Efficient Tuning of Hyper-parameters in Convolutional Neural Network for Classification of Tuberculosis Images
Proceedings of International Conference on Women Researchers in Electronics and Computing(2021)
摘要
Deep Learning (DL) algorithms, especially Convolutional Neural Network (CNN) have outperformed in medical image classification tasks and have achieved human-competitive performance. This has become possible because CNN learns image features through backpropagation. However, the strategy for designing a CNN model with the highest accuracy for a specific application is often unclear. Because finding an appropriate network structure with the best combination of hyperparameters for different datasets is always a challenging task. To address this, we propose an optimized CNN framework that automatically and efficiently tune its hyper-parameters using a hyperband search optimization approach. In this paper, an efficient CNN with optimized hyperparameters for the classification of tuberculosis disease in Chest X-Ray (CXR) images is trained and tested on a publicly available NLM-China dataset. The experimental results illustrate that the hyperparameters optimize the CNN framework and achieve 91.42% accuracy for the classification of tuberculosis disease in CXR images.
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关键词
Tuberculosis Detection,Deep Learning,Transfer Learning,Feature Extraction,Image Processing
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