Comparison of CNN Algorithms for Feature Extraction on Fundus Images to Detect Glaucoma

Sunanthini, V.; Deny, J.; Kumar, E. Govinda; Vairaprakash, S.; Govindan, Petchinathan; Sudha, S.; Muneeswaran, V.; Thilagaraj, M.

doi:10.1155/2022/7873300

Cited by 10 publications

(4 citation statements)

References 13 publications

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“…The authors developed a convolutional neural network (CNN) algorithm for calculating CDR. Since then, algorithms have become increasingly sophisticated and clinically targeted toward the identification of referable glaucoma 14,17–23. For example, Bhuiyan and colleagues developed one of the first proprietary semiautomated software to quantify vertical CDR.…”

Section: Methodsmentioning

confidence: 99%

“…Since then, algorithms have become increasingly sophisticated and clinically targeted toward the identification of referable glaucoma. 14,[17][18][19][20][21][22][23] For example, Bhuiyan and colleagues developed one of the first proprietary semiautomated software to quantify vertical CDR. CDRs above 0.5 were considered "suspect."…”

Section: Fundus Photo-based Algorithmsmentioning

confidence: 99%

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Applications of Artificial Intelligence and Deep Learning in Glaucoma

Chen

Anran

Tan

et al. 2023

Asia-Pacific Journal of Ophthalmology

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Diagnosis and detection of progression of glaucoma remains challenging. Artificial intelligence-based tools have the potential to improve and standardize the assessment of glaucoma but development of these algorithms is difficult given the multimodal and variable nature of the diagnosis. Currently, most algorithms are focused on a single imaging modality, specifically screening and diagnosis based on fundus photos or optical coherence tomography images. Use of anterior segment optical coherence tomography and goniophotographs is limited. The majority of algorithms designed for disease progression prediction are based on visual fields. No studies in our literature search assessed the use of artificial intelligence for treatment response prediction and no studies conducted prospective testing of their algorithms. Additional challenges to the development of artificial intelligence-based tools include scarcity of data and a lack of consensus in diagnostic criteria. Although research in the use of artificial intelligence for glaucoma is promising, additional work is needed to develop clinically usable tools.

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Section: Methodsmentioning

confidence: 99%

Section: Fundus Photo-based Algorithmsmentioning

confidence: 99%

Applications of Artificial Intelligence and Deep Learning in Glaucoma

Chen

Anran

Tan

et al. 2023

Asia-Pacific Journal of Ophthalmology

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“…[7] This emphasizes the significance of addressing data scarcity challenges for more robust intrusion detection systems. [8] Abdulkadir Tasdele and Baha Sen propose a hybrid CNN-LSTM model for the classification of pre-miRNAs. However, the study notes a limitation attributed to a restricted dataset, emphasizing the need for further data acquisition to enhance the model's classification capabilities in pre-miRNA analysis.…”

Section: IImentioning

confidence: 99%

Tomato Leaves Disease Detection using Hybrid Model CNN-LSTM

Chandran

2024

IJRASET

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This paper delves into the exploration and comparison of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN-LSTM model for the task of tomato leaf disease detection. Through rigorous experimentation and evaluation, it was determined that the CNN-LSTM hybrid model demonstrates superior performance both CNN and LSTM individually in terms of accuracy and robustness. Leveraging a comprehensive dataset comprising images of healthy and diseased tomato leaves, including various disease types, such as Bacterial Spot, Early Blight, Septorial Leaf Spot, Leaf Mold, and Yellow Leaf Curl Virus, the study demonistrate the efficacy of the suggested hybrid approach. The hybrid model capitalizes on the strengths of both CNN and LSTM, integrating spatial and temporal information for enhanced disease identification accuracy. The results highlight the effect of hybrid deep learning structures in agricultural disease management, offering a promising solution for early detection and mitigation of plant diseases. This paper dds to the expanding collection of literature on deep learning applications in agriculture and offers valuable insights for researchers and practitioners seeking to address challenges in crop health monitoring and management.

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“…In recent years, methods have been developed that can perform feature extraction. The feature extraction model mainly uses CNN as its architecture [17], [18]. The feature extraction results are then matched, and several methods are used, such as PCA [19], SVM [20], Euclidean distance, and cosine similarity [21].…”

Section: Introductionmentioning

confidence: 99%

Design and Build of Masked Face Identification System and IoT-Based Body Temperature Measurement

Bayupati,

Ersapramana,

Dharmaadi

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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The new normal is an era in the behavior changed to obstruct the spread of COVID-19, such as decreasing people's mobility, body temperature measuring, mandatory masking, and getting a COVID-19 vaccine regularly. This study develops an identification system based on the Internet of Things through facial biometrics and temperature measurement. Face identification is divided into two main steps: face detection and identification. Face detections used the Framework YOLOv5, in which the systems can detect masked and without masked faces. Pre-trained VGG-face is used for face identification for feature extraction and produces a 2622-dimensional vector. The feature extraction result is calculated as the distance similarity with the features stored in the Database using Euclidean distance. Temperature measurement utilizes IoT by using the NodeMCU ESP8266 and the MLX90614 sensor. NodeMCU ESP8266 is a microcontroller equipped with a WI-FI module to send temperature data so measurements can be delivered wirelessly. The MLX90614 sensor measures body temperature at a 40 â€“ 60 cm distance from the Sensor. Calibration of the sensor used Two-point Calibration, so a trim error rate level is produced. The result successfully identified the face with the F1 score of 92% without a masked face and 73% for a masked face. The body temperature was measured using the MLX90614 sensor produced an error rate of 0.1Â°C after calibration. In the future, this system can be further developed and utilized for other sectors, such as the medical and security sectors.

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Comparison of CNN Algorithms for Feature Extraction on Fundus Images to Detect Glaucoma

Cited by 10 publications

References 13 publications

Applications of Artificial Intelligence and Deep Learning in Glaucoma

Applications of Artificial Intelligence and Deep Learning in Glaucoma

Tomato Leaves Disease Detection using Hybrid Model CNN-LSTM

Design and Build of Masked Face Identification System and IoT-Based Body Temperature Measurement

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