C++ Neural Networks and Fuzzy Logic: Preface

Detecting Skin Cancer

F. Ercal, A. Chawla, W. Stoecker, and R. Moss study a neural network approach to the diagnosis of malignant

melanoma. They strive to discriminate tumor images as malignant or benign. There are as many as three

categories of benign tumors to be distinguished from malignant melanoma. Color images of skin tumors are

C++ Neural Networks and Fuzzy Logic:Preface

Crisp and Fuzzy Neural Networks for Handwritten Character Recognition

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used in the study. Digital images of tumors are classified. Backpropagation is used. Two approaches are taken

to reduce training time. The first approach involves using fewer hidden layers, and the second involves

randomization of the order of presentation of the training set.

EEG Diagnosis

Fred Wu, Jeremy Slater, R. Eugene Ramsay, and Lawrence Honig use a feedforward backpropagation neural

network as a classifier in EEG diagnosis. They compare the performance of the neural network classifier to

that of a nearest neighbor classifier. The neural network classifier shows a classifier accuracy of 75% for

Multiple Sclerosis patients versus 65% for the nearest neighbor algorithm.