Sağlık Hizmetlerinde Yapay Zeka: Temel Kavramlar ve Sınıflandırmalar

Опубликована: Янв. 7, 2025

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Early diagnosis of diabetic retinopathy using retinal network

Multimedia Tools and Applications, Год журнала: 2025, Номер unknown

Опубликована: Фев. 14, 2025

Язык: Английский

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An Improved Robust Fuzzy Local Information K-Means Clustering Algorithm for Diabetic Retinopathy Detection

IEEE Access, Год журнала: 2024, Номер 12, С. 78611 - 78623

Опубликована: Янв. 1, 2024

According to the International Diabetes Federation (IDF), roughly 33% of individuals affected by diabetes exhibit diagnoses encompassing diverse severity diabetic retinopathy. In year 2020, approximately 463 million adults within age bracket 20 79 were documented as sufferers on a global scale. Projections suggest rise 700 2045. Proposed automated retinopathy detection methods aim reduce ophthalmologist workload. The study presents Robust Fuzzy Local Information K-Means Clustering algorithm, an advanced iteration classical K-means clustering approach, integrating localized information parameters tailored individual clusters. Comparative analysis is conducted between performance and Modified C Means clustering, latter which incorporates median adjustment parameter augment for detection. results are evaluated three different datasets: IDRiD, Kaggle, fundus images collected from Shiva Netralaya Center, India. Achieving 94.4% accuracy rate average execution time 17.11 seconds, proposed algorithm aims adeptly categorize substantial volume retinal images, thereby improving meeting crucial demand prompt precise in healthcare.

Язык: Английский

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Revolutionizing diabetic retinopathy diagnosis through advanced deep learning techniques: Harnessing the power of GAN model with transfer learning and the DiaGAN-CNN model

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 99, С. 106790 - 106790

Опубликована: Сен. 12, 2024

Язык: Английский

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Exploring Machine Learning Models for Predicting Diabetic Retinopathy: A Comprehensive Comparative Study of Logistic Regression an Advanced Technique

International Journal of Innovative Science and Research Technology (IJISRT), Год журнала: 2024, Номер unknown, С. 1991 - 2004

Опубликована: Авг. 7, 2024

This research provides a comprehensive examination of machine learning models for predicting diabetes-related ocular diseases, with focus on Logistic Regression versus more advanced approaches. A large dataset encompassing variety lifestyle and health factors is used in the study to extensively train analyze multiple order demonstrate their predictive utility. The thorough evaluation results illuminated subtle differences performance between other algorithms, offering insightful information about pros cons each terms risk diabetic retinopathy complications relating eyes. findings reveal crucial themes additional advancement realm modeling eye disorders, process verifying that logistic regression works well specific situations.

Язык: Английский

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Deep neural network model for diagnosing diabetic retinopathy detection: An efficient mechanism for diabetic management

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 100, С. 107035 - 107035

Опубликована: Окт. 28, 2024

Язык: Английский

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Advancements in Deep Learning for Automated Diagnosis of Ophthalmic Diseases: A Comprehensive Review

IEEE Access, Год журнала: 2024, Номер 12, С. 171221 - 171240

Опубликована: Янв. 1, 2024

Язык: Английский

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Recent Development and Application in Deep Learning for Diabetic Retinopathy Image Classification

Deleted Journal, Год журнала: 2024, Номер 6(07), С. 2398 - 2407

Опубликована: Июль 26, 2024

The Diabetic Retinopathy Poses a significant risk of vision loss if not detected early. Deep learning has made Substantial strides in classifying images, enhancing screening accuracy and efficiency paper review the current advancement application deep for image classification. Convolutional Neural Network, transfer have demonstrated notable improvement identifying stages. This emphasizes importance collaborative efforts innovative technologies creating robust, interpretable clinically relevant solution early detection management Retinopathy. By harnessing these advanced techniques, health care providers can better manage increasing burden Retinopathy, ultimately patient reducing Loss

Язык: Английский

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Tuberculosis detection bars on VGG19 transfer learning and Zebra Optimization Algorithm

EAI Endorsed Transactions on Pervasive Health and Technology, Год журнала: 2024, Номер 10

Опубликована: Авг. 22, 2024

Tuberculosis (TB) remains a significant global health challenge, necessitating accurate and efficient diagnostic tools. This study introduces novel approach combining VGG19, deep convolutional neural network model, with newly developed Zebra Optimization Algorithm (ZOA) to enhance the accuracy of TB detection from chest X-ray images. The Algorithm, inspired by social behavior zebras, was applied optimize hyperparameters VGG19 aiming improve model's generalizability performance. Our method evaluated using well-defined metric system that included accuracy, sensitivity, specificity. Results indicate combination ZOA significantly outperforms traditional methods, achieving high rate, which underscores potential hybrid approaches in image analysis.

Язык: Английский

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Application of Artificial Intelligence for Classification, Segmentation, Early Detection, Early Diagnosis, and Grading of Diabetic Retinopathy from Fundus Retinal Images: A Comprehensive Review

IEEE Access, Год журнала: 2024, Номер 12, С. 172499 - 172536

Опубликована: Янв. 1, 2024

Язык: Английский

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