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The book covers ways to use neural networks to classify patients with diseases. You will know how to apply computer vision techniques and convolutional neural networks (CNNs) to segment diseases such as cancer (e.g., skin, breast, and brain cancer) and pneumonia. The hidden Markov decision making process is presented to help you identify hidden states of time-dependent data. In addition, it shows how NLP techniques are used in medical records classification.
This book is suitable for experienced practitioners in varying medical specialties (neurology, virology, radiology, oncology, and more) who want to learn Python programming to help them work efficiently. It is also intended for data scientists, machine learning engineers, medical students, and researchers.
What You Will Learn
Apply artificial neural networks when modelling medical data
Know the standard method for Markov decision making and medical data simulation
Understand survival analysis methods for investigating data from a clinical trial
Understand medical record categorization
Measure personality differences using psychological models
The book covers ways to use neural networks to classify patients with diseases. You will know how to apply computer vision techniques and convolutional neural networks (CNNs) to segment diseases such as cancer (e.g., skin, breast, and brain cancer) and pneumonia. The hidden Markov decision making process is presented to help you identify hidden states of time-dependent data. In addition, it shows how NLP techniques are used in medical records classification.
This book is suitable for experienced practitioners in varying medical specialties (neurology, virology, radiology, oncology, and more) who want to learn Python programming to help them work efficiently. It is also intended for data scientists, machine learning engineers, medical students, and researchers.
What You Will Learn
Apply artificial neural networks when modelling medical data
Know the standard method for Markov decision making and medical data simulation
Understand survival analysis methods for investigating data from a clinical trial
Understand medical record categorization
Measure personality differences using psychological models
Covers descriptive analysis, visualizing medical data, and developing/evaluating algorithms
Explains integrating deep belief networks and CNNs, computer vision, and NLP to find patterns in medical data
Presents CNNs to model chest CT scan images and differentiate patients with/without COVID-19
Erscheinungsjahr: | 2022 |
---|---|
Genre: | Importe, Informatik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
Inhalt: |
xi
173 S. 2 s/w Illustr. 57 farbige Illustr. 173 p. 59 illus. 57 illus. in color. |
ISBN-13: | 9781484282168 |
ISBN-10: | 1484282167 |
Sprache: | Englisch |
Ausstattung / Beilage: | Paperback |
Einband: | Kartoniert / Broschiert |
Autor: | Nokeri, Tshepo Chris |
Auflage: | 1st ed. |
Hersteller: |
Apress
Apress L.P. |
Verantwortliche Person für die EU: | Books on Demand GmbH, In de Tarpen 42, D-22848 Norderstedt, info@bod.de |
Maße: | 235 x 155 x 11 mm |
Von/Mit: | Tshepo Chris Nokeri |
Erscheinungsdatum: | 20.05.2022 |
Gewicht: | 0,295 kg |
Covers descriptive analysis, visualizing medical data, and developing/evaluating algorithms
Explains integrating deep belief networks and CNNs, computer vision, and NLP to find patterns in medical data
Presents CNNs to model chest CT scan images and differentiate patients with/without COVID-19
Erscheinungsjahr: | 2022 |
---|---|
Genre: | Importe, Informatik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
Inhalt: |
xi
173 S. 2 s/w Illustr. 57 farbige Illustr. 173 p. 59 illus. 57 illus. in color. |
ISBN-13: | 9781484282168 |
ISBN-10: | 1484282167 |
Sprache: | Englisch |
Ausstattung / Beilage: | Paperback |
Einband: | Kartoniert / Broschiert |
Autor: | Nokeri, Tshepo Chris |
Auflage: | 1st ed. |
Hersteller: |
Apress
Apress L.P. |
Verantwortliche Person für die EU: | Books on Demand GmbH, In de Tarpen 42, D-22848 Norderstedt, info@bod.de |
Maße: | 235 x 155 x 11 mm |
Von/Mit: | Tshepo Chris Nokeri |
Erscheinungsdatum: | 20.05.2022 |
Gewicht: | 0,295 kg |