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2 2022

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TL-med: A Two-stage transfer learning recognition model for medical images of COVID-19

Meng Jiana, Tan Zhiyong, Yu Yuhai, Wang Pengjie, Liu Shuang

Biocybernetics and Biomedical Engineering

2022

Vol. 42, no. 3

842--855

The recognition of medical images with deep learning techniques can assist physicians in clinical diagnosis, but the effectiveness of recognition models relies on massive amounts of labeled data. With the rampant development of the novel coronavirus (COVID-19) worldwide, rapid COVID-19 diagnosis has become an effective measure to combat the outbreak. However, labeled COVID-19 data are scarce. Therefore, we propose a two-stage transfer learning recognition model for medical images of COVID-19 (TL-Med) based on the concept of ‘‘generic domain-target-related domain-target domain”. First, we use the Vision Transformer (ViT) pretraining model to obtain generic features from massive heterogeneous data and then learn medical features from large-scale homogeneous data. Two-stage transfer learning uses the learned primary features and the underlying information for COVID-19 image recognition to solve the problem by which data insufficiency leads to the inability of the model to learn underlying target dataset information. The experimental results obtained on a COVID-19 dataset using the TL-Med model produce a recognition accuracy of 93.24%, which shows that the proposed method is more effective in detecting COVID-19 images than other approaches and may greatly alleviate the problem of data scarcity in this field.

Experimental investigation on mode II fracture performance of old-new concrete

Liu Shuang, Shi Zhenwu, Jiang Tao, Wang Huili

Archives of Civil Engineering

2022

Vol. 68, nr 3

469--480

The old-new concrete interface is the weakest part in the composite structure, and there are a large number of microcracks on the interface. In order to study the mode II fracture performance of the bonding surface of old-new concrete, the effect of planting rebar and basalt fiber is investigated. Nine Z-shaped old-new concrete composite specimens with initial cracks are made. Nine shear fracture load-displacement curves are obtained, and the failure process and interface fracture are discussed. On this basis, the mode II fracture toughness and fracture energy are obtained. The regression equations for fracture toughness and fracture energy are deduced with analysis of variance (ANOVA). The results show that fracture toughness and fracture energy increase with the increase of planting rebar number and basalt fiber content. With the increase of the planting rebar number, mode II fracture toughness and fracture energy increase more significantly. Planting rebar is the major factor for mode II fracture performance.