Machine-Learning-Scheme to Detect Choroidal-Neovascularization in Retinal OCT Image

Rajinikanth, V.; Kadry, Seifedine; Damaševičius, Robertas; Taniar, David; Rauf, Hafiz Tayyab

doi:10.1109/icbsii51839.2021.9445134

Cited by 26 publications

(14 citation statements)

References 26 publications

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“…Technological advancements are increasingly improving medical diagnostics. Available electronic aids have focused on improving diagnostics by automating image analysis [ 9 , 10 , 11 , 12 ]. However, most of these AI-assisted technologies are designed to identify a single disease, in contrast to our algorithm that differentiates between diagnoses [ 19 ].…”

Section: Discussionmentioning

confidence: 99%

“…The field of medical diagnostics is being transformed by electronic aids that utilize machine learning and artificial intelligence. Within the field of ophthalmology, these tools have made it possible to automate the detection of diabetic retinopathy and glaucoma from retinal fundus imaging and the detection of choroidal neovascularization from Ocular Coherence Tomography (OCT) images [ 9 , 10 , 11 ]. These valuable electronic aids have the potential to improve triaging for patients who have already been referred to specialists [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the Diagnostic Accuracy of a Novel Bayesian Decision-Making Algorithm for Vision Loss

Basilious

Govas

Deans

et al. 2022

Vision

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The current diagnostic aids for acute vision loss are static flowcharts that do not provide dynamic, stepwise workups. We tested the diagnostic accuracy of a novel dynamic Bayesian algorithm for acute vision loss. Seventy-nine “participants” with acute vision loss in Windsor, Canada were assessed by an emergency medicine or primary care provider who completed a questionnaire about ocular symptoms/findings (without requiring fundoscopy). An ophthalmologist then attributed an independent “gold-standard diagnosis”. The algorithm employed questionnaire data to produce a differential diagnosis. The referrer diagnostic accuracy was 30.4%, while the algorithm’s accuracy was 70.9%, increasing to 86.1% with the algorithm’s top two diagnoses included and 88.6% with the top three included. In urgent cases of vision loss (n = 54), the referrer diagnostic accuracy was 38.9%, while the algorithm’s top diagnosis was correct in 72.2% of cases, increasing to 85.2% (top two included) and 87.0% (top three included). The algorithm’s sensitivity for urgent cases using the top diagnosis was 94.4% (95% CI: 85–99%), with a specificity of 76.0% (95% CI: 55–91%). This novel algorithm adjusts its workup at each step using clinical symptoms. In doing so, it successfully improves diagnostic accuracy for vision loss using clinical data collected by non-ophthalmologists.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating the Diagnostic Accuracy of a Novel Bayesian Decision-Making Algorithm for Vision Loss

Basilious

Govas

Deans

et al. 2022

Vision

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“…Features are manually extracted in machine learning techniques ( 9 , 10 ) whereas features are automatically extracted in deep learning techniques ( 11 , 12 ). Tuncer et al.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of the Deep Learning Based Convolutional Neural Network Approach for the Recognition of Chest X-Ray Images

Sharma

Gupta

et al. 2022

Front. Oncol.

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Recent advancement in the field of deep learning has provided promising performance for the analysis of medical images. Every year, pneumonia is the leading cause for death of various children under the age of 5 years. Chest X-rays are the first technique that is used for the detection of pneumonia. Various deep learning and computer vision techniques can be used to determine the virus which causes pneumonia using Chest X-ray images. These days, it is possible to use Convolutional Neural Networks (CNN) for the classification and analysis of images due to the availability of a large number of datasets. In this work, a CNN model is implemented for the recognition of Chest X-ray images for the detection of Pneumonia. The model is trained on a publicly available Chest X-ray images dataset having two classes: Normal chest X-ray images and Pneumonic Chest X-ray images, where each class has 5000 Samples. 80% of the collected data is used for the purpose to train the model, and the rest for testing the model. The model is trained and validated using two optimizers: Adam and RMSprop. The maximum recognition accuracy of 98% is obtained on the validation dataset. The obtained results are further compared with the results obtained by other researchers for the recognition of biomedical images.

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“…Slime mould optimization algorithm was used for extracting blood vessel from digital fundus images for the recognition of retinal disease [22]. e mayfly optimization algorithm was used for the extraction of optimal features from optical-coherence-tomography eye images for further classification and recognition of the choroidal-neovascularization disease [23]. Firefly optimization algorithm was used for COVID-19 case recognition from chest CT images [24].…”

Section: Introductionmentioning

confidence: 99%

Lung X-Ray Image Segmentation Using Heuristic Red Fox Optimization Algorithm

Jaszcz

Połap

Damaševičius

2022

Scientific Programming

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Medical image segmentation identifies an area that should be analyzed later in the processing process, such as for disease recognition and classification. As the image search area is reduced, this action allows for faster computation and analysis. We propose the use of a heuristic red fox heuristic optimization algorithm (RFOA) for medical image segmentation in this paper. The heuristics’ operation was adapted to the analysis of two-dimensional images, with a focus on equation modification and the novel fitness function. The proposed solution analyzes the image by converting the selected pixels to one of two color variants, black or white, based on the threshold value used. Their number is counted, allowing analysis of the chosen threshold. As a result, such analysis results in the automatic selection of the segmentation threshold parameter. Our method propose a new fitness function and the adjustment of RFOA to image analysis. We used a publicly available database of lung X-ray images for evaluation, and based on the results, an accuracy analysis was performed, as well as a discussion of the benefits and drawbacks is presented.

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Machine-Learning-Scheme to Detect Choroidal-Neovascularization in Retinal OCT Image

Cited by 26 publications

References 26 publications

Evaluating the Diagnostic Accuracy of a Novel Bayesian Decision-Making Algorithm for Vision Loss

Evaluating the Diagnostic Accuracy of a Novel Bayesian Decision-Making Algorithm for Vision Loss

Performance Evaluation of the Deep Learning Based Convolutional Neural Network Approach for the Recognition of Chest X-Ray Images

Lung X-Ray Image Segmentation Using Heuristic Red Fox Optimization Algorithm

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