title: Using neural networks to diagnose cancer.
abstract: While artificial brains are in the realm of science fiction, artificial neural networks (ANNs) are scientific facts. An artificial neural network is a computational structure modeled somewhat on the neural structure of the brain; both have many highly interconnected processing elements. These biologically inspired processing elements are taught by feeding examples until the results are acceptable. In the past 5 years, neural networks have become successful in providing meaningful second opinions in clinical diagnosis. In our research, a prototype artificial neural network was trained on numeral ultrasound data of 52 actual cases and then correctly identified renal cell carcinoma from renal cysts and other conditions without diagnostic errors. Our nonlinear artificial neural network was trained on software using the standard backpropagation paradigm on a 80386 microcomputer. Our ANN learned from ultrasound data in 52 cases (17 malignant, 30 cysts, and 5 other) at a Memphis hospital. The trained prototype performed without error on 47 cases which were not in the data used for training. This prototype must be validated by extending this study to more cases.
authors.affiliation: University of Tennessee, Department of Health Informatics, Memphis 38163.
authors.forename: P S
authors.initials: PS
authors.lastname: Maclin
authors.forename: J
authors.initials: J
authors.lastname: Dempsey
authors.forename: J
authors.initials: J
authors.lastname: Brooks
authors.forename: J
authors.initials: J
authors.lastname: Rand
mesh_terms: D002292:Carcinoma, Renal Cell / Q000175:diagnosis; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation*; D003937:Diagnosis, Differential; D005103:Expert Systems; D006801:Humans; D052177:Kidney Diseases, Cystic / Q000175:diagnosis; D007680:Kidney Neoplasms / Q000175:diagnosis; D008838:Microcomputers; D009369:Neoplasms / Q000175:diagnosis*; D016571:Neural Networks, Computer; D012984:Software