An Innovative Deep Learning Framework for Skin Cancer Detection Employing ConvNeXtV2 and Focal Self-Attention Mechanisms

Results in Engineering, Год журнала: 2024, Номер unknown, С. 103692 - 103692

Опубликована: Дек. 1, 2024

Язык: Английский

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HViTML: Hybrid vision transformer with machine learning-based classification model for glaucomatous eye

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

Опубликована: Янв. 10, 2025

Язык: Английский

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A deep retinal vision network for glaucoma classification

Deleted Journal, Год журнала: 2025, Номер 7(3)

Опубликована: Фев. 25, 2025

Язык: Английский

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Fundus Image Classification via an Integrated Deep Learning Model and Random Forest for Glaucoma Diagnostics

Lecture notes in networks and systems, Год журнала: 2025, Номер unknown, С. 339 - 352

Опубликована: Янв. 1, 2025

Язык: Английский

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Deep transfer learning driven model for mango leaf disease detection

International Journal of Systems Assurance Engineering and Management, Год журнала: 2024, Номер unknown

Опубликована: Сен. 19, 2024

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Lightweight vision image transformer (LViT) model for skin cancer disease classification

International Journal of Systems Assurance Engineering and Management, Год журнала: 2024, Номер unknown

Опубликована: Сен. 20, 2024

Язык: Английский

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Optic Disc Segmentation in Human Retina Images Using a Meta Heuristic Optimization Method and Disease Diagnosis with Deep Learning

Applied Sciences, Год журнала: 2024, Номер 14(12), С. 5103 - 5103

Опубликована: Июнь 12, 2024

Glaucoma is a common eye disease that damages the optic nerve and leads to loss of vision. The shows few symptoms in early stages, making its identification complex task. To overcome challenges associated with this task, study aimed tackle localization segmentation disc, as well classification glaucoma. For disc segmentation, we propose novel metaheuristic approach called Grey Wolf Optimization (GWO). Two different approaches are used for glaucoma classification: one-stage approach, which whole image without cropping classification, two-stage approach. In region detected using You Only Look Once (YOLO) detection algorithm. interest (ROI) identified, performed pre-trained convolutional neural networks (CNNs) vision transformation techniques. addition, both applied combination CNN Random Forest GWO achieved an average sensitivity 96.04%, specificity 99.58%, accuracy 99.39%, DICE coefficient 94.15%, Jaccard index 90.4% on Drishti-GS dataset. proposed method remarkable results high-test 100% 88.18% hold-out validation three-fold cross-validation dataset, 96.15% 93.84% ORIGA five-fold cross-validation, respectively. Comparing previous studies, model outperforms them. use Swin transformer effectiveness classifying subsets data.

Язык: Английский

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Intrusion Detection for Internet of Vehicles using Machine Learning

2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Год журнала: 2023, Номер unknown, С. 1 - 6

Опубликована: Июль 6, 2023

The Internet of Vehicles (IoV) has replaced vehicular networks as the preferred paradigm a result enormous expansion in computer and network capabilities. Because dynamic IoV's diverse nature necessitates effective resource management, which calls for cutting-edge technologies like Software Defined Networking (SDN), Machine Learning (ML), others. In Defined-IoV (SD-IoV) networks, Road Side Units (RSUs) are charge effectiveness provide number safety features. However, it is not practical to deploy enough RSUs, current RSU placement does complete coverage an area. Furthermore, any lapse security or performance negative influence on driving. Thus, objective this study increase IoV by using different types learning Algorithm efficiency. As result, suggested use XG-BOOST method decrease communication time while expanding among devices. Along with method, paper works CAN-OITDS Dataset. comparative conventional ML algorithms shows that IDS detects malicious attack help XGBOOST high accuracy 96.04%.

Язык: Английский

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Vision transformer for detecting ocular diseases

AIP conference proceedings, Год журнала: 2024, Номер 3232, С. 040035 - 040035

Опубликована: Янв. 1, 2024

Язык: Английский

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A Systematic Literature Review: Leveraging Vision Transformers for Glaucoma Diagnosis

Опубликована: Авг. 29, 2024

Язык: Английский

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