PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Classification Algorithms”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 235 records · Page 13Linked to original sources
PubMed1992

Data processing for multi-channel optical recording: action potential detection by neural network.

Using a neural network, we have developed a program for fast and precise detection of action potentials (AP) in raw multi-channel optical recording data. The AP detection was performed in two steps: first, peaks were detected in raw optical data, and, second, the peaks were classified by the neural network into APs, noise and undecided peaks. The network was optimized and trained by the backpropagation learning algorithm, employing some thousands of manually classified peaks. The performance of the optimized network was found to be not completely satisfactory, although it was better than the classification by template matching and nearest-neighbor rules. The addition of a signal-to-noise ratio (SNR) of a peak to the network classification improved the classification performance: in comparison with the manual classification results, 96% of manually classified APs were detected. The causes of classification errors were discussed. In spite of the fact that the program required a slight amount of human intervention for undecided peaks, the program could allow mostly automatic AP detection.

Action Potentials↗
PubMed1990

A hybrid classifier for automated radiologic diagnosis: preliminary results and clinical applications.

We describe the design, implementation, and preliminary evaluation of a computer system to aid clinicians in the interpretation of cranial magnetic-resonance (MR) images. The system classifies normal and pathologic tissues in a test set of MR scans with high accuracy. It also provides a simple, rapid means whereby an unassisted expert may reliably label an image with his best judgment of its histologic composition, yielding a gold-standard image; this step facilitates objective evaluation of classifier performance. This system consists of a preprocessing module; a semiautomatic, reliable procedure for obtaining objective estimates of an expert's opinion of an image's tissue composition; a classification module based on a combination of the maximum-likelihood (ML) classifier and the isodata unsupervised-clustering algorithm; and an evaluation module based on confusion-matrix generation. The algorithms for classifier evaluation and gold-standard acquisition are advances over previous methods. Furthermore, the combination of a clustering algorithm and a statistical classifier provides advantages not found in systems using either method alone.

Algorithms↗
PubMed1985

[Automatic and semiautomatic contour finding of the left ventricle in the 2 dimensional echocardiogram. In vitro studies in formalin-fixed swine hearts].

In order to test semiautomatic and automatic contour finding procedures in 2-dimensional echocardiograms we determined endocardial borders in 42 short-axis slices of post-mortem animal hearts after interactive image enhancement such as scaling, normalisation and linearisation of grey levels semiautomatically and automatically by a complex contour finding algorithm. The areas calculated on the basis of these semiautomatic and automatic procedures were compared with "true" anatomic areas derived from planimetry. The complex computer algorithm is based on the detection and analysis of grey level gradients. The algorithm first creates a raw contour which still contains intra- and extracavitary artefacts as well as interrupted endocardial strings. Using a statistical iterative classification procedure and a least-square polynomial approximation the endocardial strings were structured, completed and smoothed and the artefacts eliminated. We were able to determine endocardial contours by semiautomatic methods in 33 (79%) and by automatic procedures in 30 (73%) of the echocardiograms. The correlation between semiautomatic and "true" contours was r = 0.97; y = 1.01x-0.46; standard error of the estimate (SEE) 0.51 cm2, between automatic and "true" contours r = 0.98; y = 0.99x - 0.31; SEE 0.43 cm2; the correlation parameters between the anatomic "true" areas and the areas calculated on the basis of manually derived borders were r = 0.98; y = 0.97x - 0.05; SEE 0.45 cm2. From our studies we conclude that left ventricular endocardium in short axis slices of postmortem animal hearts could reliably and reproducibly be detected by semiautomatic as well as by automatic procedures using a contour finding algorithm.

Animals↗
PubMed1990

Plosive/fricative distinction: the voiceless case.

