Venation Based Plant Leaves Classification Using GoogLeNet and VGG

Jasitha, P; Dileep, M. R.; Divya, M.

doi:10.1109/rteict46194.2019.9016966

Cited by 14 publications

(2 citation statements)

References 6 publications

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“…The advantage of GoogleNet is in the inception modules that CNN does not have. Inception modules consist of several small convolutions that aim to reduce the number of parameters without reducing network performance [40] . The output in the previous layer is processed in 4 layers of 1×1 convolution layer, one layer of 3×3 convolution layer, one layer of 5×5 convolution layer, and 3×3 pooling, which is added together with the arrangement as shown in Fig.…”

Section: Googlenetmentioning

confidence: 99%

Ultrasound Image Synthetic Generating Using Deep Convolution Generative Adversarial Network For Breast Cancer Identification

Haq

Fatichah

2023

JTS

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Breast cancer is the leading cause of death in women worldwide; prevention of possible death from breast cancer can be decreased by early identification ultrasound image analysis by classifying ultrasound images into three classes (Normal, Benign, and Malignant), where the dataset used has imbalanced data. Imbalanced data cause the classification system only to recognize the majority class, so it is necessary to handle imbalanced data. In this study, imbalanced data can be handled by implementing the Deep Convolution Generative Adversarial Network (DCGAN) method as the addition of synthetic images to the training data. The DCGAN method generates synthetic images with feature learning on a Convolutional Neural Network (CNN), making DCGAN more stable than the basic generative adversarial network method.Synthetic and original images were further classified using the CNN GoogleNet method, which performs well in image classification and with reasonable computation cost. Synthetic ultrasound images were generated using a tuning hyperparameter in the DCGAN method to adjust the input size on GoogleNet for imbalanced data handling. From the experiment result, the implementation of DCGAN-GoogleNet has a higher accuracy in handling imbalanced data than conventional augmentation and other previous research, with an accuracy value reaching 91.61%, which is 1% to 4% higher than the accuracy value in the previous method.

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Section: Googlenetmentioning

confidence: 99%

Ultrasound Image Synthetic Generating Using Deep Convolution Generative Adversarial Network For Breast Cancer Identification

Haq

Fatichah

2023

JTS

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“…GoogLeNet is a 22-layer network structure proposed by Jasitha et al (2019). Inception V3, with three inception groups, is more complicated than GoogLeNet.…”

Section: Inception V3mentioning

confidence: 99%

Automatic Classification of NVST Short-exposure Data Based on Deep Learning

Zheng

Huang

et al. 2021

PASP

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The New Vacuum Solar Telescope is one of the most important solar telescopes in China. However, in the process of reconstructing high-resolution solar data, the data may be distorted by thin film interference fringes. In this paper, an automatic classification method based on deep learning is proposed to distinguish fringe-contained data and fringe-free data, employing the Adaptive Wavelet Transform to construct the sample data set while transfer learning is utilized to train the classification model. The experimental results show that classification accuracy of the proposed method can reach up to 99.3%. This proposed method can make the high-resolution reconstruction pipeline run automatically whether the solar data contains fringes or not.

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Leaf Diagnosis Using Transfer Learning

Udawant

Srinath

2021

Advances in Intelligent Systems and Computing

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Venation Based Plant Leaves Classification Using GoogLeNet and VGG

Cited by 14 publications

References 6 publications

Ultrasound Image Synthetic Generating Using Deep Convolution Generative Adversarial Network For Breast Cancer Identification

Ultrasound Image Synthetic Generating Using Deep Convolution Generative Adversarial Network For Breast Cancer Identification

Automatic Classification of NVST Short-exposure Data Based on Deep Learning

Leaf Diagnosis Using Transfer Learning

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