Medical diagnosis system and method with multispectral imaging

A skin diagnosis system includes a scanning and optical arrangement whereby light reflected from each incremental area (pixel) of the skin is directed simultaneously to three separate light filters, e.g., IR, red, and green. As a result, the three devices simultaneously produce three signals which are directly related to the reflectance of light of different wavelengths from the corresponding pixel. These three signals for each pixel after processing are used as inputs to one or more output devices to produce a visual color display and/or a hard copy color print, for one usable as a diagnostic aid by a physician

ARTMAP-IC and Medical Diagnosis: Instance Counting and Inconsistent Cases

For complex database prediction problems such as medical diagnosis, the ARTMAP-IC neural network adds distributed prediction and category instance counting to the basic fuzzy ARTMAP system. For the ARTMAP match tracking algorithm, which controls search following a predictive error, a new version facilitates prediction with sparse or inconsistent data. Compared to the original match tracking algorithm (MT+), the new algorithm (MT-) better approximates the real-time network differential equations and further compresses memory without loss of performance. Simulations examine predictive accuracy on four medical databases: Pima Indian diabetes, breast cancer, heart disease, and gall bladder removal. ARTMAP-IC results arc equal to or better than those of logistic regression, K nearest neighbor (KNN), the ADAP perceptron, multisurface pattern separation, CLASSIT, instance-based (IBL), and C4. ARTMAP dynamics are fast, stable, and scalable. A voting strategy improves prediction by training the system several times on different orderings of an input set. Voting, instance counting, and distributed representations combine to form confidence estimates for competing predictions.National Science Foundation (IRI 94-01659); Office of Naval Research (N00014-95-J-0409, N00014-95-0657

Fuzzy neural network methodology applied to medical diagnosis

This paper presents a technique for building expert systems that combines the fuzzy-set approach with artificial neural network structures. This technique can effectively deal with two types of medical knowledge: a nonfuzzy one and a fuzzy one which usually contributes to the process of medical diagnosis. Nonfuzzy numerical data is obtained from medical tests. Fuzzy linguistic rules describing the diagnosis process are provided by a human expert. The proposed method has been successfully applied in veterinary medicine as a support system in the diagnosis of canine liver diseases

Self-reported Symptoms of Reproductive Tract Infections: The Question of Accuracy and Meaning

Verbal surveys are the most common way of gauging any population s health status, but questions remain regarding the accuracy of the responses they elicit. The present paper compares women s self-reports regarding their experiences with reproductive tract infections (RTIs) and the medical diagnosis that they went through to ascertain the presence or otherwise of the infections. Weak concordance was found between women s self-reports and the medical diagnosis, with the former over-representing the presence of infections. Some of the selfreported symptoms were pathogenic in nature, as represented by the true positive reports, but the majority of the self-reports were false positives when compared to medical diagnosis. The conventional health surveys, relying solely on verbal responses, thus, do not essentially represent the actual health situation of a population studied, and any policy intervention formulated exclusively on this information would be flawed. There is a need to understand the non-medical context of illnesses to understand the disease fully.Health, Reproductive Health, Health Surveys Accuracy