AIMC Topic: Neural Networks, Computer

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CNN-based deep learning approach for classification of invasive ductal and metastasis types of breast carcinoma.

Cancer medicine
OBJECTIVE: Breast cancer is one of the leading cancer causes among women worldwide. It can be classified as invasive ductal carcinoma (IDC) or metastatic cancer. Early detection of breast cancer is challenging due to the lack of early warning signs. ...

Artificial Intelligence in Dental Caries Diagnosis and Detection: An Umbrella Review.

Clinical and experimental dental research
BACKGROUND AND AIM: Dental caries is largely preventable, yet an important global health issue. Numerous systematic reviews have summarized the efficacy of artificial intelligence (AI) models for the diagnosis and detection of dental caries. Therefor...

High Prevalence of Artifacts in Optical Coherence Tomography With Adequate Signal Strength.

Translational vision science & technology
PURPOSE: This study aims to investigate the prevalence of artifacts in optical coherence tomography (OCT) images with acceptable signal strength and evaluate the performance of supervised deep learning models in improving OCT image quality assessment...

Evaluating the consistency of lenition measures: Neural networks' posterior probability, intensity velocity, and duration.

The Journal of the Acoustical Society of America
Predictions of gradient degree of lenition of voiceless and voiced stops in a corpus of Argentine Spanish are evaluated using three acoustic measures (minimum and maximum intensity velocity and duration) and two recurrent neural network (Phonet) meas...

Speakers-Used as sensors for detecting acoustic loads with artificial intelligence.

The Journal of the Acoustical Society of America
Previous research demonstrated the potential of using speakers as sensors to detect ear canal conditions. This study continues that effort by using a single speaker to measure electrical impedance across various acoustic loads. Electrical impedance d...

A Neural Network-Based Scoring System for Predicting Prognosis and Therapy in Breast Cancer.

Current protocols
Breast cancer is a prevalent malignancy affecting women worldwide. Currently, there are no precise molecular biomarkers with immense potential for accurately predicting breast cancer development, which limits clinical management options. Recent evide...

Multimodal Retinal Imaging Classification for Parkinson's Disease Using a Convolutional Neural Network.

Translational vision science & technology
PURPOSE: Changes in retinal structure and microvasculature are connected to parallel changes in the brain. Two recent studies described machine learning algorithms trained on retinal images and quantitative data that identified Alzheimer's dementia a...

Topographic Clinical Insights From Deep Learning-Based Geographic Atrophy Progression Prediction.

Translational vision science & technology
PURPOSE: To explore the contributions of fundus autofluorescence (FAF) topographic imaging features to the performance of convolutional neural network-based deep learning (DL) algorithms in predicting geographic atrophy (GA) growth rate.

Self-organization toward 1/f noise in deep neural networks.

Chaos (Woodbury, N.Y.)
In biological neural networks, it has been well recognized that a healthy brain exhibits 1/f noise patterns. However, in artificial neural networks that are increasingly matching or even out-performing human cognition, this phenomenon has yet to be e...

Evaluating the efficacy of few-shot learning for GPT-4Vision in neurodegenerative disease histopathology: A comparative analysis with convolutional neural network model.

Neuropathology and applied neurobiology
AIMS: Recent advances in artificial intelligence, particularly with large language models like GPT-4Vision (GPT-4V)-a derivative feature of ChatGPT-have expanded the potential for medical image interpretation. This study evaluates the accuracy of GPT...