Table of contents of journal:

Results: 13

Introduction to artificial neural networks and their use in cancer diagnosis, prognosis, and patient management

Authors: Naguib, RNG Sherbet, GV

Citation: Rng. Naguib et Gv. Sherbet, Introduction to artificial neural networks and their use in cancer diagnosis, prognosis, and patient management, BIOMED EN S, 2001, pp. 1-7

Analysis of molecular prognostic factors in breast cancer by artificial neural networks

Authors: Angus, B Lennard, TWJ Naguib, RNG Sherbet, GV

Citation: B. Angus et al., Analysis of molecular prognostic factors in breast cancer by artificial neural networks, BIOMED EN S, 2001, pp. 9-21

Artificial neural approach to analysing the prognostic significance of DNAploidy and cell cycle distribution of breast cancer aspirate cells

Authors: Naguib, RNG Sherbet, GV

Citation: Rng. Naguib et Gv. Sherbet, Artificial neural approach to analysing the prognostic significance of DNAploidy and cell cycle distribution of breast cancer aspirate cells, BIOMED EN S, 2001, pp. 23-27

Neural networks for the estimation of prognosis in lung cancer

Authors: Esteva, H Bellotti, M Marchevsky, AM

Citation: H. Esteva et al., Neural networks for the estimation of prognosis in lung cancer, BIOMED EN S, 2001, pp. 29-37

The use of a genetic algorithm neural network (GANN) for prognosis in surgically treated nonsmall cell lung cancer (NSCLC)

Authors: Jefferson, MF Pendleton, N Lucas, SB Horan, MA

Citation: Mf. Jefferson et al., The use of a genetic algorithm neural network (GANN) for prognosis in surgically treated nonsmall cell lung cancer (NSCLC), BIOMED EN S, 2001, pp. 39-54

The use of machine learning in screening for oral cancer

Authors: Speight, PM Hammond, P

Citation: Pm. Speight et P. Hammond, The use of machine learning in screening for oral cancer, BIOMED EN S, 2001, pp. 55-72

Outcome prediction of oesophago-gastric cancer using neural analysis of pre- and postoperative parameters

Authors: Wayman, J Griffin, SM

Citation: J. Wayman et Sm. Griffin, Outcome prediction of oesophago-gastric cancer using neural analysis of pre- and postoperative parameters, BIOMED EN S, 2001, pp. 73-91

Artificial neural networks in urologic oncology

Authors: Douglas, TH Moul, JW

Citation: Th. Douglas et Jw. Moul, Artificial neural networks in urologic oncology, BIOMED EN S, 2001, pp. 93-101

Neural networks in urologic oncology

Authors: Niederberger, C Ridout, D

Citation: C. Niederberger et D. Ridout, Neural networks in urologic oncology, BIOMED EN S, 2001, pp. 103-113

Comparison of a neural network with high sensitivity and specificity to free/total serum PSA for diagnosing prostate cancer in men with a PSA < 4.0 ng/mL

Authors: Stamey, TA Barnhill, SD Zhang, Z Yemoto, CM Zhang, H Madyastha, KR

Citation: Ta. Stamey et al., Comparison of a neural network with high sensitivity and specificity to free/total serum PSA for diagnosing prostate cancer in men with a PSA < 4.0 ng/mL, BIOMED EN S, 2001, pp. 115-124

Artificial neural networks and prognosis in prostate cancer

Authors: Hamdy, FC

Citation: Fc. Hamdy, Artificial neural networks and prognosis in prostate cancer, BIOMED EN S, 2001, pp. 125-131

Comparison between urologists and artificial neural networks in bladder cancer outcome prediction

Authors: Qureshi, KN Mellon, JK

Citation: Kn. Qureshi et Jk. Mellon, Comparison between urologists and artificial neural networks in bladder cancer outcome prediction, BIOMED EN S, 2001, pp. 133-140

A probabilistic neural network framework for the detection of malignant melanoma

Authors: Hintz-Madsen, M Hansen, LK Larsen, J Drzewiecki, KT

Citation: M. Hintz-madsen et al., A probabilistic neural network framework for the detection of malignant melanoma, BIOMED EN S, 2001, pp. 141-183

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