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Chen Z, Li N, Chen Z, Zhou L, Xiao L, Zhang Y. Computer-assisted decision support for the usage of preventive antibacterial therapy in children with febrile pyelonephritis: A preliminary study. Heliyon 2024; 10:e31255. [PMID: 38818202 PMCID: PMC11137416 DOI: 10.1016/j.heliyon.2024.e31255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 05/13/2024] [Accepted: 05/13/2024] [Indexed: 06/01/2024] Open
Abstract
Urinary tract infection (UTI) is one of the most common infectious diseases among children, but there is controversy regarding the use of preventive antibiotics for children first diagnosed with febrile pyelonephritis. To the best of our knowledge, no studies have addressed this issue by the deep learning technology. Therefore, in the current study, we conducted a study using Tc 99 m - DMSA renal static imaging data to investigate the need for preventive antibiotics on children first diagnosed with febrile pyelonephritis under 2 years old. The self-collected dataset comprised 64 children who did not require preventive antibiotic treatments and 112 children who did. Using several classic deep learning models, we verified that it is feasible to screen whether the first diagnosed children with febrile pyelonephritis require preventive antibacterial therapy, achieving a graded diagnosis. With the AlexNet model, we obtained accuracy of 84.05%, sensitivity of 81.71% and specificity of 86.70%, respectively. The experimental results indicate that deep learning technology could provide a new avenue to implement computer-assisted decision support for the diagnosis of the febrile pyelonephritis.
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Affiliation(s)
- Zhengguo Chen
- NHC Key Laboratory of Nuclear Technology Medical Transformation (MIANYANG CENTRAL HOSPITAL), Mianyang, 621000, China
| | - Ning Li
- School of Computer Science and Technology, Laboratory for Brain Science and Medical Artificial Intelligence, Southwest University of Science and Technology, Mianyang, 621010, China
| | - Zhu Chen
- NHC Key Laboratory of Nuclear Technology Medical Transformation (MIANYANG CENTRAL HOSPITAL), Mianyang, 621000, China
| | - Li Zhou
- NHC Key Laboratory of Nuclear Technology Medical Transformation (MIANYANG CENTRAL HOSPITAL), Mianyang, 621000, China
| | - Liming Xiao
- NHC Key Laboratory of Nuclear Technology Medical Transformation (MIANYANG CENTRAL HOSPITAL), Mianyang, 621000, China
| | - Yangsong Zhang
- NHC Key Laboratory of Nuclear Technology Medical Transformation (MIANYANG CENTRAL HOSPITAL), Mianyang, 621000, China
- School of Computer Science and Technology, Laboratory for Brain Science and Medical Artificial Intelligence, Southwest University of Science and Technology, Mianyang, 621010, China
- Key Laboratory of Testing Technology for Manufacturing Process, Ministry of Education, Southwest University of Science and Technology, Mianyang, 621010, China
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Binson VA, Thomas S, Subramoniam M, Arun J, Naveen S, Madhu S. A Review of Machine Learning Algorithms for Biomedical Applications. Ann Biomed Eng 2024; 52:1159-1183. [PMID: 38383870 DOI: 10.1007/s10439-024-03459-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 01/24/2024] [Indexed: 02/23/2024]
Abstract
As the amount and complexity of biomedical data continue to increase, machine learning methods are becoming a popular tool in creating prediction models for the underlying biomedical processes. Although all machine learning methods aim to fit models to data, the methodologies used can vary greatly and may seem daunting at first. A comprehensive review of various machine learning algorithms per biomedical applications is presented. The key concepts of machine learning are supervised and unsupervised learning, feature selection, and evaluation metrics. Technical insights on the major machine learning methods such as decision trees, random forests, support vector machines, and k-nearest neighbors are analyzed. Next, the dimensionality reduction methods like principal component analysis and t-distributed stochastic neighbor embedding methods, and their applications in biomedical data analysis were reviewed. Moreover, in biomedical applications predominantly feedforward neural networks, convolutional neural networks, and recurrent neural networks are utilized. In addition, the identification of emerging directions in machine learning methodology will serve as a useful reference for individuals involved in biomedical research, clinical practice, and related professions who are interested in understanding and applying machine learning algorithms in their research or practice.
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Affiliation(s)
- V A Binson
- Department of Electronics Engineering, Saintgits College of Engineering, Kottayam, India
| | - Sania Thomas
- Department of Computer Science and Engineering, Saintgits College of Engineering, Kottayam, India
| | - M Subramoniam
- Department of Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
| | - J Arun
- Centre for Waste Management-International Research Centre, Sathyabama Institute of Science and Technology, Chennai, 600119, India
| | - S Naveen
- Department of Automobile Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India
| | - S Madhu
- Department of Automobile Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.
