Application of Artificial Intelligence in Early Detection of Lung Cancer
Autor Madhuchanda Kar, Jhilam Mukherjee, Amlan Chakrabarti, Sayan Dasen Limba Engleză Paperback – 10 mai 2024
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
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Specificații
ISBN-13: 9780323952453
ISBN-10: 0323952453
Pagini: 254
Dimensiuni: 191 x 235 x 16 mm
Greutate: 0.53 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0323952453
Pagini: 254
Dimensiuni: 191 x 235 x 16 mm
Greutate: 0.53 kg
Editura: ELSEVIER SCIENCE
Public țintă
Researchers and graduate students on cancer research; oncologistsClinical researchers; radiologists
Cuprins
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
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