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GlossaryConvolutional Neural Network

deep learning model for analyzing visual data

A Convolutional Neural Network (CNN) is a type of deep learning algorithm specifically designed for processing structured grid data like images. It uses layers of convolutional filters to automatically and adaptively learn spatial hierarchies of features from input data, making it highly effective for tasks such as image recognition, classification, and segmentation.

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Hair Disease Classification Using Convolutional Neural Network Algorithm with VGG-16 Architecture

research Hair Disease Classification Using Convolutional Neural Network Algorithm with VGG-16 Architecture

October 2023 in “Sinkron”

The system can accurately classify hair diseases with 94.5% accuracy using a CNN.

Convolutional Neural Networks for Non-Invasive Diagnosis of Androgenetic Alopecia Using Dermoscopic Hair Images

research Convolutional Neural Networks for Non-Invasive Diagnosis of Androgenetic Alopecia Using Dermoscopic Hair Images

August 2024

Deep learning can improve non-invasive alopecia diagnosis using hair images.

Identification of Drug-Disease Associations Using Information of Molecular Structures and Clinical Symptoms via Deep Convolutional Neural Network

research Identification of Drug-Disease Associations Using Information of Molecular Structures and Clinical Symptoms via Deep Convolutional Neural Network

18 citations, January 2020 in “Frontiers in Chemistry”

A new model can predict drug-disease links well, helping drug research.

Methods of Transfer Learning for Multiclass Hair Disease Categorization

research Methods of Transfer Learning for Multiclass Hair Disease Categorization

December 2023

Transfer learning with three neural network architectures accurately classifies hair diseases.

Trichoscopy of Alopecia Areata: Hair Loss Feature Extraction and Computation Using Grid Line Selection and Eigenvalue

research Trichoscopy of Alopecia Areata: Hair Loss Feature Extraction and Computation Using Grid Line Selection and Eigenvalue

10 citations, September 2020 in “Computational and Mathematical Methods in Medicine”

Researchers developed an algorithm for self-diagnosing scalp conditions with high accuracy using smart device-attached microscopes.

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