TLDR GoogLeNet is the best model for identifying folliculitis.
The study investigates the accuracy of various Convolutional Neural Network (CNN) models in classifying different types of folliculitis, a common skin condition that can lead to severe complications if untreated. The models tested include AlexNet, DenseNet201, GoogLeNet, InceptionV3, ResNet50, VGG19, and Xception. The results indicate that GoogLeNet performs the best in identifying the type of folliculitis. This deep learning-based approach aims to assist dermatologists in accurately diagnosing folliculitis, potentially improving patient outcomes and extending the methodology to other skin conditions.
3 citations,
January 2023 in “European Journal of Information Technologies and Computer Science” The machine learning model accurately detected hair loss and scalp diseases using processed images.
1 citations,
March 2024 in “Skin research and technology” A new AI model diagnoses hair and scalp disorders with 92% accuracy, better than previous models.
October 2023 in “Sinkron” The system can accurately classify hair diseases with 94.5% accuracy using a CNN.
3 citations,
January 2023 in “European Journal of Information Technologies and Computer Science” The machine learning model accurately detected hair loss and scalp diseases using processed images.
4 citations,
January 2021 in “Dermatologic Therapy” AI is effective in diagnosing and treating hair disorders, including detecting hair loss and scalp conditions with high accuracy, but it should supplement, not replace, doctor-patient interactions.
4 citations,
October 2022 in “Journal of Imaging” An intelligent system can classify hair follicles and measure hair loss severity with reasonable accuracy.
