Machine Learning in Audiology: Applications and Implications

Charih, François

doi:10.22215/etd/2019-13494

Cited by 2 publications

(1 citation statement)

References 59 publications

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“…SVM is a supervised machine learning classifier [36] with the maximum margin between the given classes to have minimum classification error [37]. It classifies two classes by identifying the optimal hyperplane that marks the decision boundaries [38].…”

Section: ) Svm Classifiermentioning

confidence: 99%

Automated Detection of Retinopathy of Prematurity Using Quantum Machine Learning and Deep Learning Techniques

Sankari,

Umapathy,

Alasmari

et al. 2023

IEEE Access

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Retinopathy of prematurity (ROP) is a vasoproliferative retinal disease that affects premature infants and causes permanent blindness if left untreated. Automated retinal diagnosis from the Retinal fundus images aid in the early detection of many pathological conditions. The low-level statistical features used in literatures have not provided the complete ROP-specific profile, and hence it, has to be replaced by high-level features. The proposed system involves extracting Scale Invariant Feature Transform (SIFT) -Speeded Up Robust Features (SURF) combined highlevel features from the SegNet segmented retinal vessels and classified using the Quantum Support Vector Machine (QSVM) classifier. This study aims (i) to segment retinal vessels from the acquired fundus images using SegNet and extract their features using the SURF and SIFT Feature Extraction method, (ii) to classify the Normal and ROP retinal vessels using four classical machine learning classifiers such as Support Vector Machine (SVM), Reduced Error Pruning (REP) tree, K-Star, and LogitBoost and Quantum SVM classifier. (iii) to develop a novel transformer-based Swin-T ROP model to classify ROP from normal Neonatal fundus images. (iv) to compare the performance characteristics of the proposed QSVM model with the Resnet50, DarkNet19 and classical machine learning classifiers. The study is conducted using 200 fundus images, including 100 normal and 100 ROP-positive neonatal retinal images. The machine learning classifiers such as SVM, REP Tree, K-Star, and Logit Boost Classifiers attain 86.7%, 75%, 74%, and 76.5% accuracy in classifying ROP from normal retinal images. The deep learning networks such as ResNet50 and DarkNet19 classified ROP from normal fundus images with an accuracy of 92.87% and 89%, respectively. The Quantum machine learning classifier outperforms the classical machine learning classifiers, Pretrained Convolutional Neural Networks (CNN) and SwinT-ROP in terms of classification accuracy (95.5%), sensitivity (93%), and specificity (98%). The proposed system accurately diagnoses ROP from the neonatal fundus images and could be used in point-of-care diagnosis to access diagnostic expertise in underserved regions. INDEX TERMS QSVM, Transformer, transfer learning, Quantum classifier, Retinal Image processing I. INTRODUCTION I MAGING the microcirculation of the retinal layer allows the diagnosis of ocular diseases and the wellness of the entire circulatory system and brain [1]. The retinal fundus images aid in diagnosing many pathological conditions such as Diabetic Retinopathy (DR), Retinopathy of Prematurity (ROP), Glaucoma, Macular Edema, etc., which could cause partial to complete blindness. The prevalence of visual impairment due to ROP is estimated to be 10% worldwide [2]. About 490000 preterm neonates survive in India annually, of which at least 5000 neonates are diagnosed with severe VOLUME XX, XXXX

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Section: ) Svm Classifiermentioning

confidence: 99%