Architectural Distortion-Based Digital Mammograms Classification Using Depth Wise Convolutional Neural Network

Rehman, Khalil Ur; Li, Jianqiang; Pei, Yan; Yasin, Anaa; Ali, Saqib; Saeed, Yousaf

doi:10.3390/biology11010015

Cited by 14 publications

(12 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All the images are split into 7:3 and 5:5 to be the training set and the testing set, respectively. This split percentage was considered based on a review of the relevant literature [ 22 , 23 , 24 , 25 ]. In the positive cases, the lesions included calcifications, well-defined or localized masses, radially shaped masses, other and uncertain defined masses, structural distortions, and asymmetry tissue.…”

Section: Methodsmentioning

confidence: 99%

Using Deep Neural Network Approach for Multiple-Class Assessment of Digital Mammography

et al. 2022

View full text Add to dashboard Cite

According to the Health Promotion Administration in the Ministry of Health and Welfare statistics in Taiwan, over ten thousand women have breast cancer every year. Mammography is widely used to detect breast cancer. However, it is limited by the operator’s technique, the cooperation of the subjects, and the subjective interpretation by the physician. It results in inconsistent identification. Therefore, this study explores the use of a deep neural network algorithm for the classification of mammography images. In the experimental design, a retrospective study was used to collect imaging data from actual clinical cases. The mammography images were collected and classified according to the breast image reporting and data-analyzing system (BI-RADS). In terms of model building, a fully convolutional dense connection network (FC-DCN) is used for the network backbone. All the images were obtained through image preprocessing, a data augmentation method, and transfer learning technology to build a mammography image classification model. The research results show the model’s accuracy, sensitivity, and specificity were 86.37%, 100%, and 72.73%, respectively. Based on the FC-DCN model framework, it can effectively reduce the number of training parameters and successfully obtain a reasonable image classification model for mammography.

show abstract

Section: Methodsmentioning

confidence: 99%

Using Deep Neural Network Approach for Multiple-Class Assessment of Digital Mammography

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Radiomics with machine learning, and deep learning using convolutional neural network (CNN), have been applied to analyze images for detection and diagnosis of lesions in various clinical applications (25-27). Several studies have applied AI for the detection of architectural distortion (13,28,29). Rehman et al proposed an automated computer-aided diagnostic system using computer vision and deep learning to predict breast cancer based on the architectural distortion on DM (13).…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have applied AI for the detection of architectural distortion (13,28,29). Rehman et al proposed an automated computer-aided diagnostic system using computer vision and deep learning to predict breast cancer based on the architectural distortion on DM (13). Bahl et al performed a retrospective review and concluded that the presence of architectural distortion on mammography indicated malignancy in approximately 75% of cases (30).…”

Section: Introductionmentioning

confidence: 99%

“…However, the detection and interpretation of architectural distortion on 2-dimensional (2D) mammograms is challenging. Due to the overlapping tissues, the appearance may be subtle, and it is subjective for radiologists to detect these abnormalities, especially when there are co-existence of other findings such as mass and asymmetry ( 13 ).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Diagnosis of architectural distortion on digital breast tomosynthesis using radiomics and deep learning

Chen

Zhang

Zhou³

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

PurposeTo implement two Artificial Intelligence (AI) methods, radiomics and deep learning, to build diagnostic models for patients presenting with architectural distortion on Digital Breast Tomosynthesis (DBT) images.Materials and MethodsA total of 298 patients were identified from a retrospective review, and all of them had confirmed pathological diagnoses, 175 malignant and 123 benign. The BI-RADS scores of DBT were obtained from the radiology reports, classified into 2, 3, 4A, 4B, 4C, and 5. The architectural distortion areas on craniocaudal (CC) and mediolateral oblique (MLO) views were manually outlined as the region of interest (ROI) for the radiomics analysis. Features were extracted using PyRadiomics, and then the support vector machine (SVM) was applied to select important features and build the classification model. Deep learning was performed using the ResNet50 algorithm, with the binary output of malignancy and benignity. The Gradient-weighted Class Activation Mapping (Grad-CAM) method was utilized to localize the suspicious areas. The predicted malignancy probability was used to construct the ROC curves, compared by the DeLong test. The binary diagnosis was made using the threshold of ≥ 0.5 as malignant.ResultsThe majority of malignant lesions had BI-RADS scores of 4B, 4C, and 5 (148/175 = 84.6%). In the benign group, a substantial number of patients also had high BI-RADS ≥ 4B (56/123 = 45.5%), and the majority had BI-RADS ≥ 4A (102/123 = 82.9%). The radiomics model built using the combined CC+MLO features yielded an area under curve (AUC) of 0.82, the sensitivity of 0.78, specificity of 0.68, and accuracy of 0.74. If only features from CC were used, the AUC was 0.77, and if only features from MLO were used, the AUC was 0.72. The deep-learning model yielded an AUC of 0.61, significantly lower than all radiomics models (p<0.01), which was presumably due to the use of the entire image as input. The Grad-CAM could localize the architectural distortion areas.ConclusionThe radiomics model can achieve a satisfactory diagnostic accuracy, and the high specificity in the benign group can be used to avoid unnecessary biopsies. Deep learning can be used to localize the architectural distortion areas, which may provide an automatic method for ROI delineation to facilitate the development of a fully-automatic computer-aided diagnosis system using combined AI strategies.

show abstract

Reinforcement learning (RL)-based semantic segmentation and attention based backpropagation convolutional neural network (ABB-CNN) for breast cancer identification and classification using mammogram images

Thakur,

Kumar,

Kumar

2024

Neural Comput & Applic

View full text Add to dashboard Cite

Architectural Distortion-Based Digital Mammograms Classification Using Depth Wise Convolutional Neural Network

Cited by 14 publications

References 47 publications

Using Deep Neural Network Approach for Multiple-Class Assessment of Digital Mammography

Using Deep Neural Network Approach for Multiple-Class Assessment of Digital Mammography

Diagnosis of architectural distortion on digital breast tomosynthesis using radiomics and deep learning

Reinforcement learning (RL)-based semantic segmentation and attention based backpropagation convolutional neural network (ABB-CNN) for breast cancer identification and classification using mammogram images

Contact Info

Product

Resources

About