Performance analysis of deep neural networks through transfer learning in retinal detachment diagnosis using fundus images

Yadav, Sonal; Das, Sanjoy; Murugan, R.; Roy, Sumantra Dutta; Agrawal, Monika; Goel, Tripti; Dutta, Anurag

doi:10.1007/s12046-022-01822-5

Cited by 10 publications

(4 citation statements)

References 31 publications

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“…The overall accuracy of the system was found to be 79.8%. Recently, Sonal Yadav et al [13] conducted a study aimed at diagnosing RD from non-RD images using various pre-trained CNN models and the TL technique on color fundus images. The study employed several well-known CNN models, including AlexNet, Inception-V3, GoogleNet, VGG-19, DenseNet, and ResNet-50.…”

Section: Related Workmentioning

confidence: 99%

“…The application of DL techniques in diverse imaging modalities, such as UWF images and Optos images, has greatly enhanced the precision and efficiency of diagnosing retinal diseases, with a particular focus on RD [10][11][12][13][14][15]. These studies have showcased significant advancements, leading to improved patient care.…”

Section: Related Workmentioning

confidence: 99%

“…Among the valuable imaging techniques in ophthalmology, optical coherence tomography (OCT) stands out for providing high-resolution cross-sectional images of the retina [9]. The integration of DL-driven CAD systems, along with OCT imaging capabilities, has the potential to revolutionize the diagnosis and management of RRD [10][11][12][13]. These systems automate the detection process and leverage the computational power of DL algorithms to accurately identify RRD and retinal layers evolution.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Postoperative Visual Acuity in Rhegmatogenous Retinal Detachment Using OCT Images

Hosni,

Khachnaoui,

Zgolli

et al. 2023

IEEE Access

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Deep Learning (DL) methods, such as Convolution Neural Networks (CNNs), have shown great potential in diagnosing complex diseases. Among these diseases, Rhegmatogenous Retinal Detachment (RRD) stands out as a critical condition necessitating precise diagnosis and postoperative Visual Acuity (VA) prediction. This research introduces a DL-based Computer-Aided Diagnosis (CAD) system that utilizes Optical Coherence Tomography (OCT) images for both the diagnosis of RRD and the prediction of postoperative VA. The CAD system utilizes DL techniques and a diverse dataset, including OCT images of patients with RRD from the Hedi Raies Ophthalmology Institute of Tunis and a large public dataset of normal subjects OCT. Preprocessing steps, such as image cropping, enhancement, denoising, and resizing, are applied to the tomographic images. Data oversampling and augmentation techniques address class imbalance and improve the dataset by generating additional samples. Various DL models, including pre-trained CNN models (VGG-16, Inception-V3, Inception-ResNet-V2), Bilinear (BCNN) (BCNN (VGG − 16) 2 and BCNN (Inception − V3) 2 ), and a custom CNN architecture, are implemented for RRD diagnosis and postoperative VA prediction. The experimental outcomes demonstrate the effectiveness of the proposed CAD system in accurately diagnosing RRD and predicting postoperative VA. The system achieves high accuracy, with 99.87% for diagnosing RRD and 98.06% for predicting postoperative VA using the BCNN (VGG − 16) 2 model. The developed CAD system represents a significant advancement in the field of RRD and postoperative VA prediction. By combining DL and OCT imaging, the system provides automated and accurate diagnosis, showing potential in improving patient care and treatment decisions.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Postoperative Visual Acuity in Rhegmatogenous Retinal Detachment Using OCT Images

Hosni,

Khachnaoui,

Zgolli

et al. 2023

IEEE Access

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show abstract

“…In recent years, artificial intelligence (AI) models for RD detection based on color fundus photography (CFP) and optical coherence tomography (OCT) have been gradually established (11)(12)(13)(14). However, the emergence of the ultra-widefield fundus (UWF) imaging system promotes the intelligent diagnosis of fundus diseases to a new height.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-assisted management of retinal detachment from ultra-widefield fundus images based on weakly-supervised approach

Li,

Cao,

You

et al. 2024

Front. Med.

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BackgroundRetinal detachment (RD) is a common sight-threatening condition in the emergency department. Early postural intervention based on detachment regions can improve visual prognosis.MethodsWe developed a weakly supervised model with 24,208 ultra-widefield fundus images to localize and coarsely outline the anatomical RD regions. The customized preoperative postural guidance was generated for patients accordingly. The localization performance was then compared with the baseline model and an ophthalmologist according to the reference standard established by the retina experts.ResultsIn the 48-partition lesion detection, our proposed model reached an 86.42% (95% confidence interval (CI): 85.81–87.01%) precision and an 83.27% (95%CI: 82.62–83.90%) recall with an average precision (PA) of 0.9132. In contrast, the baseline model achieved a 92.67% (95%CI: 92.11–93.19%) precision and limited recall of 68.07% (95%CI: 67.25–68.88%). Our holistic lesion localization performance was comparable to the ophthalmologist’s 89.16% (95%CI: 88.75–89.55%) precision and 83.38% (95%CI: 82.91–83.84%) recall. As to the performance of four-zone anatomical localization, compared with the ground truth, the un-weighted Cohen’s κ coefficients were 0.710(95%CI: 0.659–0.761) and 0.753(95%CI: 0.702–0.804) for the weakly-supervised model and the general ophthalmologist, respectively.ConclusionThe proposed weakly-supervised deep learning model showed outstanding performance comparable to that of the general ophthalmologist in localizing and outlining the RD regions. Hopefully, it would greatly facilitate managing RD patients, especially for medical referral and patient education.

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Explaining Eye Diseases Detected by Machine Learning Using SHAP: A Case Study of Diabetic Retinopathy and Choroidal Nevus

Shakeri¹,

Crump²,

Weis³

et al. 2023

SN COMPUT. SCI.

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Performance analysis of deep neural networks through transfer learning in retinal detachment diagnosis using fundus images

Cited by 10 publications

References 31 publications

Prediction of Postoperative Visual Acuity in Rhegmatogenous Retinal Detachment Using OCT Images

Prediction of Postoperative Visual Acuity in Rhegmatogenous Retinal Detachment Using OCT Images

Artificial intelligence-assisted management of retinal detachment from ultra-widefield fundus images based on weakly-supervised approach

Explaining Eye Diseases Detected by Machine Learning Using SHAP: A Case Study of Diabetic Retinopathy and Choroidal Nevus

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