting healthcare data for designing practical tools for automated diagnosis of chronic diseases such as dementia and diabetes. This book highlights the current research trends in applying AI models in various disease diagnoses and prognoses to provide enhanced healthcare solutions. The primary audience of the book are postgraduate students and researchers in the broad domain of healthcare technologies.
In the last decade, automated computerized skin disease systems based on Machine Learning and Deep Learning have gained extreme attention among researchers due to their outstanding performance. This chapter is allocated to the overall process and trends toward skin disease recognition and classification based on Machine Learning and Deep Learning. The main purpose of this chapter is to provide knowledge useful for both beginners and more advanced-level researchers in this field. To this end, after presenting a brief introduction to skin diseases, main steps of automated skin disease recognition systems such as image acquisition and available datasets, pre-processing, segmentation, augmentation, feature extraction, and classification are all explained in detail. The general concept of deep learning-based architectures is presented. Additionally, some samples of Python codes in each step are provided so that you can comfortably and easily apply the Keras, Tensorflow, Scikit-learn, and OpenCV libraries with Python programming language to design your own automated system by following the steps and then train and evaluate them.
Features:
In-depth coverage of the role of AI in smart healthcare.
Research guideline for AI and Data Science researchers/practitioners interested in the healthcare sector.
Comprehensive coverage on security and privacy issues for AI in smart healthcare.
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