Towards Balancing the Complexity of Convolutional Neural Network with the Role of Optical Coherence Tomography in Retinal Conditions

Marginean, Anca; Groza, Adrian; Nicoară, Simona Delia; Muntean, George Adrian; Slavescu, Radu Razvan; Letia, Ioan Alfred

doi:10.1109/iccp48234.2019.8959714

Cited by 6 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Optical Coherence Tomography (OCT) in retinal images used VGG-16 and SqueezeNet [198]. The DNN models were trained on the OCT-2017 dataset [198].…”

Section: Diabetic Retinopathymentioning

confidence: 99%

“…Optical Coherence Tomography (OCT) in retinal images used VGG-16 and SqueezeNet [198]. The DNN models were trained on the OCT-2017 dataset [198]. To identify the significant areas of the image for the prediction, an occlusion mask was moved across the image recording the classification probabilities with the most contributing areas that resulted in a high drop [198].…”

Section: Diabetic Retinopathymentioning

confidence: 99%

“…The DNN models were trained on the OCT-2017 dataset [198]. To identify the significant areas of the image for the prediction, an occlusion mask was moved across the image recording the classification probabilities with the most contributing areas that resulted in a high drop [198]. A DNN model was proposed for classification of diabetes-related retinal diseases on Optical Coherence Tomography (OCT) volumes and the model was found to perform better on accuracy, sensitivity, and specificity compared to other similar models, without requiring manual annotations [199].…”

Section: Diabetic Retinopathymentioning

confidence: 99%

See 2 more Smart Citations

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Nazir¹,

Dickson²,

Akram³

2023

Computers in Biology and Medicine

View full text Add to dashboard Cite

“…Optical Coherence Tomography (OCT) in retinal images used VGG-16 and SqueezeNet [198]. The DNN models were trained on the OCT-2017 dataset [198].…”

Section: Diabetic Retinopathymentioning

confidence: 99%

Section: Diabetic Retinopathymentioning

confidence: 99%

Section: Diabetic Retinopathymentioning

confidence: 99%

See 1 more Smart Citation

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Nazir¹,

Dickson²,

Akram³

2023

Computers in Biology and Medicine

View full text Add to dashboard Cite

“…Neural networks, a sub-domain of machine learning, based on practical data and a brief description, build algorithms that allow solving problems and making predictions. This is achieved (Bishop, 2006;Marginean et al, 2019) by learning the characteristics and testing the algorithms on the sample data to allow the introduction of static equilibrium with a new set of input data. It is worth mentioning, Vochozka and Machova (2018) observed that artificial neural networks allow the construction of strong models for various economic issues.…”

Section: Business Performance Measurement and Prediction -Related Workmentioning

confidence: 99%

Composite Financial Performance Index Prediction – A Neural Networks Approach

Popa

Bogdan

et al. 2021

Journal of Business Economics and Management

View full text Add to dashboard Cite

Financial indicators are the most used variables in measuring the business performance of companies, signaling about the financial position, comprehensive income, and other significant reporting aspects. In a competitive environment, the performance measurement model allows performing comparative analysis in the same industry and between industries. This paper aims to design a composite financial index to determine the financial performance of listed companies, further used in predicting business performance through neural networks. Principal components analysis was used to build a composite financial index, employing four traditional accounting indicators and four value-based indicators for the period 2011–2018. Five experiments were conducted to predict business performance through the composite financial index. The results showed that observations from two years, of the first three experiments, indicate a better predictive behavior than the same experiments using observations from one year. Therefore, we concluded that observations from more than one year are necessary to predict the value of the financial performance index. Findings led us to the conclusion that recurrent neural networks model predicted better financial performance composite index when taken into consideration more real data for the financial performance index (2012–2018) instead of just for one year (2018).

show abstract

“…With the current surge of interest in Machine Learning (ML)-based medical software, explaining decisions made based on black-box ML models remains challenging [2]. For many domains [8,9], especially the medical one [17,23], there is need for algorithmic decisions should be sustained by explanations [16,24] and assurance cases. It has been argued that machine learning equals human-level capacity in medical diagnosis [15], while achieving high performance metrics has indeed become common practice [25].…”

Section: Introductionmentioning

confidence: 99%

Agents that Argue and Explain Classifications of Retinal Conditions

et al. 2021

Self Cite

View full text Add to dashboard Cite

Expertise for auditing AI systems in medical domain is only now being accumulated. Conformity assessment procedures will require AI systems: 1) to be transparent, 2) not to rely decisions solely on algorithms, or iii) to include safety assurance cases in the documentation to facilitate technical audit. We are interested here in obtaining transparency in the case of machine learning (ML) applied to classification of retina conditions. High performance metrics achieved using ML has become common practice. However, in the medical domain, algorithmic decisions need to be sustained by explanations. We aim at building a support tool for ophthalmologists able to: i) explain algorithmic decision to the human agent by automatically extracting rules from the ML learned models; ii) include the ophthalmologist in the loop by formalising expert rules and including the expert knowledge in the argumentation machinery; iii) build safety cases by creating assurance argument patterns for each diagnosis. Methods: For the learning task, we used a dataset consisting of 699 OCT images: 126 Normal class, 210 with Diabetic Retinopathy (DR) and 363 with Age Related Macular Degeneration (AMD). The dataset contains patients from the Ophthalmology Department of the County Emergency Hospital of Cluj-Napoca. All ethical norms and procedures, including anonymisation, have been performed. We applied three machine learning algorithms: decision tree A.

show abstract

Towards Balancing the Complexity of Convolutional Neural Network with the Role of Optical Coherence Tomography in Retinal Conditions

Cited by 6 publications

References 17 publications

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Composite Financial Performance Index Prediction – A Neural Networks Approach

Agents that Argue and Explain Classifications of Retinal Conditions

Contact Info

Product

Resources

About