Computational Intelligence in Cancer Diagnosis

Computational Intelligence in Cancer Diagnosis: Progress and Challenges provides an insight into current strength and weaknesses of different applications as well as research findings on computational intelligence in cancer research. It 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. The book discusses topics such as neural networks, fuzzy logic, connectionist systems, genetic algorithms, evolutionary computation, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. The chapters are written by international experts from both cancer research/oncology and computational sides to cover different aspects to make it comprehensible even by readers with no background on informatics. It is a valuable source for cancer researchers, oncologists and members of biomedical fields who are interested in learning about new technologies to improve the accuracy of cancer diagnosis. Contains updated information about advanced computational intelligence spanning the areas of neural networks, fuzzy logic, connectionist systems, genetic algorithms, evolutionary computation, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems in diagnosing cancer diseases Discusses several cancer types with their detection, treatment and prevention Presents case studies illustrating the applications of intelligent computing in data analysis to help readers to analyze and advance their research in cancer