Identification and diagnosis of mammographic malignant architectural distortion using a deep learning based mask regional convolutional neural network

Liu, Yuanyuan; Tong, Yunfei; Wan, Yun; Xia, Ziqiang; Yao, Guoyan; Shang, Xiaobing; Huang, Yan; Chen, Lijun; Chen, Daniel Q.; Liu, Bo

doi:10.3389/fonc.2023.1119743

Cited by 4 publications

(1 citation statement)

References 40 publications

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“…To address this challenge, we built a classification network, Neural Network B, based on the EfficientNetV2-M framework [22]. Convolutional neural networks that share similar structures with EfficientNetV2-M have demonstrated robust performance in various medical classification tasks [34][35][36].…”

Section: Discussionmentioning

confidence: 99%

Identification of Adolescent Menarche Status using Biplanar X-ray Images: A Deep Learning-based Method

XIE

Xiao

et al. 2023

Bioengineering

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The purpose of this study is to develop an automated method for identifying the menarche status of adolescents based on EOS radiographs. We designed a deep-learning-based algorithm that contains a region of interest detection network and a classification network. The algorithm was trained and tested on a retrospective dataset of 738 adolescent EOS cases using a five-fold cross-validation strategy and was subsequently tested on a clinical validation set of 259 adolescent EOS cases. On the clinical validation set, our algorithm achieved accuracy of 0.942, macro precision of 0.933, macro recall of 0.938, and a macro F1-score of 0.935. The algorithm showed almost perfect performance in distinguishing between males and females, with the main classification errors found in females aged 12 to 14 years. Specifically for females, the algorithm had accuracy of 0.910, sensitivity of 0.943, and specificity of 0.855 in estimating menarche status, with an area under the curve of 0.959. The kappa value of the algorithm, in comparison to the actual situation, was 0.806, indicating strong agreement between the algorithm and the real-world scenario. This method can efficiently analyze EOS radiographs and identify the menarche status of adolescents. It is expected to become a routine clinical tool and provide references for doctors’ decisions under specific clinical conditions.

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Section: Discussionmentioning

confidence: 99%

Identification of Adolescent Menarche Status using Biplanar X-ray Images: A Deep Learning-based Method

XIE

Xiao

et al. 2023

Bioengineering

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Image segmentation for pest detection of crop leaves by improvement of regional convolutional neural network

Wu,

Liu,

Xing

et al. 2024

Sci Rep

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Pest detection is important for crop cultivation. Crop leaf is the main place of pest invasion. Current technologies to detect crop pests have constraints, such as low efficiency, storage demands and limited precision. Image segmentation is a fast and efficient computer-aided detection technology. High resolution image capture solidly supports the crucial processes in discerning pests from images. Study of analytical methods help parse information in the images. In this paper, a regional convolutional neural network (R-CNN) architecture is designed in combination with the radial bisymmetric divergence (RBD) method for enhancing the efficiency of image segmentation. As a special application of RBD, the hierarchical mask (HM) is produced to endorse detection and classification of the leaf-dwelling pests, offering enhanced efficiency and reduced storage requirements. Moreover, to deal with some mislabeled data, a threshold variable is introduced to adjust a fault-tolerant mechanism into HM, to generate a novel threshold-based hierarchical mask (TbHM). Consequently, the hierarchical mask R-CNN (HM-R-CNN) model and the threshold-based hierarchical mask R-CNN (TbHM-R-CNN) model are established to detect various types of healthy and pest-invasive crop leaves to select the regional image features that are rich in pest information. Then simple linear iterative clustering (SLIC) method is incorporation to finish the image segmentation for the classification of pest invasion. The models are tuned and optimized, then validated. The most optimized modeling results are from the TbHM-R-CNN model, with the classification accuracy of 96.2%, the recall of 97.5% and the F1 score of 0.982. Additionally, the HM-R-CNN model observed appreciable results second only to the best model. These results indicate that the proposed methodologies are well-suited for training and testing a dataset of plant diseases, offering heightened accuracy in pest classification. This study revealed that the proposed methods significantly outperform the existing techniques, marking a substantial improvement over current methods.

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Breast Cancer Detection and Localization Using a Novel Multimodal Approach

Kumar,

Mahto

et al. 2025

IEEE Trans. Instrum. Meas.

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Identification and diagnosis of mammographic malignant architectural distortion using a deep learning based mask regional convolutional neural network

Cited by 4 publications

References 40 publications

Identification of Adolescent Menarche Status using Biplanar X-ray Images: A Deep Learning-based Method

Identification of Adolescent Menarche Status using Biplanar X-ray Images: A Deep Learning-based Method

Image segmentation for pest detection of crop leaves by improvement of regional convolutional neural network

Breast Cancer Detection and Localization Using a Novel Multimodal Approach

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