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Értesítés a kedvenc témákról
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Application of Artificial Intelligence in Early Detection of Lung Cancer

 
Kiadó: Academic Press
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EUR 144.00
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59 421 Ft (56 592 Ft + 5% áfa)
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A termék adatai:
ISBN13:9780323952453
ISBN10:0323952453
Kötéstípus:Puhakötés
Terjedelem:450 oldal
Méret:234x191 mm
Súly:450 g
Nyelv:angol
700
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Hosszú leírás:

Application of Artificial Intelligence in Early Detection of Lung Cancer presents the most up-to-date computer-aided diagnosis techniques used to effectively predict and diagnose lung cancer. The presence of pulmonary nodules on lung parenchyma is often considered an early sign of lung cancer, thus using machine and deep learning technologies to identify them is key to improve patients' outcome and decrease the lethal rate of such disease. The book discusses topics such as basics of lung cancer imaging, pattern recognition techniques, deep learning, and nodule detection and localization. In addition, the book discusses risk prediction based on radiological analysis and 3D modeling. This is a valuable resource for cancer researchers, oncologists, graduate students, radiologists, and members of biomedical field who are interested in the potential of AI technologies in the diagnosis of lung cancer.




  • Provides an overview of the latest developments of artificial intelligence technologies applied to the detection of pulmonary nodules
  • Discusses the different technologies available and guides readers step-by-step to the most applicable one for the specific lung cancer type
  • Describes the entire study design on prediction of lung cancer to help readers apply it to their research successfully
Tartalomjegyzék:
1. Introduction to Computer Aided Detection and Diagnosis
2. Basics of Lung Cancer Imaging
3. Terminologies of Lung cancer (biopsy, cytology, lung anatomy, radiological features related to lung cancer)
4. Overview of Pattern Recognition Technique
5. Deep learning Techniques
6. Nodule Detection (Segmentation of pulmonary abnormalities and differentiation of pulmonary nodule from pulmonary vessels and similar looking pulmonary abnormalities)
7. Radiological Feature Analysis based Risk Prediction (Analysis of shape, margin, presence of calcification, necrotic pattern, classification of nodule based on anatomical positions and density)
8. Nodule Localization (Among which lobes the pulmonary nodules are initiated)
9. 3D Modelling (3D segmentation of pulmonary nodules.)
10. Conclusion