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Fuzzy Image Processing and Deep Learning for Microaneurysms Detection

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Artificial Intelligence and Machine Learning for Digital Pathology

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12090))

Abstract

Diabetic retinopathy is an eye disease generated by long-standing diabetes, and it is one of the main causes of vision loss if not diagnosed and treated properly. Diabetic retinopathy consists of several types of lesions found in the retina of diabetic individuals. One of the important lesions of diabetic retinopathy is microaneurysms, which are small red dots that appear due to the local weakness of the capillary walls. This paper presents a novel automatic microaneurysms detection method, in retinal images by employing fuzzy image processing and deep learning. Firstly, the paper explores the existing systems of diabetic retinopathy screening, with a focus on the microaneurysms detection methods and deep learning classification. The proposed system consists of two parts, namely: image preprocessing with a combination of fuzzy image processing techniques, and also the microaneurysms classification using deep neural networks. This paper investigates the capability of a combination of different fuzzy image preprocessing techniques for the detection of microaneurysms in eye fundus images. In addition to the proposed microaneurysms detection system, the paper also highlights a novel dataset for the microaneurysms detection that includes the ground truth data. The purpose of the proposed automated microaneurysm detection with digital analysis of eye fundus images is to substitute current practice that is based on manual diagnosis and visual inspection, and eventually to contribute to producing a more reliable diabetic retinopathy screening system.

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Acknowledgements

This project is part of a postdoctoral research currently being carried out at the Faculty of Engineering, Environment and Computing, Coventry University, United Kingdom. The deepest gratitude and thanks go to the Universiti Teknikal Malaysia Melaka (UTeM) for sponsoring this postdoctoral research. The authors are thankful to the Ministry of Health Malaysia and the Melaka Hospital, Malaysia, for providing the database of retinal images and also for the manual grading done by the experts.

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Authors and Affiliations

  1. Faculty of Engineering, Environment and Computing, Coventry University, Priory Street, Coventry, CV1 5FB, UK

    Sarni Suhaila Rahim, Vasile Palade & Ibrahim Almakky

  2. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia

    Sarni Suhaila Rahim

  3. Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz, Austria

    Andreas Holzinger

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  1. Sarni Suhaila Rahim
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Correspondence to Sarni Suhaila Rahim .

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Editors and Affiliations

  1. Medical University of Graz, Graz, Austria

    Andreas Holzinger

  2. University of Alberta, Edmonton, AB, Canada

    Randy Goebel

  3. University of Alberta, Edmonton, AB, Canada

    Michael Mengel

  4. Medical University of Graz, Graz, Austria

    Heimo Müller

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Rahim, S.S., Palade, V., Almakky, I., Holzinger, A. (2020). Fuzzy Image Processing and Deep Learning for Microaneurysms Detection. In: Holzinger, A., Goebel, R., Mengel, M., Müller, H. (eds) Artificial Intelligence and Machine Learning for Digital Pathology. Lecture Notes in Computer Science(), vol 12090. Springer, Cham. https://doi.org/10.1007/978-3-030-50402-1_19

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  • DOI: https://doi.org/10.1007/978-3-030-50402-1_19

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