Medical Diagnosis

Recognition of medical conditions, such as cancer.


Articles & Tech Transfers


A neural network method for detection of obstructive sleep apnea and narcolepsy based on pupil size and EEG
Abstract Electroencephalogram (EEG) is able to indicate states of mental activity ranging from concentrated cognitive efforts to sleepiness. Such mental activity can be reflected by EEG energy. In particular, intrusion of EEG theta ...

An intelligent ballistocardiographic chair using a novel SF-ART neural network and biorthogonal wavelets
Abstract This paper presents a comparative analysis of novel supervised fuzzy adaptive resonance theory (SF-ART), multilayer perceptron (MLP) and Multi Layer Perceptrons (MLP) neural networks over Ballistocardiogram (BCG) signal ...

Ovarian cancer diagnosis with complementary learning fuzzy neural network
Abstract Early detection is paramount to reduce the high death rate of ovarian cancer. Unfortunately, current detection toot is not sensitive. New techniques such as deoxyribonucleic acid (DNA) micro-array and proteomics data are ...

Self-organizing arterial pressure pulse classification using neural networks: theoretical considerations and clinical applicability
Abstract A self-organizing classification system for the arterial pressure pulse based on the ART2 (adaptive resonance theory) network was developed. The system consists of a preprocessor and an ART2 recognition network. The ...

Classification of malignant and benign masses based on hybrid ART2LDA approach
Abstract A new type of classifier combining an unsupervised and a supervised model was designed and applied to classification of malignant and benign masses on mammograms. The unsupervised model was based on an adaptive resonance ...

Screening of stress enhancer based on analysis of gene expression profiles: Enhancement of hyperthermia-induced tumor necrosis by an MMP-3 inhibitor
Abstract To improve the therapeutic benefit of hyperthermia, we examined changes of global gene expression after heat shock using DNA microarrays consisting of 12 814 clones. HeLa cells were treated for 1 h at 44degreesC and RNA was ...

Multi-class cancer classification by semi-supervised ellipsoid ARTMAP with gene expression data
Abstract To accurately identify the site of origin of a tumor is crucial to cancer diagnosis and treatment. With the emergence of DNA microarray technologies, constructing gene expression profiles for different cancer types has ...

Time-course data analysis of gene expression profiles reveals purR regulon concerns in organic solvent tolerance in Escherichia coli
Abstract A time-course gene-expression profile was generated for Escherichia coli TK31 when it was exposed to an organic solvent mixture, and classified by fuzzy adaptive resonance theory (Fuzzy ART). It was found that the purR ...

Hybrid feature vector extraction in unsupervised learning neural classifier
Abstract Feature extraction and selection method as a preliminary stage of heart rate variability (HRV) signals unsupervised learning neural classifier is presented. Multi-domain, mixed new feature vector is created from time, ...

Modified signal-to-noise: a new simple and practical gene filtering approach based on the concept of projective adaptive resonance theory (PART) filtering method
Abstract Considering the recent advances in and the benefits of DNA microarray technologies, many gene filtering approaches have been employed for the diagnosis and prognosis of diseases. In our previous study, we developed a new ...

Classification of intramural metastases and lymph node metastases of esophageal cancer from gene expression based on boosting and projective adaptive resonance theory
Abstract Esophageal cancer is a well-known cancer with poorer prognosis than other cancers. An optimal and individualized treatment protocol based on accurate diagnosis is urgently needed to improve the treatment of cancer patients. ...

Cancer diagnosis marker extraction for soft tissue sarcomas based on gene expression profiling data by using projective adaptive resonance theory (PART) filtering method
Abstract Background: Recent advances in genome technologies have provided an excellent opportunity to determine the complete biological characteristics of neoplastic tissues, resulting in improved diagnosis and selection of ...

Multiclass cancer classification using semisupervised ellipsoid ARTMAP and particle swarm optimization with gene expression data
Abstract It is crucial for cancer diagnosis and treatment to accurately identify the site of origin of a tumor. With the emergence and rapid advancement of DNA microarray technologies, constructing gene expression profiles for ...

