Interpretable Optimization Training Strategy-Based DCNN and Its Application on CT Image Recognition

Ronghan Wang, Tao Liu, Junwei Lu, Yuwei Zhou and Akemi Gálvez

Mathematical Problems in Engineering, 2022, vol. 2022, 1-13

Abstract: The theoretical basis of the discrete random sample batch classification is not clear and the sample division is not scientific during the process of Deep Convolutional Neural Network (DCNN) model training. Aiming at the problems above, starting from the DCNN detection recognition mechanism, the theory of random discrete samples is given and proved, and a scientific quantitative batch of sample input method is proposed. Combined with image preprocessing, based on the strategy of random dispersion of samples, and scientifically quantified sample input batches, the DCNN model is trained with limited label samples, and then the CT image recognition of pulmonary nodules is carried out. Experimental results based on the LIDC-ID-RI public dataset show that the sensitivity, specificity, and accuracy of the proposed method have reached 96.40%, 95.60%, and 96.00%, respectively. Compared with the multiscale convolutional neural network method and the multiscale multimode image fusion method, the recognition accuracy of the proposed method is improved by 1.6 and 3.49 percentage points, respectively.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/mpe/2022/2170596.pdf (application/pdf)
http://downloads.hindawi.com/journals/mpe/2022/2170596.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:2170596

DOI: 10.1155/2022/2170596

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().