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Karimi A, Kalhor A, Sadeghi Tabrizi M. Forward layer-wise learning of convolutional neural networks through separation index maximizing. Sci Rep 2024; 14:8576. [PMID: 38615041 DOI: 10.1038/s41598-024-59176-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 04/08/2024] [Indexed: 04/15/2024] Open
Abstract
This paper proposes a forward layer-wise learning algorithm for CNNs in classification problems. The algorithm utilizes the Separation Index (SI) as a supervised complexity measure to evaluate and train each layer in a forward manner. The proposed method explains that gradually increasing the SI through layers reduces the input data's uncertainties and disturbances, achieving a better feature space representation. Hence, by approximating the SI with a variant of local triplet loss at each layer, a gradient-based learning algorithm is suggested to maximize it. Inspired by the NGRAD (Neural Gradient Representation by Activity Differences) hypothesis, the proposed algorithm operates in a forward manner without explicit error information from the last layer. The algorithm's performance is evaluated on image classification tasks using VGG16, VGG19, AlexNet, and LeNet architectures with CIFAR-10, CIFAR-100, Raabin-WBC, and Fashion-MNIST datasets. Additionally, the experiments are applied to text classification tasks using the DBPedia and AG's News datasets. The results demonstrate that the proposed layer-wise learning algorithm outperforms state-of-the-art methods in accuracy and time complexity.
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Affiliation(s)
- Ali Karimi
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Ahmad Kalhor
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Melika Sadeghi Tabrizi
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
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Chowa SS, Azam S, Montaha S, Payel IJ, Bhuiyan MRI, Hasan MZ, Jonkman M. Graph neural network-based breast cancer diagnosis using ultrasound images with optimized graph construction integrating the medically significant features. J Cancer Res Clin Oncol 2023; 149:18039-18064. [PMID: 37982829 PMCID: PMC10725367 DOI: 10.1007/s00432-023-05464-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 10/06/2023] [Indexed: 11/21/2023]
Abstract
PURPOSE An automated computerized approach can aid radiologists in the early diagnosis of breast cancer. In this study, a novel method is proposed for classifying breast tumors into benign and malignant, based on the ultrasound images through a Graph Neural Network (GNN) model utilizing clinically significant features. METHOD Ten informative features are extracted from the region of interest (ROI), based on the radiologists' diagnosis markers. The significance of the features is evaluated using density plot and T test statistical analysis method. A feature table is generated where each row represents individual image, considered as node, and the edges between the nodes are denoted by calculating the Spearman correlation coefficient. A graph dataset is generated and fed into the GNN model. The model is configured through ablation study and Bayesian optimization. The optimized model is then evaluated with different correlation thresholds for getting the highest performance with a shallow graph. The performance consistency is validated with k-fold cross validation. The impact of utilizing ROIs and handcrafted features for breast tumor classification is evaluated by comparing the model's performance with Histogram of Oriented Gradients (HOG) descriptor features from the entire ultrasound image. Lastly, a clustering-based analysis is performed to generate a new filtered graph, considering weak and strong relationships of the nodes, based on the similarities. RESULTS The results indicate that with a threshold value of 0.95, the GNN model achieves the highest test accuracy of 99.48%, precision and recall of 100%, and F1 score of 99.28%, reducing the number of edges by 85.5%. The GNN model's performance is 86.91%, considering no threshold value for the graph generated from HOG descriptor features. Different threshold values for the Spearman's correlation score are experimented with and the performance is compared. No significant differences are observed between the previous graph and the filtered graph. CONCLUSION The proposed approach might aid the radiologists in effective diagnosing and learning tumor pattern of breast cancer.
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Affiliation(s)
- Sadia Sultana Chowa
- Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia
| | - Sami Azam
- Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia.
