A Novel Hyperparameter Optimized Quantized MobileNet Architecture for Improving Diagnostic Accuracy in the Medical Image Classification

Author:

Patel RupaORCID,Chaware AnitaORCID

Publisher

Springer Nature Switzerland

Reference24 articles.

1. Denil, M., Shakibi, B., Dinh, L., Ranzato, M.A., De Freitas, N.: Predicting parameters in deep learning. In: Advances in Neural Information Processing Systems, pp. 2148–2156 (2013)

2. Patel, R., Chaware, A.: Quantizing mobilenet models for classification problem. In: INDIACom-2021; 15th INDIACom; 2021 8th International Conference on “Computing for Sustainable Global Development”,organised by BVICAM, New Delhi. 17th–19th March (2021)

3. Gholami, A., Kim, S., Dong, Z., Yao, Z., Mahoney, M.W., Keutzer, K.: A survey of quantization methods for efficient neural network inference. arXiv preprint arXiv:2103.13630 (2021)

4. Jin, H., et al.: Hyperparameter Importance for Machine Learning Algorithms. arXiv preprint arXiv:2201.05132 (2022)

5. Gülcü, A., Kuş, Z.: Hyper-parameter selection in convolutional neural networks using microcanonical optimization algorithm. IEEE Access 8, 52528–52540 (2020)

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