دانلود کتاب کاربردهای هوش مصنوعی و رایانش ابری در مهندسی پزشکی

The chapters proposed for the book include genome sequence and visualization, the role of AI and cloud in the detection of several diseases, nature-inspired algorithms for disease detection, and frameworks employed for disease classification.
The book chapter majorly focuses on
• Designing AI techniques for several disease detection.
• Exploring the role of AI in biomedical engineering.
• Implementing machine learning (ML) algorithms and models to genome detection.
• Analysis of genome sequence.
Chapter 1 emphasizes how AI helps in drug development and comprehension. AI has great potential in many areas of healthcare, particularly in research and medication creation. Large datasets may be analyzed and transformed into useful insights thanks to the incorporation of AI. By identifying novel therapeutic targets and enhancing current treatment approaches, this skill expands the field of drug discovery. Prominent pharmaceutical firms have begun integrating AI technology into their research procedures in an effort to improve their capacity to create novel medications through the use of ML and computational biology.
Chapter 2 covers various approaches is imagery from classical to advanced AI/ML and how image analysis can enhance feature extraction, genomic sequence visualization, and gene expression analysis Through practical case studies, the book shows how these techniques apply to a variety of plant genomics issues, such as transcriptome visualization, metagenomics analysis, and crop improvement, providing insights to their potential and guiding readers to develop customized solutions for specific research needs
Chapter 3 explores computational workflows, automation of bioinformatics pipelines, and integration with cloud services, supported by real-world examples that demonstrate enhanced efficiency and productivity. ML and AI’s transformative impact on structural bioinformatics are examined, highlighting applications, cloud-based AI services, and future trends in predictive modeling and personalized medicine.
Chapter 4 highlights the diverse AI methodologies utilized, such as natural language processing (NLP) for symptom analysis, ML for predictive modeling, and deep learning (DL) for imaging. The results demonstrate that AI can substantially enhance the capabilities of current medical practices, paving the way for more proactive and personalized healthcare solutions. This research aims to provide a comprehensive overview of AI integration in disease management and its potential to transform the future of healthcare.
Chapter 5 covers the role of AI in medicine for the detection and prevention of various diseases. AI is revolutionizing disease detection and prevention through newer approaches like ML and DL. By analyzing diverse medical data providers such as ultrasound, MRI, mammography, genomics, and CT scans, AI enables highly accurate and rapid diagnoses, often surpassing traditional methods. AI identifies early signs of diseases like cancer, Alzheimer’s, and cardiovascular conditions, providing personalized treatments based on genetic profiles, lifestyle factors, and environmental influences. AI’s predictive analytics forecast disease outbreaks and individual patient risks, allowing proactive healthcare measures.
Chapter 6 aims to examine several approaches and strategies for early lung cancer detection, classification, and classification. Lung cancer often appears without symptoms in its early stages, making detection difficult. Consequently, the timely identification of cancer is crucial for improving patient outcomes. Early detection significantly enhances the likelihood of successful treatment and recovery for individuals affected by the disease.
Chapter 7 focuses on methodologies/frameworks for detecting Diabetic Retinopathy, particularly the early diagnosis and staging of DR (normal, mild, moderate, and severe), and reviews a few of the existing retinal fundus datasets. This chapter discusses the potential research gaps in DR detection/classification, highlighting areas needing further study and analysis.
Chapter 8 covers a comparative analysis of numerous state-of-the-art approaches for medical imaging diagnosis and evaluates various key qualities. The process involves assessing several critical elements, including semantic data, interpretability, visualization, and the measurement of logical linkages in medical data. Thus, we can conclude that the potential for imaging in the future will have a high degree of diagnostic accuracy for the diagnosis of various diseases, which is important for the disease’s diagnosis and has a higher chance of being realized in the field of clinical diagnosis. Lastly, the applications and potential were also covered.
Chapter 9 discusses the application of advanced technology in biomedical engineering, including the conceptualization, development, and deployment of biomedical solutions. This work focuses on ML algorithms in advancements of biomedical engineering and identification and exploration of the bio fabrication, biomechanics, and biomaterials applications. The opportunities, challenges, and future enhancements in the ML algorithms for biomedical engineering are discussed in this chapter.
Chapter 10 explores the role of AI in drug discovery and development and its application in emergency and critical care through decision support systems. Robotic and automated systems, including surgical robots and AI-assisted surgeries, as well as automation in laboratory diagnostics and rehabilitation, are also discussed.
Chapter 11 presents a design to aid the blind person using edge detection. The technique aims to give the patient information about the free space apart from the obstacles around him in all directions for better mobility. The technique comprises three modules: the histogram equalization module, the segmentation module, and the Kalman filtering module. In the histogram equalization module, canny edge detector is employed to detect edges and subsequently, histogram equalization is carried out. Furthermore, adaptive region growth is employed in the segmentation module to complete the segmentation process.
We hope that the works published in this book will be able to serve the concerned communities of ML and healthcare society.