Classification of dances using AlexNet, ResNet18 and SqueezeNet1_0

Zakry, Khalif Amir; Hipiny, Irwandi; Ujir, Hamimah

doi:10.11591/ijai.v12.i2.pp602-609

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…SqueezeNet [14,36] is chosen as the base architecture for this project due to its small size and low number of parameters. Compared to larger models like AlexNet [37][38][39], VGG [40,41], or ResNet [42][43][44], SqueezeNet has a significantly smaller number of parameters, making it easier to train on limited computational resources. Table 1 compares the number of parameters of various deep learning models.…”

Section: Fire Module and Squeezenet With Complex Bypassmentioning

confidence: 99%

SNSVM: SqueezeNet-Guided SVM for Breast Cancer Diagnosis

Wang,

Attique Khan,

Wang

et al. 2023

Computers, Materials &Amp; Continua

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Breast cancer is a major public health concern that affects women worldwide. It is a leading cause of cancer-related deaths among women, and early detection is crucial for successful treatment. Unfortunately, breast cancer can often go undetected until it has reached advanced stages, making it more difficult to treat. Therefore, there is a pressing need for accurate and efficient diagnostic tools to detect breast cancer at an early stage. The proposed approach utilizes SqueezeNet with fire modules and complex bypass to extract informative features from mammography images. The extracted features are then utilized to train a support vector machine (SVM) for mammography image classification. The SqueezeNet-guided SVM model, known as SNSVM, achieved promising results, with an accuracy of 94.10% and a sensitivity of 94.30%. A 10-fold cross-validation was performed to ensure the robustness of the results, and the mean and standard deviation of various performance indicators were calculated across multiple runs. This model also outperforms state-of-the-art models in all performance indicators, indicating its superior performance. This demonstrates the effectiveness of the proposed approach for breast cancer diagnosis using mammography images. The superior performance of the proposed model across all indicators makes it a promising tool for early breast cancer diagnosis. This may have significant implications for reducing breast cancer mortality rates.

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Section: Fire Module and Squeezenet With Complex Bypassmentioning

confidence: 99%

SNSVM: SqueezeNet-Guided SVM for Breast Cancer Diagnosis

Wang,

Attique Khan,

Wang

et al. 2023

Computers, Materials &Amp; Continua

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“…It introduced several innovative features that revolutionized the field. This architecture's significance lies in its ability to address challenges posed by high-resolution images and its success in the ImageNet competition [21][22][23]. AlexNet's architecture incorporated several key elements that contributed to its success.…”

Section: Alexnetmentioning

confidence: 99%

The Effectiveness of Deep Learning Methods on Groundnut Disease Detection

Kursun,

Yasin,

Koklu

2023

icat

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Early detection of plant diseases in the agricultural sector is considered an important goal to increase productivity and minimize damage. This study deals with the use of deep learning methods to realize the automatic detection of leaf diseases in peanut plants and the explicability of the model with heatmap visualizations formed during the detection of diseases. In the study, a dataset containing 3058 images with 5 classes enriched with diseased and healthy samples of peanut leaves was used. The explainability property has also been studied to understand why the models detect a particular disease. The decision processes of deep learning models, which are usually described as the "magic box", were visualized with the heatmap method in this study. By highlighting the pixels that are effective in detecting diseased leaves with heatmap visualization, the decision-making process of the model has been tried to be made understandable. The results show that deep learning models have high performance in detecting peanut leaf diseases, and the explainability obtained by heatmap visualization is a reliable tool for agricultural specialists and producers. Thanks to the visual explanations provided by the model, the level of confidence in the detection of diseases has been increased and confidence in the decision processes of the model has been provided. This study constitutes an important step towards increasing efficiency in agricultural applications and providing a more efficient approach to disease management by investigating the impact and explicability of deep learning methods in the field of disease detection in peanut plants.

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EnGesto: An Ensemble Learning Approach for Classification of Hand Gestures

Ⅴ,

2024

IEEE Access

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Classification of dances using AlexNet, ResNet18 and SqueezeNet1_0

Cited by 3 publications

References 18 publications

SNSVM: SqueezeNet-Guided SVM for Breast Cancer Diagnosis

SNSVM: SqueezeNet-Guided SVM for Breast Cancer Diagnosis

The Effectiveness of Deep Learning Methods on Groundnut Disease Detection

EnGesto: An Ensemble Learning Approach for Classification of Hand Gestures

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