Mobile-based Primate Image Recognition using CNN

Wiranda, Nuruddin; Putra, Agfianto Eko

doi:10.22146/ijccs.65640

Cited by 1 publication

(1 citation statement)

References 26 publications

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“…This approach ensures that the model's functionality is readily available to users, facilitating seamless integration in to various applications or systems. The deployed model is executed within a Docker container environment, which includes the Flask framework for building the API (Application Programming Interface) [25]. The API serves as the communication channel between users and the machine learning system.…”

Section: Model Deploymentmentioning

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: Model Deploymentmentioning

confidence: 99%