The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances. ing a Decision Support System for the Histopathalogical Diagnosis of Chronic Idiopathic Inflammatory Bowel Disease- Comparison of Radial Basis Function Neural Networks and Logistic Regression

The medical problem domain that is investigated i this study is the histopathological diagnosis of chronic idiopathic inflammatory bowel disease (CIBD). CIBD is a generic category that describes diseases of the bowel which are characterised by acute and chronic inflammation and which have no identified aetiological agent (such as an infective agent) The two major diseases within this category are ulcerative colitis (UC) and Crohn's disease. Both diseases are chronic conditions characterised by periods of relapse and remission and may produce life threatening complications such as intestinal perforation, sepsis and carcinoma. Many conditions mimic the clinical symptoms of CIBID (Farmer, 1990: Hamilton 1987: Moxon et al. 1994 Shepherd 1991: Shivananda et al. 1991: Suarwicz et al. 1994) and thus histopathalogical examination of colorectal biopsies is important, both in confirming the diagnosis and in excluding other conditions such as infective colitis..........

Making the Distinction Between Crohn's Disease and Ulcerative Colitis by Histopathological Examination: A Comparison of Human Performance, Logistic Regression and Adaptive Resonance Theory Mapping Neural Networks (ARTMAP)

280 cases of inflammatory bowel disease were examined histopathologically and defined features were observed and recorded using a graphical user interface with digitised reference images. The outcome of each case was determined independent of the histopathological report giving 75 cases of Crohn's disease and 105 cases of ulcerative colitis. The cases were randomised and split into training and test sets, each of 140 cases. All 23 observed features were used as input data for logistic regression and adaptive resonance theory mapping neural networks (ARTMAP). The ARTMAP's were used singularly or as a voting cohort of 11 networks, the majority and unanimous decisions of the cohort were analysed separately. The vigilance parameter for the ARTMAP's was varied from high (0.9) to lower (0.5) to vary the number of clusters in ARTa. The best resukts were produced by logistic regression and the 11 high vigilance ARTMAP's with a sensitivity for Crohn's disease of 60% and a specificity of 80%. This was a significant improvement on the original human diagnoses that gave a sensitivity of 25% and specificity of 60%. Either of these technologies could form the basis of a decision support system in this domain (193

An Automated Pattern Recognition System for the Quantification of Inflammatory Cells in Hepatitis C Infected Liver Biopsies.

Hepatitis C is a common viral infection of the liver. The degree of inflammation associated with the infection is normally estimated manually from a liver biopsy, by considering the quantity and nature of inflammatory cells. This paper presents an automated pattern recognition system for the quantification of inflammatory cells in liver biopsies. Initially, images are corrected for colour variation. Features are then extracted for from colour biopsy images at positions of interest identified by adaptive thresholding and clump decomposition. A sequential floating search method and principal component analysis are used to reduce the dimensionality of the feature vector. Manually annotated training images allow supervised training by providing the class membership for each position of interest. Gaussian parametric and Gaussian mixture model density estimation methods are compared and are used to classify cells as either inflammatory or healthy via Bayes' theorem. The system is optimised using a response surface method, where the response or system performance is derived from the area under the receiver operating characteristic curve. The optimised system is then tested on test images previously ranked by a number of observers with varing levels of pathology experience. The observers results are compared to the automated system using Spearman rank correlation. Results show that this system can rank 15 test images, with varying degrees of inflammation, in strong agreement with five expert pathologists

Explanation by General Rules Extracted From trained Multi-Layer Perceptrons

GR2 is a hybrid knowledge-based system where the knowledge acquired in a trained Multi-layer Perceptron is translated into a symbolic and abstract form called general rules. This is based on both white-box and black-box criteria. The extracted rules can be used for inference on a case-by-case basis, explaining how a decision is made. The extracted rules possess both qualitative and quantitative properties of the domain knowledge, thus enhancing the reasoning capability of the system. The methodology for extracting rules from a trained MLP via two heuristics-the Potential Default Set and the Feature Salient Degree-is outlined and the use of the resulting domain rules in case-by-case explanation is described. A number of examples from synthetic domains is considered and the problem of diagnosing malignancy in breast lesions from observed cytopathological features is presented. Here the case explanations are commented upon by a senior pathologist and favourable agreement is found

A Neural Network Decision-Support Tool for the Diagnosis of Breast Cancer

An application of the ARTMAP neural network to the diagnosis of breast cancer is described. Performance results are given for 10 individual ARTMAP networks and the five most accurate such networks using "pooled" decision making (the so-called voting strategy). The results are compared with those of expert and neophyte human pathologists. These show that ARTMAP diagnoses are at least as accurate as those of the expert and can approach the optimum for the domain. However, human pathologists bias their predictions in order to minimise false positive predictions at the expense of increased false negatives. The same effect is achieved in ARTMAP by pruning category cluster nodes which make positive predictions........

