Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management

The potential value of artificial neural networks (ANN) as a predictor of malignancy has begun to receive increased recognition. Research and case studies can be found scattered throughout a multitude of journals. Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management brings together the work of top researchers - primaril

Raouf Naguib, Ph.D., is Professor of Biomedical Computing in the School of Mathematical and Information Sciences, Coventry University, England, where he also leads the Biomedical Computing Research Group. Prior to this appointment, he was a Lecturer at the University of Newcastle upon Tyne, England. Professor Naguib received the degrees of Ph.D., M.Sc. (with distinction), and D.I.C. from Imperial College of Science, Technology and Medicine, University of London, England, and the B.Sc. degree from Cairo University, Egypt. In 1995-1996 he was awarded the Fulbright Cancer Fellowship to pursue his research at the University of Hawaii in Mãnoa on the applications of artificial neural networks in breast cancer diagnosis and prognosis. Professor Naguib is a Chartered Engineer and a member of the Institution of Electrical Engineers (IEE), the Institute of Physics and Engineering in Medicine (IPEM), the American Association for Cancer Research (AACR), and a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE). He is the representative of IEEE Engineering in Medicine and Biology Society (EMBS) to the European Society for Engineering and Medicine, and the IEEE-USA Committee on Communications and Information Policy. He is also a Special Area Editor for the IEEE Transactions on Information Technology in Biomedicine. Professor Naguib has worked extensively on the applications of artificial neural networks in the field of clinical oncology. This work was also combined with studies on image processing, image cytometry and the stratification of significant conventional and experimental prognostic markers in a variety of cancers. His current interests lie in the applications of evolutionary computational models, fuzzy logic, genetic algorithms and parallel image processing methodologies to cancer diagnosis, prognosis and disease management. He also has a special interest in content-based image retrieval and human form perception for histopathological identifi

Introduction to Artificial Networks and Their Use in Cancer Diagnosis, Prognosis, and Patient Management. Analysis of Molecular Prognostic Factors in Breast Cancer by Artificial Neural Networks. Artificial Neural Approach to Analysing the Prognostic Significance of DNA Ploidy and Cell Cycle Distribution of Breast Cancer Aspirate Cells. Neural Networks for the Estimation of Prognosis in Lung Cancer. The Use of a Genetic Algorithm Neural Network (GANN) for Prognosis in Surgically Treated Non-Small Cell Lung Cancer (NSCLC). The Use of Machine Learning in Screening for Oral Cancer. Outcome Prediction of Oesophago-Gastric Cancer Using Neural Analysis of Pre- and Post-Operative Parameters. Artificial Neural Networks in Urologic Oncology. Neural Networks in Urologic Oncology. Comparison of a Neural Network with High Sensitivity and Specificity to Free/Total Serum PSA for Diagnosing Prostate Cancer in Men with PSA. Artificial Neural Networks and Prognosis in Prostate Cancer. Comparison Between Urologists and Artificial Neural Networks in Bladder Cancer Outcome Prediction. A Probabilistic Neural Network Framework for Detection of Malignant Melanoma.