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Machine and Deep Learning Algorithms, Applications Cancer Detection
Paperback

Machine and Deep Learning Algorithms, Applications Cancer Detection

$283.99
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A book that covers different machine learning and deep learning algorithms specifically for lung cancer would be an excellent resource for researchers, medical professionals, and data scientists interested in leveraging artificial intelligence for medical diagnosis and treatment. While I can't provide a specific book title, I can suggest the key topics and areas such a book might cover: Introduction to Lung Cancer: The book may start with an overview of lung cancer, including its types, causes, diagnosis methods, and current treatment options. Machine Learning Fundamentals: It may cover fundamental concepts of machine learning, including supervised, unsupervised, and semi-supervised learning, as well as evaluation metrics commonly used in medical applications.Data Preprocessing and Feature Engineering: The book may discuss techniques for preprocessing medical imaging data, such as CT scans or X-rays, including image normalization, noise reduction, and feature extraction.Classification Algorithms: It would likely delve into various classification algorithms used for lung cancer detection and diagnosis, such as support vector machines (SVM), random forests, decision trees.

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MORE INFO
Format
Paperback
Publisher
LAP Lambert Academic Publishing
Date
30 April 2024
Pages
184
ISBN
9786207487905

A book that covers different machine learning and deep learning algorithms specifically for lung cancer would be an excellent resource for researchers, medical professionals, and data scientists interested in leveraging artificial intelligence for medical diagnosis and treatment. While I can't provide a specific book title, I can suggest the key topics and areas such a book might cover: Introduction to Lung Cancer: The book may start with an overview of lung cancer, including its types, causes, diagnosis methods, and current treatment options. Machine Learning Fundamentals: It may cover fundamental concepts of machine learning, including supervised, unsupervised, and semi-supervised learning, as well as evaluation metrics commonly used in medical applications.Data Preprocessing and Feature Engineering: The book may discuss techniques for preprocessing medical imaging data, such as CT scans or X-rays, including image normalization, noise reduction, and feature extraction.Classification Algorithms: It would likely delve into various classification algorithms used for lung cancer detection and diagnosis, such as support vector machines (SVM), random forests, decision trees.

Read More
Format
Paperback
Publisher
LAP Lambert Academic Publishing
Date
30 April 2024
Pages
184
ISBN
9786207487905