CNN for the detection of COVID-19 from chest X-ray images

Florez, Ruben; Concha-Ramos, Yesenia; Palomino-Quispe, Facundo; Coaquira-Castillo, Roger Jesus

doi:10.1109/eircon56026.2022.9934820

Cited by 1 publication

(2 citation statements)

References 11 publications

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“…In this study, we use a pre-trained ResNet50V2 model as the backbone of our proposed approach, due to its effectiveness in various deep learning tasks [18,19,23]. A pre-trained ResNet50V2 model is initialized with pretrained weights from the ImageNet dataset, which aids in capturing general image features.…”

Section: Resnet50v2mentioning

confidence: 99%

“…One such deep learning architecture that has gained popularity is ResNet50V2 [17]. ResNet50V2 is a convolutional neural network that has achieved remarkable performance in image classification tasks [18,19]. By leveraging its pre-trained weights and features learned from large-scale datasets, we can leverage transfer learning to adapt ResNet50V2 for forest fire detection.…”

Section: Introductionmentioning

confidence: 99%

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TeutongNet: A Fine-Tuned Deep Learning Model for Improved Forest Fire Detection

Idroes,

Maulana,

Suhendra

et al. 2023

Leuser J. Environ. Stud.

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Forest fires have emerged as a significant threat to the environment, wildlife, and human lives, necessitating the development of effective early detection systems for firefighting and mitigation efforts. In this study, we introduce TeutongNet, a modified ResNet50V2 model designed to detect forest fires accurately. The model is trained on a curated dataset and evaluated using various metrics. Results show that TeutongNet achieves high accuracy (98.68%) with low false positive and false negative rates. The model's performance is further supported by the ROC curve analysis, which indicates a high degree of accuracy in classifying fire and non-fire images. TeutongNet demonstrates its effectiveness in reliable forest fire detection, providing valuable insights for improved fire management strategies.

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Section: Resnet50v2mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%