Car Classification Based on Image Using Transfer Learning Convolutional Neural Network

Saputra, Wahyu S.J.; Puspaningrum, Eva Yulia; Syahputra, Wahyu Firman; Sari, Anggraini Puspita; Via, Yisti Vita; Idhom, Mohammad

doi:10.1109/itis57155.2022.10010073

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…It has revolutionized fields like autonomous vehicles, medical imaging, and surveillance systems. In autonomous vehicles, it helps recognize pedestrians, traffic signs, and other vehicles for safe navigation [23].…”

Section: Cnn Algorithm Designmentioning

confidence: 99%

Pneumonia Classification Utilizing VGG-16 Architecture and Convolutional Neural Network Algorithm for Imbalanced Datasets

Idhom,

Prasetya,

Riyantoko

et al. 2023

TIERS

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This research focuses on accurately classifying pneumonia in children under the age of 5 using X-ray images, considering the challenge of an imbalanced dataset. A modified VGG-16 CNN architecture is evaluated for pneumonia classification in Chest X-Ray Images. The study compares testing results with and without data augmentation techniques and explores the potential application of the model in an Android-based machine learning system for pneumonia diagnosis assistance. Using a dataset of 5,856 Chest X-Ray images categorized as normal or pneumonia, obtained from Kaggle, the research conducts two test scenarios: one without data augmentation and another with data augmentation techniques. The modified VGG-16 CNN algorithm's performance is evaluated using the accuracy metric. The results highlight the effectiveness of data augmentation in improving pneumonia classification accuracy. The augmented tests outperform the non-augmented ones, achieving an impressive 92% accuracy, indicating a significant 15% improvement over the non-augmented scenario. This improvement underscores the efficacy of data augmentation techniques in enhancing the CNN's ability to accurately classify pneumonia, particularly when faced with an imbalanced dataset. Furthermore, the research explores the potential integration of the trained model into an Android-based machine learning system for pneumonia diagnosis assistance. This integration would enable doctors to analyze X-ray images and identify potential pneumonia cases in patients. The integration of advanced machine learning systems in healthcare holds promise for improving patient care and the accuracy of pneumonia diagnoses. In summary, this research contributes to the accurate classification of pneumonia in children under 5 years old using X-ray images. It emphasizes the efficacy of data augmentation techniques in enhancing classification accuracy and explores the practical application of an Android-based machine learning system for pneumonia diagnosis assistance. These findings underscore the importance of advanced machine learning systems in healthcare and their potential to improve pneumonia diagnosis accuracy and enhance patient care.

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Section: Cnn Algorithm Designmentioning

confidence: 99%

Pneumonia Classification Utilizing VGG-16 Architecture and Convolutional Neural Network Algorithm for Imbalanced Datasets

Idhom,

Prasetya,

Riyantoko

et al. 2023

TIERS

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“…S. J. Saputra and E. Y. Puspaningrum used the CIFAR-10 and CIFAR-100 datasets, with data preprocessing involving resizing, rescaling, and data augmentation. They employed the DenseNet model for transfer learning, resulting in an average accuracy of 92% and an average precision of 87% in their test results [9].We use these models that have already shown significant performance to train in order to achieve higher accuracy on our dataset.This article selects three models with the best performance and analyzes their structural characteristics. The fundamental building block of MobileNet is the depthwise separable convolution, which can be broken down into two smaller operations: depthwise convolution and pointwise convolution.…”

Section: Introductionmentioning

confidence: 99%

Improving vehicle classification model algorithm based on ImageNet pretraining

Wang

2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

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This study employed a training dataset comprising 10 distinct vehicle models for prediction purposes. Among a plethora of models, InceptionV3, MobileNet, and DenseNet were selected due to their relatively high accuracy rates, and they were utilized to enhance the algorithm. Comparative analyses were conducted with the models prior to improvement. The primary approach in this study involved data augmentation, which included rotating images by specific angles, scaling, proportionally shifting images horizontally, and performing horizontal flips on the images within the dataset. Various performance metrics, namely Accuracy, Precision, Recall were calculated using specific formulae. The predictive outcomes for the classification problem were summarized, and specific metric data for different vehicle models were observed to determine the most suitable model for our dataset. A comparative analysis of the predictive accuracy of different network architectures for different types of vehicle models has been determined.

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Car Classification Based on Image Using Transfer Learning Convolutional Neural Network

Cited by 2 publications

References 11 publications

Pneumonia Classification Utilizing VGG-16 Architecture and Convolutional Neural Network Algorithm for Imbalanced Datasets

Pneumonia Classification Utilizing VGG-16 Architecture and Convolutional Neural Network Algorithm for Imbalanced Datasets

Improving vehicle classification model algorithm based on ImageNet pretraining

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