| | - Sidratul Montaha
- Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia
| | - Israt Jahan Payel
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1216, Bangladesh
| | - Md Rahad Islam Bhuiyan
- Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia
| | - Md Zahid Hasan
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1216, Bangladesh
| | - Mirjam Jonkman
- Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia
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Alam T, Shia WC, Hsu FR, Hassan T. Improving Breast Cancer Detection and Diagnosis through Semantic Segmentation Using the Unet3+ Deep Learning Framework. Biomedicines 2023; 11:1536. [PMID: 37371631 DOI: 10.3390/biomedicines11061536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 05/23/2023] [Accepted: 05/24/2023] [Indexed: 06/29/2023] Open
Abstract
We present an analysis and evaluation of breast cancer detection and diagnosis using segmentation models. We used an advanced semantic segmentation method and a deep convolutional neural network to identify the Breast Imaging Reporting and Data System (BI-RADS) lexicon for breast ultrasound images. To improve the segmentation results, we used six models to analyse 309 patients, including 151 benign and 158 malignant tumour images. We compared the Unet3+ architecture with several other models, such as FCN, Unet, SegNet, DeeplabV3+ and pspNet. The Unet3+ model is a state-of-the-art, semantic segmentation architecture that showed optimal performance with an average accuracy of 82.53% and an average intersection over union (IU) of 52.57%. The weighted IU was found to be 89.14% with a global accuracy of 90.99%. The application of these types of segmentation models to the detection and diagnosis of breast cancer provides remarkable results. Our proposed method has the potential to provide a more accurate and objective diagnosis of breast cancer, leading to improved patient outcomes.
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Affiliation(s)
- Taukir Alam
- Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407, Taiwan
| | - Wei-Chung Shia
- Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407, Taiwan
- Molecular Medicine Laboratory, Department of Research, Changhua Christian Hospital, Changhua 500, Taiwan
| | - Fang-Rong Hsu
- Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407, Taiwan
| | - Taimoor Hassan
- Institute of Translational Medicine and New Drug Development, China Medical University, Taichung 404333, Taiwan
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Gu Y, Xu W, Liu T, An X, Tian J, Ran H, Ren W, Chang C, Yuan J, Kang C, Deng Y, Wang H, Luo B, Guo S, Zhou Q, Xue E, Zhan W, Zhou Q, Li J, Zhou P, Chen M, Gu Y, Chen W, Zhang Y, Li J, Cong L, Zhu L, Wang H, Jiang Y. Ultrasound-based deep learning in the establishment of a breast lesion risk stratification system: a multicenter study. Eur Radiol 2023; 33:2954-2964. [PMID: 36418619 DOI: 10.1007/s00330-022-09263-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 09/03/2022] [Accepted: 10/22/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVES To establish a breast lesion risk stratification system using ultrasound images to predict breast malignancy and assess Breast Imaging Reporting and Data System (BI-RADS) categories simultaneously. METHODS This multicenter study prospectively collected a dataset of ultrasound images for 5012 patients at thirty-two hospitals from December 2018 to December 2020. A deep learning (DL) model was developed to conduct binary categorization (benign and malignant) and BI-RADS categories (2, 3, 4a, 4b, 4c, and 5) simultaneously. The training set of 4212 patients and the internal test set of 416 patients were from thirty hospitals. The remaining two hospitals with 384 patients were used as an external test set. Three experienced radiologists performed a reader study on 324 patients randomly selected from the test sets. We compared the performance of the DL model with that of three radiologists and the consensus of the three radiologists. RESULTS In the external test set, the DL model achieved areas under the receiver operating characteristic curve (AUCs) of 0.980 and 0.945 for the binary categorization and six-way categorizations, respectively. In the reader study set, the DL BI-RADS categories achieved a similar AUC (0.901 vs. 0.933, p = 0.0632), sensitivity (90.98% vs. 95.90%, p = 0.1094), and accuracy (83.33% vs. 79.01%, p = 0.0541), but higher specificity (78.71% vs. 68.81%, p = 0.0012) than those of the consensus of the three radiologists. CONCLUSIONS The DL model performed well in distinguishing benign from malignant breast lesions and yielded outcomes similar to experienced radiologists. This indicates the potential applicability of the DL model in clinical diagnosis. KEY POINTS • The DL model can achieve binary categorization for benign and malignant breast lesions and six-way BI-RADS categorizations for categories 2, 3, 4a, 4b, 4c, and 5, simultaneously. • The DL model showed acceptable agreement with radiologists for the classification of breast lesions. • The DL model performed well in distinguishing benign from malignant breast lesions and had promise in helping reduce unnecessary biopsies of BI-RADS 4a lesions.
