Classification of COVID-19 X-ray images using transfer learning with visual geometrical groups and novel sequential convolutional neural networks

Kumar, Sunil; Kumar, Harish

doi:10.1016/j.mex.2023.102295

Cited by 16 publications

(1 citation statement)

References 39 publications

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“…In the VGG-19 model, VGG-19 is a CNN model that has the most and deepest layers and can reduce the number of parameters because each convolution layer uses a tiny filter of size 3x3, so it is well applied and produces an error rate of 7.3%. CNN consists of three layers: convolutional, pooling, and fully connected [6], [7]. Therefore, the CNN method with the VGG-19 and ResNet50 models was used in this research because the VGG-19 and ResNet50 models match the dataset used in this research.…”

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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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: Introductionmentioning

confidence: 99%