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2022 | OriginalPaper | Buchkapitel

A Lightweight Deep Learning Approach for Diabetic Retinopathy Classification

verfasst von : Ruchika Bala, Arun Sharma, Nidhi Goel

Erschienen in: Artificial Intelligence and Speech Technology

Verlag: Springer International Publishing

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Abstract

In the present time, chances of suffering from diabetes have drastically increased due to the genetic probability, lack of physical activities, high blood pressure and modern lifestyle related problems. Diabetic Retinopathy (DR) is an intense problem which affects blood vessels in the eye retina. Early detection of DR can avoid severe eye damage. Several machine-learning and deep-learning based techniques have been used for DR detection and classification. However, these techniques are complex, time consuming, and take millions of parameters in training and deploying the DR classifier. In this paper, a lightweight dual-branch based CNN architecture is proposed for DR classification. The proposed architecture involves 84,645 (0.084 M) parameters for training and deploying the model. APTOS dataset has been used for analysis.

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Vorheriges Kapitel Deep Neural Networks for Spoken Language Identification in Short Utterances

Nächstes Kapitel Machine Hearing a Cognitive Service for Aiding Clinical Diagnosis

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Titel: A Lightweight Deep Learning Approach for Diabetic Retinopathy Classification
verfasst von: Ruchika Bala
Arun Sharma
Nidhi Goel
Verlag: Springer International Publishing
Buch: Artificial Intelligence and Speech Technology
Print ISBN: 978-3-030-95710-0

Electronic ISBN: 978-3-030-95711-7

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-030-95711-7_25

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