Using only three measures of the waveform, the zero-crossing rate, the logarithm of the root-mean-square (rms) energy, and the derivative of the log rms energy with respect to time [termed rate of rise (ROR)], voiceless plosives (including affricates) can be distinguished from voiceless fricatives in word-initial, medial, and final positions. Peaks in the ROR contour are considered for significance to the plosive/fricative distinction by examining the log rms energy and zero-crossing rate. Then, the magnitude of the first significant peak in the ROR contour is used as the primary classifier. The algorithm was tested on 1364 tokens (720 word-initial tokens produced by four female and four male speakers; 360 word-medial tokens produced by two males and two females; 320 word-final tokens produced by two males and two females). Data from two male and two female speakers (360 word-initial tokens) were used as a training set, and the remaining data were used as a test set. The overall rate of correct classification was 96.8%. Implications of this result are discussed.

Algorithms↗
PubMed1985

Maximum likelihood estimation of variance components for a multivariate mixed model with equal design matrices.

An algorithm is described for estimating variance and covariance components by restricted maximum likelihood for a multivariate mixed two-way classification with equal design matrices. The procedure involves a transformation to canonical scale, effectively reducing a q-variate analysis to q corresponding univariate analyses. A small numerical example is given as well as a large-scale practical application.

Analysis of Variance↗
PubMed1990

Image analysis of Nissl-stained neuronal perikarya in the primary visual cortex of the rat: automatic detection and segmentation of neuronal profiles with nuclei and nucleoli.

An image analysing procedure for the morphometric characterization of cortical neurons in Nissl-stained brain sections is described. It consists of the automatic detection of cellular profiles and their compartments: cytoplasm, nucleus and nucleolus. The algorithm was designed to cope with the large morphological spectrum of cortical perikarya (e.g. geometrical properties of perikarya, staining intensities of cell compartments and nucleo-plasmic area-ratio) including pyramidal (Golgi-category I) and non-pyramidal (Golgi-category II) neurons. Clusters of cells were separated and non-neuronal structures (e.g. glia, endothelial cells) as well as tangential, non-nucleolated sections through neuronal perikarya recognized and excluded from further analysis without requiring interactive procedures. The performance of the profile recognition procedure was evaluated using 426 nucleolated and non-nucleolated profiles of different types of neurons in the primary visual cortex of the rat. Nucleolated profiles were recognized as such with a 91% accuracy, non-nucleolated profiles were rejected correctly in 90% of cases. After automatic segmentation and selection of nucleolated neuronal profiles from the microscopic field, a large set of quantitative morphological features including geometrical, densitometrical and textural parameters can be measured using high power light microscopy. This permits quantitative morphometric characterization of different neuronal types. This procedure is the first part of a system for the automatic classification of Nissl-stained cortical neurons.

Algorithms↗
PubMed2026

Digital and computational morphology in hematology: current platforms, clinical evidence, and future requirements.

INTRODUCTION: Morphologic examination of peripheral blood and bone marrow remains central to the diagnosis and classification of hematologic disorders. Conventional optical microscopy, however, is labor-intensive, dependent on operator expertise, and affected by interobserver variability. Digital morphology has developed from automated image acquisition and cell pre-classification into a broader field that includes whole-slide imaging, remote review, quantitative morphometry, and artificial intelligence-based analysis. CONTENT: This review examines current applications of digital morphology in peripheral blood, bone marrow aspirates, malaria detection, and body-fluid analysis. Commercial platforms are evaluated with particular attention to the distinction between raw automated pre-classification, expert digital post-classification, and comparison with independent optical microscopy. Digital systems generally perform well for common mature leukocyte populations but remain less reliable for rare or diagnostically critical cells, including blasts, abnormal lymphoid cells, plasma cells, and intermediate maturation stages. Research systems increasingly extend analysis from individual-cell classification to whole-slide, specimen-level, and patient-level assessment. SUMMARY: Digital morphology can improve standardization, image traceability, remote consultation, education, proficiency testing, quality assurance, and selected aspects of laboratory workflow. Its clinical value depends on appropriate validation, transparent reporting of reference methods, recognition of algorithm-specific failure modes, and clearly defined criteria for expert review and conventional microscopy. Human expertise remains essential not only for validating results but also for adapting cell taxonomies and interpretive rules to evolving classifications of hematologic diseases. OUTLOOK: Future progress will require representative multicenter datasets, harmonized morphologic terminology, external validation, interoperability with laboratory information systems, and continuous monitoring after software or hardware updates. Integration of morphology with quantitative hematology, flow cytometry, cytogenetics, genomics, and clinical data may support more comprehensive computational diagnosis. Digital platforms may also broaden access to specialist expertise, training, and quality programs in resource-limited institutions and regions, provided that infrastructure, governance, and professional competency are adequately supported.

