Deep learning-based efficient diagnosis of periapical diseases with dental X-rays

Image and Vision Computing, Год журнала: 2024, Номер 147, С. 105061 - 105061

Опубликована: Май 8, 2024

Язык: Английский

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Advances in retinal microaneurysms detection, segmentation and datasets for the diagnosis of diabetic retinopathy: a systematic literature review

Multimedia Tools and Applications, Год журнала: 2024, Номер 83(30), С. 74897 - 74935

Опубликована: Фев. 13, 2024

Язык: Английский

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A Cascade U‐Net With Transformer for Retinal Multi‐Lesion Segmentation

International Journal of Imaging Systems and Technology, Год журнала: 2024, Номер 34(5)

Опубликована: Сен. 1, 2024

ABSTRACT Diabetic retinopathy (DR) is an important cause of blindness. If not diagnosed and treated in a timely manner, it can lead to irreversible vision loss. The diagnosis DR relies heavily on specialized ophthalmologists. In recent years, with the development artificial intelligence number diagnostics using this technique have begun appear. One method for diagnosing diseases field segment four common kinds lesions from color fundus images, including: exudates (EX), soft (SE), hemorrhages (HE), microaneurysms (MA). paper, we propose segmentation model based deep learning. main part consists two layers improved U‐Net network transformer, corresponding stages coarse fine segmentation, respectively. input image at same time. To validate performance our proposed model, test three public datasets: IDRiD, DDR, DIARETDB1. results show that achieves competitive compared existing methods terms PR‐AUC, ROC‐AUC, Dice, IoU, especially SE MA.

Язык: Английский

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Diversified and Structure-Realistic Fundus Image Synthesis for Diabetic Retinopathy Lesion Segmentation

Lecture notes in computer science, Год журнала: 2024, Номер unknown, С. 77 - 86

Опубликована: Янв. 1, 2024

Язык: Английский

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