Acousto-Prosodic Delineation and Classification of Speech Disfluencies in Bilingual Children

Pravin, Sheena Christabel; Palanivelan, M.

doi:10.1007/978-3-030-73689-7_59

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Performance metrics of efficient DenseNet model Table15, the proposed efficient DenseNet model performs well in unique feature extraction for accurate classification of the severity levels of DR, and it has enhanced the efficacy of DR screening. Moreover, the computational complexity[24][25][26][27][28][29] has been reduced compared with the baseline models. The metrics such as precision, recall, and F1 score are used to monitor the grading of DR by the efficient DenseNet model as depicted in Table16, along with the trainable parameters in Table17.…”

mentioning

confidence: 99%

An Efficient DenseNet for Diabetic Retinopathy Screening

Pravin

Sabapathy²,

Selvakumar³

et al. 2023

Int. j. eng. technol. innov.

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This study aims to propose a novel deep learning framework, i.e., efficient DenseNet, for identifying diabetic retinopathy severity levels in retinal images. Diabetic retinopathy is an eye condition that damages blood vessels in the retina. Detecting diabetic retinopathy at the early stage can avoid retinal detachment and effects leading to blindness in diabetic adults. A thin-layered efficient DenseNet model has been proposed with fewer training learnable parameters, leading to higher classification accuracy than the other deep learning models. The proposed deep learning framework for diabetic retinopathy severity level detection has an inbuilt automatic pre-processing module. Afterward, the efficient DenseNet model and classifier will provide data augmentation and higher-level feature extraction. The proposed efficient DenseNet framework is trained and tested using 13000 retinal fundus images within the diabetic retinopathy database and combined with the k-nearest neighbor classifier demonstrating the best classification accuracy of 98.40%.

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