Classification of the qilou (arcade building) using a robust image processing framework based on the Faster R-CNN with ResNet50

Li, Ming Ho; Chen, Yu; Wei, Hongni; Chan, Ting On

doi:10.1080/13467581.2023.2238038

Cited by 4 publications

(2 citation statements)

References 46 publications

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“…The method used in this study uses the CNN method , with the proposed models being ResNet50 and VGG-19, to get the best results in identifying banana plant diseases through banana leaf images. The choice to use the ResNet50 model is because ResNet50 is one of the best models and a suitable model for the dataset used, and ResNet50 also has a total of 50 layers [18], [27]. Challenge and is a model that also fits the dataset used.…”

Section: Processing Of Datasets and Architectural Modelsmentioning

confidence: 99%

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CNN Method to Identify the Banana Plant Diseases based on Banana Leaf Images by Giving Models of ResNet50 and VGG-19

Ilham Rahmana Syihad,

Muhammad Rizal,

Zamah Sari

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Identify banana plant diseases using machine learning with the CNN method to make it easier to identify diseases in banana plants through leaf images. It employs the CNN method, incorporating ResNet50 because ResNet50 is one of the best models and a suitable model for the dataset used, and the VGG-19 model is used because VGG-19 was one of the winning models of the 2014 ImageNet Challenge and is a model that also fits the dataset used. The research objectives encompass dataset processing, model architecture development, evaluation, and result reporting, all aimed at enhancing disease identification in banana plants. The ResNet50 model achieved an impressive 94% accuracy, with 88% precision, 91% recall, and an 89% F1-score, while the VGG-19 model demonstrated strong performance with 91% accuracy, surpassing prior research and highlighting the effectiveness of these models in identifying banana plant diseases through leaf images. In conclusion, the ResNet50 model's exceptional accuracy positions it as the preferred model for CNN-based disease identification in banana plants, offering significant advancements and insights for agricultural practices. Future research opportunities include exploring alternative CNN models, architectural variations, and more extensive training datasets to enhance disease identification accuracy.

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Section: Processing Of Datasets and Architectural Modelsmentioning

confidence: 99%

“…ResNet-50 is a variant of ResNet that has 50 layers. If, in the previous ResNet variant, there was a skip connection of 2 layers, then ResNet-50 skips three layers, and there is a 1 X 1 convolution layer [17], [18]. So that makes ResNet-50 one of the best models.…”

Section: Introductionmentioning

confidence: 99%

CNN Method to Identify the Banana Plant Diseases based on Banana Leaf Images by Giving Models of ResNet50 and VGG-19

Ilham Rahmana Syihad,

Muhammad Rizal,

Zamah Sari

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Research on Intelligent Chassis Drive-by-Wire Technology Using Computer Vision in Autonomous Driving

2024

2024 IEEE 7th International Conference on Information Systems and Computer Aided Education (ICISCAE)

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Adaptive Mish activation and ranger optimizer-based SEA-ResNet50 model with explainable AI for multiclass classification of COVID-19 chest X-ray images

Sannasi Chakravarthy,

Bharanidharan,

Vinothini

et al. 2024

BMC Med Imaging

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A recent global health crisis, COVID-19 is a significant global health crisis that has profoundly affected lifestyles. The detection of such diseases from similar thoracic anomalies using medical images is a challenging task. Thus, the requirement of an end-to-end automated system is vastly necessary in clinical treatments. In this way, the work proposes a Squeeze-and-Excitation Attention-based ResNet50 (SEA-ResNet50) model for detecting COVID-19 utilizing chest X-ray data. Here, the idea lies in improving the residual units of ResNet50 using the squeeze-and-excitation attention mechanism. For further enhancement, the Ranger optimizer and adaptive Mish activation function are employed to improve the feature learning of the SEA-ResNet50 model. For evaluation, two publicly available COVID-19 radiographic datasets are utilized. The chest X-ray input images are augmented during experimentation for robust evaluation against four output classes namely normal, pneumonia, lung opacity, and COVID-19. Then a comparative study is done for the SEA-ResNet50 model against VGG-16, Xception, ResNet18, ResNet50, and DenseNet121 architectures. The proposed framework of SEA-ResNet50 together with the Ranger optimizer and adaptive Mish activation provided maximum classification accuracies of 98.38% (multiclass) and 99.29% (binary classification) as compared with the existing CNN architectures. The proposed method achieved the highest Kappa validation scores of 0.975 (multiclass) and 0.98 (binary classification) over others. Furthermore, the visualization of the saliency maps of the abnormal regions is represented using the explainable artificial intelligence (XAI) model, thereby enhancing interpretability in disease diagnosis.

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Classification of the qilou (arcade building) using a robust image processing framework based on the Faster R-CNN with ResNet50

Cited by 4 publications

References 46 publications

CNN Method to Identify the Banana Plant Diseases based on Banana Leaf Images by Giving Models of ResNet50 and VGG-19

CNN Method to Identify the Banana Plant Diseases based on Banana Leaf Images by Giving Models of ResNet50 and VGG-19

Research on Intelligent Chassis Drive-by-Wire Technology Using Computer Vision in Autonomous Driving

Adaptive Mish activation and ranger optimizer-based SEA-ResNet50 model with explainable AI for multiclass classification of COVID-19 chest X-ray images

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