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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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7 results for “cervical spondylosis”

EasiCS: the objective and fine-grained classification method of cervical spondylosis dysfunctioncs.LG

EasiCS improves neck function assessment by objectively classifying cervical spondylosis.

problem Subjective and coarse-grained neck function assessment methods.

method Developed clustering algorithms on sEMG data to objectively classify cervical spondylosis.

result EasiCS outperforms existing seven algorithms overall.

cervical spondylosis neck function assessment objective classification sEMG clustering algorithms

2019-05-15abs ↗pdf ↗

EasiCSDeep: A deep learning model for Cervical Spondylosis Identification using surface electromyography signalcs.LG

Deep learning identifies cervical spondylosis from sEMG signals.

problem Early identification of cervical spondylosis for improved cure rate and reduced costs.

method Convolutional neural network-based multi-channel algorithm.

result Significant improvement in CS identification compared to previous methods.

cervical spondylosis deep learning surface electromyography convolutional neural network spatial relationship

2018-12-12abs ↗pdf ↗

Algorithms for screening of Cervical Cancer: A chronological reviewcs.LG

This review chronicles AI algorithms for cervical cancer screening.

problem Automated screening of cervical cancer using AI methods.

method Analysis of various machine learning algorithms and clustering techniques.

result Holistic review of computational methods over time.

cervical cancer AI algorithms cell segmentation classification machine learning

2018-11-02abs ↗pdf ↗

Classification of Cervical Cancer Datasetcs.CY

The paper classifies cervical cancer using various techniques and feature selection.

problem Classifying cervical cancer from a dataset with missing values and imbalance.

method Feature selection, over-sampling, under-sampling, dimensionality reduction, and classification techniques.

result Age, first sexual intercourse, number of pregnancies, smokes, hormonal contraceptives, and STDs: genital herpes are the main predictive features with high accuracy.

cervical cancer classification feature selection over-sampling under-sampling

2018-12-11abs ↗pdf ↗

Estimation of preterm birth markers with U-Net segmentation networkeess.IV

Deep learning predicts preterm birth risk with improved accuracy.

problem Improving accuracy in predicting spontaneous preterm deliveries.

method U-Net segmentation network for automatic extraction of cervical length and anterior cervical angle.

result Combined markers reduce false-negative ratio from 30% to 18%

preterm birth deep learning ultrasound cervical length anterior cervical angle

2019-08-24abs ↗pdf ↗

Hierarchical Hidden Markov Jump Processes for Cancer Screening Modelingstat.ME

New model improves cancer screening prediction accuracy.

problem Modeling disease progression with heterogeneous populations and irregular data.

method Hierarchical Hidden Markov Jump Processes with piece-wise stationary transitions and scalable EM algorithm.

result Model outperforms state-of-the-art models in prediction accuracy and generating Kaplan-Meier estimators.

cancer screening hidden markov jump processes hierarchical models EM algorithm Kaplan-Meier estimators

2019-10-13abs ↗pdf ↗

Group Probability-Weighted Tree Sums for Interpretable Modeling of Heterogeneous Datacs.LG

G-FIGS uses instance weights to create interpretable models from diverse data.

problem Generalizing to diverse data distributions while maintaining interpretability.

method Estimates group membership probabilities, uses as instance weights in FIGS to grow decision trees.

result Achieves state-of-the-art prediction performance and maintains interpretability.

Machine Learning Interpretability Heterogeneous Data Group Membership Decision Trees

2022-05-30abs ↗pdf ↗