The evaluation of the morphology and organization of collagen fibers is critical in understanding wound healing and tissue remodeling after a thermal injury of the skin. However, histological analysis conducted by pathologists is often labor-intensive and limited to qualitative evaluations and scoring within a narrow field of view. In this study, we propose a convolutional neural network (CNN) model to classify Masson's trichrome (MT)-stained histology images of burn-induced scar tissue and to characterize the microstructures of normal tissue and scar tissue in a quantitative manner. The scar tissue is created on in vivo rodent models and prepared for MT-stained histology slides after wound healing. A CNN model is developed, trained, and tested with various sizes of the histology images for classification and characterization. The proposed model classifies both normal tissue (i.e., without burn, as the control) and scar tissue at various scales with over 97% accuracy. The features acquired from the proposed CNN model visually characterizes the density and directional variance of the collagen fibers distributed in the dermal layers from whole histology images. The proposed deep learning technique can provide an objective and reliable method to rapidly assess and quantify wound repair and remodeling.INDEX TERMS Deep learning, histology image, collagen fiber characterization, scar tissue classification.

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Defect inspection is essential in the semiconductor industry to fabricate printed circuit boards (PCBs) with minimum defect rates. However, conventional inspection systems are labor-intensive and time-consuming. In this study, a semi-supervised learning (SSL)-based model called PCB_SS was developed. It was trained using labeled and unlabeled images under two different augmentations. Training and test PCB images were acquired using automatic final vision inspection systems. The PCB_SS model outperformed a completely supervised model trained using only labeled images (PCB_FS). The performance of the PCB_SS model was more robust than that of the PCB_FS model when the number of labeled data is limited or comprises incorrectly labeled data. In an error-resilience test, the proposed PCB_SS model maintained stable accuracy (error increment of less than 0.5%, compared with 4% for PCB_FS) for noisy training data (with as much as 9.0% of the data labeled incorrectly). The proposed model also showed superior performance when comparing machine-learning and deep-learning classifiers. The unlabeled data utilized in the PCB_SS model helped with the generalization of the deep-learning model and improved its performance for PCB defect detection. Thus, the proposed method alleviates the burden of the manual labeling process and provides a rapid and accurate automatic classifier for PCB inspections.

Histological examination of collagen fiber organization is essential for pathologists to observe the wound healing process. A convolutional neural network (CNN) can be utilized to visually analyze collagen fibers during tissue remodeling in histology images. In this study, a universal CNN (UCNN) independent of the histological staining process is proposed to classify the histology images of burn-induced scar tissues and characterize collagen fiber organization. Normal and scar tissues obtained from an in vivo rodent model are stained using Masson's Trichrome (MT) and Hematoxylin & Eosin (H&E). The proposed universal model is trained using both MT-and H&E-stained histological image datasets over multiple scales with color augmentation, and classification accuracies of up to 98% and 97% are achieved for the MT-and H&E-stained image datasets, respectively. Regardless of the histological staining process used, the collagen characteristics are visualized by determining the density and directional variance of the normal and scar tissues by using the features extracted with the proposed universal model. Statistical analysis results demonstrated clear differences between scar and normal tissues in terms of collagen fiber organization. The proposed UCNN model can contribute to the development of an intelligent and efficient method that pathologists can use to rapidly evaluate wound healing and tissue remodeling.