Author Description

<p><i>Computational Intelligence in Cancer Diagnosis: Progress and Challenges</i> 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. </p> <p>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.</p> <br><br><ul> <li>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</li> <li>Discusses several cancer types, including their detection, treatment and prevention</li> <li>Presents case studies that illustrate the applications of intelligent computing in data analysis to help readers to analyze and advance their research in cancer</li></ul>