ERBVS: Enhanced Retinal Blood Vessel Segmentation using Multiple Modalities and Attention Mechanisms with Adversarial Training and Ensemble Deep Learning Operations

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Июль 26, 2024

It would, therefore, require highly advanced prediction tools to enhance early diagnosis and preemptive mechanisms for all these burgeoning diseases. Fast correct disease pre-emption have huge potential changing clinical outcome ensuring timely effective interventions that reduce morbidity mortality. Current predictive models, instrumental as they are, been found faltering in precision, recall, accuracy, timeliness. Such delays inaccuracies often miss the therapeutic window or lead misguided decisions. In this work, we present a novel model aims quite dramatically improve process of segmentation classification. Our approach embeds Attention Mechanisms with Adversarial Training Ensemble Deep Learning Operations, together multimodal approach, which places it substantially higher across several metrics. This improves AUC by 8.5%, 8.3%, 4.9%, 3.9%, respectively, classification, while reducing classification delay 5.9% different situations. Not only does our handle intrinsic limitations current methods, but also shows flexibility wide range applications. The compelling improvements preemption metrics strengthen its make sea change framework establishing optimum patient outcomes efficient scenarios healthcare delivery.

Язык: Английский

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The ideal PGGAN for the 3D medical data Segmenting

Опубликована: Июль 11, 2024

Язык: Английский

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Just Noticeable Difference-Guided Multi-Path Deep Attention Network for Microaneurysm Segmentation in Fundus Images

IEEE Transactions on Instrumentation and Measurement, Год журнала: 2024, Номер 73, С. 1 - 12

Опубликована: Янв. 1, 2024

Язык: Английский

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Customized Mechanism for Diabetic Risk Prediction: A Hybrid CNN–Autoencoder Approach with Emphasis on Retinal Imaging in the Elderly

Deleted Journal, Год журнала: 2024, Номер 20(1s), С. 190 - 199

Опубликована: Март 28, 2024

Diabetes Mellitus presents a substantial health obstacle on global scale, with particular impact the elderly demographic. Prompt identification is vital for efficient control and avoidance of complications. This study introduces new Hybrid Convolutional Neural Network (CNN) Autoencoder model specifically developed accurately predicting risk diabetes at an early stage. The designed to analyze retinal images in older individuals. introduction this paper comprehensive analysis increasing incidence population underscores significance identification. Conventional approaches frequently encounter constraints terms precision specificity, which has led investigation sophisticated machine learning models. CNN–Autoencoder combines advantageous characteristics both architectures, utilizing CNN proficiency extracting spatial features Autoencoder's capability unsupervised feature learning. approach we use consists training validating using dataset from attains remarkable accuracy 90.92%, outperforming typical deep models employed diabetic risk. experimental results demonstrate superior performance accuracy, sensitivity, specificity. Comparative shows that it highly effective identifying subtle patterns indicate signs diabetes, surpassing traditional other modern methods. research findings presented make valuable contribution expanding knowledge base detection, within population. proven suggested highlights its capacity as dependable tailored instrument forecasting, thus enabling prompt interventions individualized healthcare strategies individuals susceptible diabetes.

Язык: Английский

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Eye Disease Prediction Using Deep Learning and Attention on Oct Scans

SN Computer Science, Год журнала: 2024, Номер 5(8)

Опубликована: Ноя. 20, 2024

Язык: Английский

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