Although many workers have applied neural nets to various types of I-dimensional medical data (e.g. ECG and EEG waveforms), little work has been done on applying neural nets to diagnosis directly from medical images.

Armando Manduca Dept. of Physiology and Biophysics Mayo Clinic Rochester, MN 55905 PaulChristy Dept. of Diagnostic Radiology Mayo Clinic Rochester, MN 55905 Richard Ehman Dept. of Diagnostic Radiology Mayo Clinic Rochester, MN 55905 Abstract Avascular necrosis (AVN) of the femoral head is a common yet potentially seriousdisorder which can be detected in its very early stages with magnetic resonance imaging. We have developed multi-layer perceptron networks, trained with conjugate gradient optimization, which diagnose AVN from single magnetic resonance images of the femoral head with 100% accuracy on training data and 97% accuracy on test data. 1 INTRODUCTION Diagnostic radiology may be a very natural field of application for neural networks, since a simple answer is desired from a complex image, and the learning process that human experts undergo is to a large extent a supervised learning experience based on looking at large numbers of images with known interpretations. Although many workers have applied neural nets to various types of I-dimensional medical data (e.g. ECG and EEG waveforms), little work has been done on applying neural nets to diagnosis directly from medical images. We chose the diagnosis of avascular necrosis from magnetic resonance images as an ideal initial problem, because: the area in question is small and well-defined, its size and shape do not vary greatly between individuals, the condition (if present) is usually visible even at low spatial and gray level resolution on a single image, and real data is readily available.