Evaluating a Neural Network Decision-Support Tool for the Diagnosis of Breast Cancer

This paper describes the evaluation of an applicant of the ARTMAP neural network model to the diagnosis of breast cancer from fine-needle aspirates of the breast. The network has previously demonstrated very high performance when used with high-quality data provided by an expert pathologist. New performance results are provided for its use with "noisy" data provided by an inexperienced pathologist.....

Application of the Fuzzy ARTMAP Neural Network Model to Medical Pattern Classification Tasks

This paper presents research into the application of the fuzzy ARTMAP neural network model to medical pattern classification tasks. A number of domains, both diagnostic and prognostic, are considered. Each such domain highlights a particularly useful aspect of the model. The first, coronary care patient prognosis, demonstrates the ATMAP voting strategy involving "pooled" decision-making using a number of networks, each of which has learned a slightly different mapping of input features to pattern classes. The second domain, breast cancer diagnosis, demonstrates the model's symbolic rule extraction capabilities which support the validation and explanation of a network's predictions. The final domain, diagnosis of acute myocardial infarction, demonstrates a novel category pruning technique allowing the performance of a trained network to be altered so far as to favour predictions of one class over another (e.g. trading sensitivity for specificity or vice versa). It also introduces a "cascaded" variant of the voting strategy intended to allow identification of a subset of cases which the network has a very high certainty of classifying correctly

Feeding Tests with Indigofera endecaphylla Jacq. (Creeping Indigo) and Some Observations on Its Poisonous Effects on Domestic Animals

Leguminous plants have long been considered important as livestock feeds in Hawaii. They decrease the need for costly oil cakes and protein rich concentrates in milk and meat production. Indigofera endecapliylla Jacq. (creeping indigo or trailing indigo) seemed promising for a time as a high-rainfall-zone legume. Early experiments proved that it would grow well with a wide variety of associated grasses, and grazing tests showed that it was palatable and quite persistent under pasture conditions. Very little was known, on the other hand, of its feeding value for livestock. In 10 years of short-interval pasture trials with relatively small proportions of the legume, no adverse effects were noted on young cattle. However, when the concentration of the legume exceeded about 50 per cent of the forage, toxicity symptoms began to appear. A study of the effect of a strain of Indigofera endecapliylla Jacq. grown in Hawaii and tested as a feed for cows, heifers, sheep, and rabbits is presented

A Decision-Support Tool for the Diagnosis of Breast Cancer Based upon Fuzzy ARTMAP

This paper presents research into the application of the fuzzy ARTMAP neural network model to the diagnosis of cancer from fine-needle aspirates of the breast. Trained fuzzy ARTMAP networks are differently pruned so as to maximise accuracy, sensitivity and specificity. The differently pruned networks are then employed in a "cascade" of networks intended to separate cases into "certain" and "suspicious" classes. This mimics the predictive behaviour of a human pathologist. The fuzzy ARTMAP model also provides symbolic rule extraction facilities and the validity of the derived rules for this domain is discussed. Additionally, results are provided showing the effects upon network performance of different input features and different observers. The implications of the findings are discussed

Adaptive Resonance Theory: A Foundation for "Apprentice" Systems in Clinical Decision Support?

The idea of an "apprentice" system in contrast to an expert system, is introduced, as one which continues, perpetually, to refine its knowledge-base. Neural networks appear to offer the necessary learning ability for this task, and the Adaptive Resonance Theory family is particularly suited to on-line (casual) learning. The ability of these networks accurately to represent decision problems and to disclose their acquired knowledge is discussed, and their practical application is assessed. Two problems of medical decision making are considered using the approach. The first is the early diagnosis of myocardial infarction from clinical and electrocardiographic data gathered at presentation. The second is the cytopathological diagnosis of breast lesions from fine needle aspirate samples. In both cases good performance is obtained along with sets of "if-then" rules which are in accordance with medical opinion. In the first case, examples of on-line learning are given and the system is seen to be behaving as expected, with performance improving with increasing sample size