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Affiliation(s)
- Yang Gu
- Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1 Shuai Fu Yuan, Dong Cheng District, Beijing, 100730, China
| | - Wen Xu
- Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1 Shuai Fu Yuan, Dong Cheng District, Beijing, 100730, China
| | - Ting Liu
- Department of Medical Imaging Advanced Research, Beijing Research Institute, Shenzhen Mindray Bio-Medical Electronics Co., Ltd., Beijing, China
| | - Xing An
- Department of Medical Imaging Advanced Research, Beijing Research Institute, Shenzhen Mindray Bio-Medical Electronics Co., Ltd., Beijing, China
| | - Jiawei Tian
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Haitao Ran
- Department of Ultrasound, The Second Affiliated Hospital of Chongqing Medical University & Chongqing Key Laboratory of Ultrasound Molecular Imaging, Chongqing, China
| | - Weidong Ren
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China
| | - Cai Chang
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jianjun Yuan
- Department of Ultrasonography, Henan Provincial People's Hospital, Zhengzhou, China
| | - Chunsong Kang
- Department of Ultrasound, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan, China
| | - Youbin Deng
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Hui Wang
- Department of Ultrasound, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Baoming Luo
- Department of Ultrasound, The Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Shenglan Guo
- Department of Ultrasonography, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Qi Zhou
- Department of Medical Ultrasound, The Second Affiliated Hospital, School of Medicine, Xi'an Jiaotong University, Xi'an, China
| | - Ensheng Xue
- Department of Ultrasound, Union Hospital of Fujian Medical University, Fujian Institute of Ultrasound Medicine, Fuzhou, China
| | - Weiwei Zhan
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China
| | - Qing Zhou
- Department of Ultrasonography, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jie Li
- Department of Ultrasound, Qilu Hospital, Shandong University, Jinan, China
| | - Ping Zhou
- Department of Ultrasound, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Man Chen
- Department of Ultrasound Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ying Gu
- Department of Ultrasonography, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Wu Chen
- Department of Ultrasound, The First Hospital of Shanxi Medical University, Taiyuan, China
| | - Yuhong Zhang
- Department of Ultrasound, The Second Hospital of Dalian Medical University, Dalian, China
| | - Jianchu Li
- Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1 Shuai Fu Yuan, Dong Cheng District, Beijing, 100730, China
| | - Longfei Cong
- Department of Medical Imaging Advanced Research, Beijing Research Institute, Shenzhen Mindray Bio-Medical Electronics Co., Ltd., Beijing, China
| | - Lei Zhu
- Department of Medical Imaging Advanced Research, Shenzhen Mindray Bio-Medical Electronics Co., Ltd., Shenzhen, China
| | - Hongyan Wang
- Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1 Shuai Fu Yuan, Dong Cheng District, Beijing, 100730, China.
| | - Yuxin Jiang
- Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1 Shuai Fu Yuan, Dong Cheng District, Beijing, 100730, China.
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Artificial Intelligence in Breast Ultrasound: From Diagnosis to Prognosis-A Rapid Review. Diagnostics (Basel) 2022; 13:diagnostics13010058. [PMID: 36611350 PMCID: PMC9818181 DOI: 10.3390/diagnostics13010058] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Ultrasound (US) is a fundamental diagnostic tool in breast imaging. However, US remains an operator-dependent examination. Research into and the application of artificial intelligence (AI) in breast US are increasing. The aim of this rapid review was to assess the current development of US-based artificial intelligence in the field of breast cancer. METHODS Two investigators with experience in medical research performed literature searching and data extraction on PubMed. The studies included in this rapid review evaluated the role of artificial intelligence concerning BC diagnosis, prognosis, molecular subtypes of breast cancer, axillary lymph node status, and the response to neoadjuvant chemotherapy. The mean values of sensitivity, specificity, and AUC were calculated for the main study categories with a meta-analytical approach. RESULTS A total of 58 main studies, all published after 2017, were included. Only 9/58 studies were prospective (15.5%); 13/58 studies (22.4%) used an ML approach. The vast majority (77.6%) used DL systems. Most studies were conducted for the diagnosis or classification of BC (55.1%). At present, all the included studies showed that AI has excellent performance in breast cancer diagnosis, prognosis, and treatment strategy. CONCLUSIONS US-based AI has great potential and research value in the field of breast cancer diagnosis, treatment, and prognosis. More prospective and multicenter studies are needed to assess the potential impact of AI in breast ultrasound.
