Computational Intelligence in Cancer Diagnosis

Progress and Challenges
Herausgeber: Naik, Bighnaraj; Mishra, Manohar; Muhammad, Khan; Nayak, Janmenjoy University; Al-Dabass, David; Pelusi, Danilo

• Broschiertes Buch

Produktbeschreibung

Computational Intelligence in Cancer Diagnosis: Progress and Challenges provides insights into the current strength and weaknesses of different applications and research findings on computational intelligence in cancer research. The book improves the exchange of ideas and coherence among various computational intelligence methods and enhances the relevance and exploitation of application areas for both experienced and novice end-users. Topics discussed include neural networks, fuzzy logic, connectionist systems, genetic algorithms, evolutionary computation, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. The book's chapters are written by international experts from both cancer research, oncology and computational sides to cover different aspects and make it comprehensible for readers with no background on informatics.

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Produktdetails

• Verlag: Elsevier Science & Technology
• Seitenzahl: 420
• Erscheinungstermin: 13. April 2023
• Englisch
• Abmessung: 191mm x 235mm x 24mm
• Gewicht: 834g
• ISBN-13: 9780323852401
• ISBN-10: 0323852408
• Artikelnr.: 62919832

Inhaltsangabe

SECTION 1. Introduction to Computational Intelligence Approaches1. The
roadmap to the adoption of computational intelligence in cancer diagnosis:
The clinical-radiological perspective2. Deep learning approaches for high
dimension cancer microarray data feature prediction: A review3. Integrative
data analysis and automated deep learning technique for ovary cancer
detection4. Learning from multiple modalities of imaging data for cancer
diagnosis5. Neural network for lung cancer diagnosis6. Machine learning for
thyroid cancer diagnosis
SECTION 2. Prediction of Cancer Susceptibility7. Machine-learning-based
detection and classification of lung cancer8. Deep learning techniques for
oral cancer diagnosis9. An intelligent deep learning approach for colon
cancer diagnosis10. Effect of COVID-19 on cancer patients: Issues and
future challenges11. Empirical wavelet transform based fast deep
convolutional neural network for detection and classification of melanoma
SECTION 3. Advance Computational Intelligence Paradigms12. Convolutional
neural networks and stacked generalization ensemble method in breast cancer
prognosis13. Light-gradient boosting machine for identification of
osteosarcoma cell type from histological features14. Deep learning based
computer aided cervical cancer diagnosis in digital histopathology
images15. Deep learning techniques for hepatocellular carcinoma
diagnosis16. Issues and future challenges in cancer prognosis: (Prostate
cancer: A case study)17. A novel cancer drug target module mining approach
using non-swarm intelligence