Computational Intelligence in Cancer Diagnosis: Progress and Challenges

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.

Table of Contents:
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