artificial intelligence↗
PubMed2026

An Integrative Morphological and Genomic Analysis With a Refined Fluorescence In Situ Hybridization (FISH) Threshold and Novel Kinase Fusions in a Large Asian Cohort of Spitzoid Neoplasms.

Differentiating atypical Spitz tumors (ASTs) from true Spitz melanomas (SMs) and conventional melanomas with spitzoid features (MSFs) remains a formidable diagnostic challenge. Because current molecular epidemiological data are overwhelmingly derived from Caucasian cohorts, the genomic landscape of Asian populations remains largely unexplored. To elucidate the molecular progression landscape and refine the diagnostic criteria, we performed a comprehensive multimodal analysis-integrating histomorphology, immunohistochemistry, multiprobe fluorescence in situ hybridization (FISH), and targeted RNA/DNA-based next-generation sequencing (NGS)-on a cohort of 140 spitzoid neoplasms. This cohort, comprising 126 ASTs, 8 SMs, and 6 MSFs, represents the largest Asian cohort to date. Malignant phenotype strongly correlated with lesional asymmetry, deep atypical mitoses, a sheet-like growth pattern, diffuse preferentially expressed antigen of melanoma positivity, and significant loss of p16 expression (64.3% in SM/MSF vs 9.5% in ASTs; P < .0001). Building upon the established melanoma FISH criteria, we optimized a prognostic threshold of &#x2265;2 FISH abnormalities specifically tailored for spitzoid neoplasms. We demonstrated that isolated single chromosomal aberrations (particularly MYB loss) are relatively stable events that are frequent in indolent ASTs, whereas our refined &#x2265;2 threshold yielded 100% sensitivity and 92.5% specificity for predicting regional lymph node metastasis/local recurrence. Molecularly, NGS identified mutually exclusive initiating driver alterations (comprising kinase fusions and HRAS mutations) in 89.9% of true Spitz neoplasms, a remarkably high prevalence suggesting a distinct genetic background in Asian populations. We also characterized 5 entirely novel kinase fusions (ZNF24::ROS1, PCBP1::ROS1, NUMA1::RET, CBWD1::ALK, and TPR::NTRK1). Furthermore, NGS definitively segregated true Spitz neoplasms from morphological mimics (MSF), which lacked fusions and were driven by canonical genomic alterations of the conventional melanoma pathway. Integrating these genomic landscapes validated a stepwise progression model. Although isolated kinase fusions drove indolent ASTs, malignant SM invariably harbored concurrent pathogenic secondary alterations, demonstrating a profound reliance on CDKN2A/B, TP53, and CDK4 aberrations. Ultimately, we propose an integrated diagnostic algorithm combining morphological evaluation, the refined FISH threshold, and comprehensive NGS profiling, providing a precise, evidence-based framework for pathway classification and clinical management of spitzoid neoplasms.

fluorescence in situ hybridization↗
PubMed1991

[The etiological differentiation of neuromuscular produced dysphagia by x-ray cinematography].

850 patients with dysphagia were examined by x-ray cinematography. On the basis of these examinations the normal events of swallowing are compared with the abnormalities observed. The technique is described. An algorithm has been developed depending on the presence of symmetry or asymmetry of the abnormalities and on muscle tone, which permits classification of the various aetiological groups. In addition, specific features of individual diseases often make it possible to arrive at a definite diagnosis.

Cineradiography↗
PubMed1989

A simple quantitative screening test for the detection of extracranial carotid artery disease.

