Comparison of the Performance of CNN Models for Retinal Diseases Diagnosis

Kim, Hyewon; Zhang, Xuefeng; Kim, Yong Soo; Jung, Il-Hong

doi:10.5391/jkiis.2022.32.1.51

Cited by 2 publications

(1 citation statement)

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“…( 1), ( 2), (3), and (4), respectively. These evaluation indicators are generally used to evaluate the model performance in the multiple classification tasks of CNN models [20]. Accuracy is the ratio of the data correctly predicted by the model to the entire data, and precision is the ratio of the positive data to the data predicted to be positive by the model.…”

Section: Model Training Performance Evaluation Indicatormentioning

confidence: 99%

Development of a Forest Fire Detection System Using a Drone-based Convolutional Neural Network Model

Lee¹,

Jeong²,

Jung³

2023

Int J Fire Sci Eng

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Considering forest fires cause environmental destruction, ecosystem collapse, and severe damage to human lives and nature, developing a real-time, accurate, and stable forest fire detection system has become a critical issue in modern society. In this study, a drone-based forest fire detection system was developed using a convolutional neural network (CNN) model. Real-time forest fire detection models were developed using the CNN-based MobileNet algorithm, and their fire detection performance was evaluated. The main research results indicated that errors decreased and accuracy tended to increase during the model training and validation process as training progressed. Moreover, the V1 model exhibited the highest validation accuracy of 0.9466 among the MobileNet V1, V2, and V3 models and showed the highest accuracy of 0.9667 in evaluating the new test dataset during the model evaluation process.

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Section: Model Training Performance Evaluation Indicatormentioning

confidence: 99%