Automatic Detection of Diabetic Retinopathy from Retinal Fundus Images Using MobileNet Model
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-2053-8_23
Reference26 articles.
1. Khojasteh P, Aliahmad B, Kumar DK (2018) (2018) Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms. BMC Ophthalmol 18(288):1–13. https://doi.org/10.1186/s12886-018-0954-4
2. Albahli S, Nabi Ahmad Hassan Yar G (2022) Detection of diabetic retinopathy using custom CNN to segment the lesions. Intell Autom Soft Comput 33(2):837–853. https://doi.org/10.32604/iasc.2022.024427
3. Mushtaq G, Siddiqui F (2021) Detection of diabetic retinopathy using deep learning methodology. IOP Conf Ser Mater Sci Eng 1070:012049. https://doi.org/10.1088/1757-899X/1070/1/012049
4. Das S, Majumder S (2022) Overview and analysis of present-day diabetic retinopathy (DR) detection techniques: approaches and applications of deep learning in virtual medical care. IGI Global 12:52–80. https://doi.org/10.4018/978-1-7998-8929-8.ch003
5. Mateen M, Wen J, Nasrullah N, Sun S, Hayat S (2020) Exudate detection for diabetic retinopathy using pretrained convolutional neural networks. Complexity 2020:1–11. https://doi.org/10.1155/2020/5801870
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