A simple screening test, using continuous wave Doppler ultrasound, for the detection of all grades of extracranial carotid artery disease has been described. The test is composed of two parts: (1) the determination of the direction of flow at the orbit and (2) on-line principal component factor analysis of the maximum frequency envelope of the Doppler shifted signal obtained from the common carotid artery. A total of 154 vessel segments have been investigated; 69 normal, 41 with a stenosis of 10-49%, 32 with a stenosis of 50-99% and 12 occlusions. Of these, 70 vessel segments were assessed prospectively and they formed the data base from which the principal components and the classification factor were calculated. The remaining 84 vessel segments were analysed prospectively on-line. The combined results gave an overall sensitivity to the detection of disease of 90% and a specificity of 77%. It has been shown that although classification of Doppler waveforms by principal component analysis is a fairly sophisticated technique it is possible, by careful design of the algorithms, to design a near real-time on-line analysis system for derived waveforms such as the maximum frequency envelope.

Algorithms↗
PubMed1992

Artificial intelligence in automated classification of rat vaginal smear cells.

Microscopic examination of vaginal smears has been used routinely to determine the stage of the estrous cycle of female rats in reproductive research. The stage of the estrous cycle is based on relative counts of nucleated epithelial cells, cornified epithelial cells and leukocytes. The purpose of this project was to explore automation of vaginal smear analysis using image processing and artificial intelligence techniques. A fully connected back-propagation neural network was used to locate all potential objects in a digitized scene. A unique algorithm was then employed to center a subsequent sampling box to collect pixel intensity values from the red and green components of each image. A final neural network was used in the classification of cell type. Neural networks were used because of their ability to generalize among input patterns and to tolerate extraneous noise due to variations in staining artifacts and aberrant illumination of the microscope field. This preliminary cell diagnosing system not only provides the basis for the fully automated system but also provides a method by which many other cytologic image processing problems can be automated.

Animals↗
PubMedDate not supplied

Using the ID3 algorithm to find discrepant diagnoses from laboratory databases of thyroid patients.

Rare cases are a central problem when an expert system is constructed from example cases with machine learning techniques. It is difficult to make a decision support system (DSS) to cover all possible clinical cases. An inductive learning program can be used to construct an expert system for detecting cases that differ from routine cases. The ID3 algorithm and the pessimistic pruning algorithm were tested in this study: a DSS was built directly from the data of patient records. A decision tree was generated, and the cases misclassified by the decision tree as compared with the classifications of a clinician were listed on a checklist, which formed the feedback to the clinician. In clinical situations about 5-10% of functional thyroid disorders may be misclassified. At this error level, the method found over 90% of the errors with a specificity of 95%. In simple medical classification tasks this dynamic self-learning system can be used to create a DSS that can assist in the quality control of clinical decision making.

Adult↗
PubMed1985

Perioperative functions. Classification of knowledge and required skills.

The study results form a beginning framework from which values of resource appropriateness could be developed, which was the original intent of this study. An algorithm could be designed to score an individual's formal and informal training in specific skills as they relate to the O-Types identified. These results could be translated into resource appropriateness scores under each of the four factors. The results also provide a means for simplifying categories of the Operating Room Staffing Model and provide a beginning framework to assess resource appropriateness.

Clinical Competence↗
PubMedDate not supplied

Mapping between MR brain images and a voxel model.

This paper describes an approach to establish the correspondence between a magnetic resonance (MR) image of the brain and a slice through a 3D anatomical model. The model is of voxel structure that symbolically labels primary tissue types such as grey matter, white matter, CSF, etc. In this approach a slice is first searched for in the model to achieve the best general match with the brain MR image in question. The operation involves a minimization of parameters such as position, rotation, slant, tilt and enlargement. Having thus found a globally good registration between the image and the model, local matches that link every pixel in the image through to the model slice are then searched for. This pixel-by-pixel match is expressed within a pair of maps, one for the vertical deformation and the other for the horizontal one. The matching algorithm consists of a series of octave separated blurring convolutions combined with exhaustive grey-valued correlation. Because every pixel in the model slice is labelled in terms of its tissue type, and because every pixel in the image has been matched directly to the model, every pixel in the image is now classified. This classification is used directly to perform segmentation which serves as a basis for the computation of medically relevant indices.

Algorithms↗