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Detection of Strabismus Using Convolutional Neural Network-Based Classification Models

Publication Type : Journal Article

Publisher : Springer Nature Singapore

Source : Lecture Notes in Networks and Systems

Url : https://doi.org/10.1007/978-981-97-7710-5_12

Campus : Amritapuri

School : School of Computing

Department : Computer Science and Applications

Year : 2024

Abstract : Strabismus, a prevalent eye misalignment disorder, can lead to vision impairment if not promptly identified and treated. Existing automatic detection approaches often rely on single-feature methodologies, such as deep features or ratio features, which may suffer from reliability issues. This paper introduces a pioneering multi-feature fusion model (MFFM) aimed at augmenting the accuracy of strabismus detection that integrates deep features and ratio features extracted from corneal light reflection photographs using convolutional neural network (CNN) based on classification models. Initially, deep features are extracted employing pre-trained CNN models renowned for its efficacy in diverse image recognition and classification of strabismus images. Through the fusion of these features, MFFM offers a robust framework to bolster the accuracy of strabismus detection. This research presents a promising avenue for advancing early diagnosis and treatment of strabismus, thereby potentially mitigating the risks of vision impairment.

Cite this Research Publication : S. Subbulakshmi, Aditya Mani, Divyam Gupta, Detection of Strabismus Using Convolutional Neural Network-Based Classification Models, Lecture Notes in Networks and Systems, Springer Nature Singapore, 2024, https://doi.org/10.1007/978-981-97-7710-5_12

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