New cancer diagnosis modeling using boosting and projective adaptive resonance theory with improved reliable index
Abstract An optimal and individualized treatment protocol based on accurate diagnosis is urgently required for the adequate treatment of patients. For this purpose, it is important to develop a sophisticated algorithm that can manage ...

The role of attention in the tinnitus decompensation: reinforcement of a large-scale neural decompensation measure
Abstract Large-scale neural correlates of the tinnitus decompensation have been identified by using wavelet phase stability criteria of single sweep sequences of auditory late responses (ALRs). The suggested measure provided an ...

Proposal of new gene filtering method, BagPART, for gene expression analysis with small sample
Abstract A significant problem in gene expression analysis is that the sample size is substantially lower than the number of genes. Bagging is an effective method of solving this problem in the case of small sample datasets. We have ...

Construction of robust prognostic predictors by using projective adaptive resonance theory as a gene filtering method
Abstract We applied projective adaptive resonance theory (PART) to gene screening for DNA microarray data. Genes selected by PART were subjected to our FNN-SWEEP modeling method for the construction of a cancer class prediction ...

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

ART 2-A for optimal test series design in QSAR
Abstract The family of adaptive resonance theory (ART) based systems concerns distinct artificial neural networks for unsupervised and supervised clustering analysis. Among them, the ART 2-A paradigm presents numerous strengths for ...

Analysis of expression profile using fuzzy adaptive resonance theory
Abstract Motivation: It is well understood that the successful clustering of expression profiles give beneficial ideas to understand the functions of uncharacterized genes. In order to realize such a successful clustering, we ...

A Computational Neural Approach to Support the Discovery of Gene Function and Classes of Cancer
Abstract Advances in molecular classification of tumours may play a central role in cancer treatment. Here, a novel approach to genome expression pattern interpretation is described and applied to the recognition of B-cell ...

Predicting risk of an adverse event in complex medical data sets using fuzzy ARTMAP network
Abstract Fuzzy ARTMAP is a supervised learning system which includes nonlinear dynamics in the learning process. We introduce a new testing procedure which allows the system to estimate the probability of an outcome. Simulations ...

Fuzzy ARTMAP neural network compared to linear discriminant analysis prediction of the length of hospital stay in patients with pneumonia
Abstract On a database derived from patients hospitalized with pneumonia, the authors compared the cross-validated predictions of linear discriminant analysis (LDA) to a new self-organizing supervised neural network that incorporates ...

A fuzzy ARTMAP-based classification system for detecting cancerous cells, based on the one-class problem approach
Abstract This work investigates the use of a fuzzy ARTMAP neural network for detecting cancerous cells, based on the one-class problem approach. This approach is inspired by the way human beings perform pattern recognition. We all ...

Rule extraction: From neural architecture to symbolic representation
Abstract This paper shows how knowledge, in the form of fuzzy rules, can be derived from a supervised learning neural network called furry ARTMAP. Rule extraction proceeds in two stages: pruning, which simplifies the network ...

Comparison of regression modeling and neural network modeling for predicting postoperative adverse events
Abstract Developing clinical models to predict adverse events and mortality following major clinical interventions may be an important part of a quality assessment program. Neural Networks (NN) offer certain advantages when compared ...

Normal and amnesic learning, recognition, and memory by a neural model of cortico hippocampal interactions
Abstract The processes by which humans and other primates learn to recognize objects have been the subject of many models. Processes such as learning, categorization, attention, memory search, expectation and novelty detection work ...

Fuzzy ARTMAP neural network prediction of heart surgery mortality
Abstract A major national effort is underway to determine patterns of medical practice that most effectively result in favorable health outcomes. Databases arising from such effectiveness research may contain tens of thousands of ...

ARTMAP-IC and medical diagnosis: Instance counting and inconsistent cases
Abstract 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 ...

ART neural networks for medical data analysis and fast distributed learning
Abstract ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include airplane design and manufacturing, automatic target recognition, ...

Detection of Carcinoma with the Fuzzy ARTMAP NN
Abstract According to statistics from NIH (National Institute of Health), cervical cancer is the third most common reproductive tract malignancy (ranking behind endometrium and ovary cancers) with about 13000 new case seen annually ...