Detection of glaucoma using three-stage training with EfficientNet

Zarzà, I. de; Curtò, J. de; Calafate, Carlos T.

doi:10.1016/j.iswa.2022.200140

Cited by 13 publications

(8 citation statements)

References 18 publications

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“…The approach employs a combination of compound scaling, model fusion, and progressive learning techniques to enhance performance while minimizing computational resources. The designation "B2" denotes a distinct variant or arrangement of the EfficientNetV2 concept [ 60 ].…”

Section: Background Knowledgementioning

confidence: 99%

Enhanced PRIM recognition using PRI sound and deep learning techniques

Hasani Azhdari,

Mahmoodzadeh,

Khishe

et al. 2024

PLoS ONE

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Pulse repetition interval modulation (PRIM) is integral to radar identification in modern electronic support measure (ESM) and electronic intelligence (ELINT) systems. Various distortions, including missing pulses, spurious pulses, unintended jitters, and noise from radar antenna scans, often hinder the accurate recognition of PRIM. This research introduces a novel three-stage approach for PRIM recognition, emphasizing the innovative use of PRI sound. A transfer learning-aided deep convolutional neural network (DCNN) is initially used for feature extraction. This is followed by an extreme learning machine (ELM) for real-time PRIM classification. Finally, a gray wolf optimizer (GWO) refines the network’s robustness. To evaluate the proposed method, we develop a real experimental dataset consisting of sound of six common PRI patterns. We utilized eight pre-trained DCNN architectures for evaluation, with VGG16 and ResNet50V2 notably achieving recognition accuracies of 97.53% and 96.92%. Integrating ELM and GWO further optimized the accuracy rates to 98.80% and 97.58. This research advances radar identification by offering an enhanced method for PRIM recognition, emphasizing the potential of PRI sound to address real-world distortions in ESM and ELINT systems.

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Section: Background Knowledgementioning

confidence: 99%

Enhanced PRIM recognition using PRI sound and deep learning techniques

Hasani Azhdari,

Mahmoodzadeh,

Khishe

et al. 2024

PLoS ONE

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“…More precise and reliable glaucoma screening predictions may be made through the usage of grouping and division organisations to carry out glaucoma screening activities. de Zarza` [43] has unveiled a sophisticated glaucoma detection system that operates automatically. "The method is built on a three-step process based on forms of EfficientNet, a proposed group of models discovered using NAS that achieves convincing accuracy on Imagenet, producing consistent results that outperform the benchmark techniques, and applying Move Gaining from Imagenet to the specific task at hand.…”

Section: Related Studymentioning

confidence: 99%

Glaucoma Detection Using Explainable AI and Deep Learning

Afreen,

Aluvalu

2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Glaucoma is an incurable eye syndrome and the second leading reason of vision loss. A retinal scan is usually used to detect it. Glaucoma poses a challenge to predict in its nascent stages because the side effects of glaucoma are not recognized until the advanced stages of the disease are reached. Therefore, regular eye examinations are important and recommended. Manual glaucoma screening methods are labour-intensive and time-consuming processes. However, deep learning-based glaucoma detection methods reduce the need for manual work and improve accuracy and speed. OBJECTIVES: conduct a literature analysis of latest technical publications using various AI, Machine learning, and Deep learning methodologies for automated glaucoma detection. RESULTS: There are 329 Scopus articles on glaucoma detection using retinal images. The quantitative review presented state-of-art methods from different research publications and articles and the usage of a fundus image database for qualitative and quantitative analysis. This paper presents the execution of Explainable AI for Glaucoma prediction Analysis. Explainable AI (XAI) is artificial intelligence (AI) that allows humans to understand AI decisions and predictions. This contrasts with the machine learning “black box” concept, where even the designer cannot explain why the AI made certain decisions. XAI is committed to improving user performance. To provide reliable explanations for Glaucoma forecasting from unhealthy and diseased photos, XAI primarily employs an Adaptive Neuro-fuzzy Inference System (ANFIS). CONCLUSION: This article proposes and compares the performance metrics of ANFIS & SNN fuzzy layers, VGG19, AlexNet, ResNet, and MobileNet.

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“…In the case of detecting jellyfish on the surface of water or captured by drones, the small resolution of jellyfish versus the big scene of sea background, the ambient noise, and the lightning reflection will be different problems that need to be considered. Our comprehensive system is deployed into the application that can be easily customized into other categories, i.e., fishes, marine species, and marine waste [50][51][52]. Our work specializes in underwater imaging which can be extended to UAV-capturing images for jellyfish distribution (or jellyfish bloom) detection [53].…”

Section: Figure 11mentioning

confidence: 99%

Improved YOLOv5 Based Deep Learning System for Jellyfish Detection

Pham,

Nguyen,

Kwon

et al. 2024

IEEE Access

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Massive jellyfish outbreaks have put human lives and marine ecosystems in great danger. As a result, the jellyfish detection methods have drawn a lot of attention, following two directions optical and sonar imaging. This work focuses on using optical imagery and CNN-based deep-learning object detection models to detect jellyfish. While labeled data of jellyfish play an important part in training deep learning models, there are a few open and available labeled datasets. Hence, we create our dataset to train these models using our model-assisted labeling method with over 11 thousand images of underwater jellyfish and corresponding annotation files in PASCAL VOC format. Our model-assisted labeling method saves the work of classical manual labeling by 70 percent, which is developed into application with YOLOv5. However, the YOLOv5 baseline suffers from the tradeoff between real-time performance and low accuracy. Hence, an improved YOLOv5-nano is introduced based on adding GAM and replacing conventional Conv with CoordCov modules into the backbone of the conventional structure. The experiment results show that our improved model increases the accuracy of the conventional one by 1.3% and outperforms others including RetinaNet, SSD, Faster R-CNN, YOLOv6, and YOLOv8 at 89.1%

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Detection of glaucoma using three-stage training with EfficientNet

Cited by 13 publications

References 18 publications

Enhanced PRIM recognition using PRI sound and deep learning techniques

Enhanced PRIM recognition using PRI sound and deep learning techniques

Glaucoma Detection Using Explainable AI and Deep Learning

Improved YOLOv5 Based Deep Learning System for Jellyfish Detection

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