Performance Analysis of Optimizers for Glaucoma Diagnosis from Fundus Images Using Transfer Learning

Elangovan, Poonguzhali; Nath, Malaya Kumar

doi:10.1007/978-981-16-0289-4_37

Cited by 8 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this work, the performance of each base learner is investigated in five different configurations. With batch size of 32 and maximum epochs of 20, the parameters of customized base learners are updated using Adam optimizer 41 with an initial learning rate of 0.0001. Each configuration's performance is evaluated using metrics like accuracy (ACC), sensitivity (SN), specificity (SP), and precision (PR) 5 .…”

Section: Resultsmentioning

confidence: 99%

En‐ConvNet: A novel approach for glaucoma detection from color fundus images using ensemble of deep convolutional neural networks

Elangovan¹,

Nath²

2022

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Glaucomatous optic neuropathy is the preeminent cause of incurable vision impairment and blindness across the world. Manual interpretation of the pathological structures in fundus images is time‐consuming and requires the expertise of a competent specialist. With the development of deep learning approaches, automated glaucoma diagnosis is easy and effective for larger screening. Convolutional neural networks, in particular, have emerged as a promising choice for glaucoma detection from fundus images due to their remarkable success in image classification. Transferring the optimized weights from a pre‐trained model expedites and simplifies the training process of deep neural network. In this paper, a deep ensemble model using the stacking ensemble learning technique is developed to attain the optimum performance for the classification of glaucomatous and normal images. Thirteen pre‐trained models such as Alexnet, Googlenet, VGG‐16, VGG‐19, Squeezenet, Resnet‐18, Resnet‐50, Resnet‐101, Efficientnet‐b0, Mobilenet‐v2, Densenet‐201, Inception‐v3, and Xception are implemented. Their performance is compared in 65 different configurations, comprising 13 CNN architectures and five various classification approaches. A two‐stage ensemble selection technique is proposed to select the optimal configurations. Selected configurations are pooled using a probability averaging technique. The final classification is performed using an SVM classifier. In this work, publicly available databases are modified (such as: DRISHTI‐GS1‐R, ORIGA‐R, RIM‐ONE2‐R, LAG‐R, and ACRIMA‐R) based on oversampling data‐level technique for validating the performance of deep ensemble model. Ensembling the best configurations reports an overall classification accuracy of 93.4%, 79.6%, 91.3%, 99.5%, and 99.6% in DRISHTI‐GS1‐R, ORIGA‐R, RIM‐ONE2‐R, ACRIMA‐R, and LAG‐R databases, respectively.

show abstract

Section: Resultsmentioning

confidence: 99%

En‐ConvNet: A novel approach for glaucoma detection from color fundus images using ensemble of deep convolutional neural networks

Elangovan¹,

Nath²

2022

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…Thavasimani and Srinath 49 experimented with various optimizers such as ADAM, SGD, RMS prop, Ada delta, Ada max, Ada grad, Nadam to detect bot accounts with the help of deep learning model from CRESCI-2017 twitter dataset issued by Indiana University and establishes the highest accuracy of 98.90% with RMS prop. Elangovan and Nath 50 bring out the role of optimizer in improving the performance of deep neural network for image classification problem and analyzes three standard first-order optimizers like stochastic gradient descent with momentum (SGDM), adaptive moment estimation (Adam), and root mean square propagation (RMS Prop) for detecting glaucoma employing architectures like Alex net,VGG-19 and Resnet 101. Adam optimizer shows better result in this.…”

Section: Related Workmentioning

confidence: 99%

A novel deep learning technique for medical image analysis using improved optimizer

Agarwal,

Lohani,

Bist

2024

Health Informatics J

View full text Add to dashboard Cite

Application of Convolutional neural network in spectrum of Medical image analysis are providing benchmark outputs which converges the interest of many researchers to explore it in depth. Latest preprocessing technique Real ESRGAN (Enhanced super resolution generative adversarial network) and GFPGAN (Generative facial prior GAN) are proving their efficacy in providing high resolution dataset. Objective: Optimizer plays a vital role in upgrading the functioning of CNN model. Different optimizers like Gradient descent, Stochastic Gradient descent, Adagrad, Adadelta and Adam etc. are used for classification and segmentation of Medical image but they suffer from slow processing due to their large memory requirement. Stochastic Gradient descent suffers from high variance and is computationally expensive. Dead neuron problem also proves to detrimental to the performance of most of the optimizers. A new optimization technique Gradient Centralization is providing the unparalleled result in terms of generalization and execution time. Method: Our paper explores the next factor which is the employment of new optimization technique, Gradient centralization (GC) to our integrated framework (Model with advanced preprocessing technique). Result and conclusion: Integrated Framework of Real ESRGAN and GFPGAN with Gradient centralization provides an optimal solution for deep learning models in terms of Execution time and Loss factor improvement.

show abstract

“…It is optional to train the network with random initial weights, thus reducing the computational cost of training the model. The authors [13], [14], [15], [16], [17] and [18] used pre-trained models to identify glaucoma.…”

Section: Related Workmentioning

confidence: 99%

“…Elangovan et al [17] used a CNN architecture to extract image information and compared classification traditional methods with the network using Softmax. Another work that also compared different classification methods was from Singh et al [19], in which they proposed a multimodalitybased approach for the detection of glaucoma.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Comparative Analysis of Glaucoma Feature Extraction and Classification Techniques in Fundus Images

Assis

Cortez

2023

JCIS

View full text Add to dashboard Cite

Glaucoma is an asymptomatic chronic eye disease that, if not treated in the early stages, can lead to blindness. Therefore, detection in the early stages is essential to preserve the patient’s quality of life. Thus, it is crucial to have a noninvasive method capable of detecting this disease through images in the fundus examination. In the literature, datasets are available with fundus images; however, only a few have glaucoma images and labels. Learning from an imbalanced dataset challenges machine learning, which limits supervised learning algorithms. We compared approaches to extract and classify three public datasets with 2390 images: ACRIMA, REFUGE, and RIM-ONE DL. First, we evaluated extracted features non-structural from HOG, LBP, Zernike, and Gabor filters and features obtained from transfer learning. Then, we classified them with Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB). Finally, each classifier was evaluated individually and in a voting classifier (VOT). We extracted and classified features from transfer learning models in the same process. Also, they were classified using traditional machine learning. Due to class imbalance, we undersampled the majority class normal by applying the following methods: random choice, near miss, and cluster centroid. We also evaluated our model using a cross-dataset approach. Therefore, we efficiently identified glaucoma in different fundus images using network VGG19 and a voting classifier. In addition, balancing classes reduced false negatives and improved model quality. Our approach achieved an average F1-score equals to 94.69%, accuracy rate of 94.77%, precision of 96.10%, recall of 93.45%, and specificity of 96.08%.

show abstract

Performance Analysis of Optimizers for Glaucoma Diagnosis from Fundus Images Using Transfer Learning

Cited by 8 publications

References 18 publications

En‐ConvNet: A novel approach for glaucoma detection from color fundus images using ensemble of deep convolutional neural networks

En‐ConvNet: A novel approach for glaucoma detection from color fundus images using ensemble of deep convolutional neural networks

A novel deep learning technique for medical image analysis using improved optimizer

A Comparative Analysis of Glaucoma Feature Extraction and Classification Techniques in Fundus Images

Contact Info

Product

Resources

About