Abstract
Deep learning is an obvious method for the detection of disease, analyzing medical images and many researchers have looked into it. However, the performance of deep learning algorithms is frequently influenced by hyperparameter selection, the question of which combination of hyperparameters are best emerges. To address this challenge, we proposed a novel algorithm for Adaptive Hyperparameter Tuning (AHT) that automates the selection of optimal hyperparameters for Convolutional Neural Network (CNN) training. All of the optimal hyperparameters for the CNN models were instantaneously selected and allocated using a novel proposed algorithm Adaptive Hyperparameter Tuning (AHT). Using AHT, enables CNN models to be highly autonomous to choose optimal hyperparameters for classifying medical images into various classifications. The CNN model (Deep-Hist) categorizes medical images into basic classes: malignant and benign, with an accuracy of 95.71%. The most dominant CNN models such as ResNet, DenseNet, and MobileNetV2 are all compared to the already proposed CNN model (Deep-Hist). Plausible classification results were obtained using large, publicly available clinical datasets such as BreakHis, BraTS, NIH-Xray and COVID-19 X-ray. Medical practitioners and clinicians can utilize the CNN model to corroborate their first malignant and benign classification assessment. The recommended Adaptive high F1 score and precision, as well as its excellent generalization and accuracy, imply that it might be used to build a pathologist’s aid tool.
Funder
National Key R&D Program of China
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference49 articles.
1. Ilievski, I., Akhtar, T., Feng, J., and Shoemaker, C. (2017, January 4–9). Efficient hyperparameter optimization for deep learning algorithms using deterministic rbf surrogates. Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.
2. Ogundokun, R.O., Misra, S., Douglas, M., Damaševičius, R., and Maskeliūnas, R. (2022). Medical Internet-of-Things Based Breast Cancer Diagnosis Using Hyperparameter-Optimized Neural Networks. Future Internet, 14.
3. Random search for hyper-parameter optimization;J. Mach. Learn. Res.,2012
4. Algorithms for hyper-parameter optimization;Adv. Neural Inf. Process. Syst.,2011
5. Bayesian methods in global optimization;J. Glob. Optim.,1991
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献