An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization

Nawaz, Marriam; Nazir, Tahira; Javed, Ali; Tariq, Usman; Yong, Hwan-Seung; Khan, Muhammad Attique; Cha, Jaehyuk

doi:10.3390/s22020434

Cited by 85 publications

(42 citation statements)

References 46 publications

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“…Augmentation of datasets is important step and it can be refining through more up-to-date methods [ 54 , 55 ]. Moreover, the optimization through some latest techniques should be opted [ 56 , 57 ].…”

Section: Discussionmentioning

confidence: 99%

Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine

Afza

Sharif

Khan

et al. 2022

Sensors

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The variation in skin textures and injuries, as well as the detection and classification of skin cancer, is a difficult task. Manually detecting skin lesions from dermoscopy images is a difficult and time-consuming process. Recent advancements in the domains of the internet of things (IoT) and artificial intelligence for medical applications demonstrated improvements in both accuracy and computational time. In this paper, a new method for multiclass skin lesion classification using best deep learning feature fusion and an extreme learning machine is proposed. The proposed method includes five primary steps: image acquisition and contrast enhancement; deep learning feature extraction using transfer learning; best feature selection using hybrid whale optimization and entropy-mutual information (EMI) approach; fusion of selected features using a modified canonical correlation based approach; and, finally, extreme learning machine based classification. The feature selection step improves the system’s computational efficiency and accuracy. The experiment is carried out on two publicly available datasets, HAM10000 and ISIC2018. The achieved accuracy on both datasets is 93.40 and 94.36 percent. When compared to state-of-the-art (SOTA) techniques, the proposed method’s accuracy is improved. Furthermore, the proposed method is computationally efficient.

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Section: Discussionmentioning

confidence: 99%

Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine

Afza

Sharif

Khan

et al. 2022

Sensors

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“…In general, diagnostic accuracy using artificial intelligence for the detection of glaucoma from fundus photographs and optical coherence tomography images is worse in external datasets than in test sets from the original data source 4 . Others have reported better diagnostic accuracy on some of the external fundus photograph test sets used in the current study 6,7,[31][32][33][34] . However, these reports trained and tested the datasets, so the diagnostic accuracy is expected to be higher than when the fundus photographs are independent external test sets as in the current study.…”

Section: Discussionmentioning

confidence: 52%

Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions: Transformer for Improved Generalization

Fan¹,

Alipour²,

Bowd³

et al. 2022

Preprint

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Purpose: To compare the diagnostic accuracy and explainability of a new Vision Transformer deep learning technique, Data-efficient image Transformer (DeiT), and Resnet-50, trained on fundus photographs from the Ocular Hypertension Treatment Study (OHTS) to detect primary open-angle glaucoma (POAG) and to identify the salient areas of the photographs most important for each model's decision-making process. Study Design: Evaluation of a diagnostic technology Subjects, Participants, and/or Controls: 66,715 photographs from 1,636 OHTS participants and an additional five external datasets of 16137 photographs of healthy and glaucoma eyes. Methods, Intervention, or Testing: DeiT models were trained to detect five ground truth OHTS POAG classifications: OHTS Endpoint Committee POAG determinations due to disc changes (Model 1), visual field changes (Model 2), or either disc or visual field changes (Model 3) and reading center determinations based on disc (Model 4) and visual fields (Model 5). The best-performing DeiT models were compared to ResNet-50 on OHTS and five external datasets. Main Outcome Measures: Diagnostic performance was compared using areas under the receiver operating characteristic curve (AUROC) and sensitivities at fixed specificities. The explainability of the DeiT and ResNet-50 models was compared by evaluating the attention maps derived directly from DeiT to 3 gradient-weighted class activation map generation strategies. Results: Compared to our best-performing ResNet-50 models, the DeiT models demonstrated similar performance on the OHTS test sets for all five-ground truth POAG labels; AUROC ranged from 0.82 (Model 5) to 0.91 (Model 1). However, the AUROC of DeiT was consistently higher than ResNet-50 on the five external datasets. For example, AUROC for the main OHTS endpoint (Model 3) was between 0.08 and 0.20 higher in the DeiT compared to ResNet-50 models. The saliency maps from the DeiT highlight localized areas of the neuroretinal rim, suggesting the use of important clinical features for classification, while the same maps in the ResNet-50 models show a more diffuse, generalized distribution around the optic disc, Conclusions: Vision transformer has the potential to improve the generalizability and explainability of deep learning models for the detection of eye disease and possibly other medical conditions that rely on imaging modalities for clinical diagnosis and management.

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“…Recently, deep learning has shown improved performance for medical applications [32,33] such as breast cancer [34], retinopathy [35], COVID-19 [36], and many more [37]. The need to foresee the advancement to AD from MCI is consistently important to help treat this illness in its initial phase.…”

Section: Literature Reviewmentioning

confidence: 99%

A Long Short-Term Memory Biomarker-Based Prediction Framework for Alzheimer’s Disease

Aqeel

Hassan

Khan

et al. 2022

Sensors

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The early prediction of Alzheimer’s disease (AD) can be vital for the endurance of patients and establishes as an accommodating and facilitative factor for specialists. The proposed work presents a robotized predictive structure, dependent on machine learning (ML) methods for the forecast of AD. Neuropsychological measures (NM) and magnetic resonance imaging (MRI) biomarkers are deduced and passed on to a recurrent neural network (RNN). In the RNN, we have used long short-term memory (LSTM), and the proposed model will predict the biomarkers (feature vectors) of patients after 6, 12, 21 18, 24, and 36 months. These predicted biomarkers will go through fully connected neural network layers. The NN layers will then predict whether these RNN-predicted biomarkers belong to an AD patient or a patient with a mild cognitive impairment (MCI). The developed methodology has been tried on an openly available informational dataset (ADNI) and accomplished an accuracy of 88.24%, which is superior to the next-best available algorithms.

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An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization

Cited by 85 publications

References 46 publications

Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine

Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine

Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions: Transformer for Improved Generalization

A Long Short-Term Memory Biomarker-Based Prediction Framework for Alzheimer’s Disease

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