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Zhao Z, Hou S, Li S, Sheng D, Liu Q, Chang C, Chen J, Li J. Application of Deep Learning to Reduce the Rate of Malignancy Among BI-RADS 4A Breast Lesions Based on Ultrasonography. ULTRASOUND IN MEDICINE & BIOLOGY 2022; 48:2267-2275. [PMID: 36055860 DOI: 10.1016/j.ultrasmedbio.2022.06.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/31/2022] [Accepted: 06/24/2022] [Indexed: 06/15/2023]
Abstract
The aim of the work described here was to develop an ultrasound (US) image-based deep learning model to reduce the rate of malignancy among breast lesions diagnosed as category 4A of the Breast Imaging-Reporting and Data System (BI-RADS) during the pre-operative US examination. A total of 479 breast lesions diagnosed as BI-RADS 4A in pre-operative US examination were enrolled. There were 362 benign lesions and 117 malignant lesions confirmed by postoperative pathology with a malignancy rate of 24.4%. US images were collected from the database server. They were then randomly divided into training and testing cohorts at a ratio of 4:1. To correctly classify malignant and benign tumors diagnosed as BI-RADS 4A in US, four deep learning models, including MobileNet, DenseNet121, Xception and Inception V3, were developed. The performance of deep learning models was compared using the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Meanwhile, the robustness of the models was evaluated by five-fold cross-validation. Among the four models, the MobileNet model turned to be the optimal model with the best performance in classifying benign and malignant lesions among BI-RADS 4A breast lesions. The AUROC, accuracy, sensitivity, specificity, PPV and NPV of the optimal model in the testing cohort were 0.897, 0.913, 0.926, 0.899, 0.958 and 0.784, respectively. About 14.4% of patients were expected to be upgraded to BI-RADS 4B in US with the assistance of the MobileNet model. The deep learning model MobileNet can help to reduce the rate of malignancy among BI-RADS 4A breast lesions in pre-operative US examinations, which is valuable to clinicians in tailoring treatment for suspicious breast lesions identified on US.
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Affiliation(s)
- Zhijin Zhao
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Size Hou
- Department of Applied Mathematics, School of Science, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Shuang Li
- International Business School Suzhou, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Danli Sheng
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qi Liu
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Cai Chang
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jiangang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, Shanghai, China; Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China.
| | - Jiawei Li
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
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Shia WC, Hsu FR, Dai ST, Guo SL, Chen DR. Semantic Segmentation of the Malignant Breast Imaging Reporting and Data System Lexicon on Breast Ultrasound Images by Using DeepLab v3. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22145352. [PMID: 35891030 PMCID: PMC9323504 DOI: 10.3390/s22145352] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/14/2022] [Accepted: 07/15/2022] [Indexed: 05/26/2023]
Abstract
In this study, an advanced semantic segmentation method and deep convolutional neural network was applied to identify the Breast Imaging Reporting and Data System (BI-RADS) lexicon for breast ultrasound images, thereby facilitating image interpretation and diagnosis by providing radiologists an objective second opinion. A total of 684 images (380 benign and 308 malignant tumours) from 343 patients (190 benign and 153 malignant breast tumour patients) were analysed in this study. Six malignancy-related standardised BI-RADS features were selected after analysis. The DeepLab v3+ architecture and four decode networks were used, and their semantic segmentation performance was evaluated and compared. Subsequently, DeepLab v3+ with the ResNet-50 decoder showed the best performance in semantic segmentation, with a mean accuracy and mean intersection over union (IU) of 44.04% and 34.92%, respectively. The weighted IU was 84.36%. For the diagnostic performance, the area under the curve was 83.32%. This study aimed to automate identification of the malignant BI-RADS lexicon on breast ultrasound images to facilitate diagnosis and improve its quality. The evaluation showed that DeepLab v3+ with the ResNet-50 decoder was suitable for solving this problem, offering a better balance of performance and computational resource usage than a fully connected network and other decoders.
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Affiliation(s)
- Wei-Chung Shia
- Molecular Medicine Laboratory, Department of Research, Changhua Christian Hospital, Changhua 500, Taiwan
- Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407, Taiwan; (F.-R.H.); (S.-T.D.)
| | - Fang-Rong Hsu
- Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407, Taiwan; (F.-R.H.); (S.-T.D.)
| | - Seng-Tong Dai
- Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407, Taiwan; (F.-R.H.); (S.-T.D.)
| | - Shih-Lin Guo
- Comprehensive Breast Cancer Center, Changhua Christian Hospital, Changhua 500, Taiwan;
| | - Dar-Ren Chen
- Comprehensive Breast Cancer Center, Changhua Christian Hospital, Changhua 500, Taiwan;
- School of Medicine, Chung Shan Medical University, Taichung 402, Taiwan
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Kumar P, Kumar A, Srivastava S, Padma Sai Y. A novel bi-modal extended Huber loss function based refined mask RCNN approach for automatic multi instance detection and localization of breast cancer. Proc Inst Mech Eng H 2022; 236:1036-1053. [DOI: 10.1177/09544119221095416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Breast cancer is an extremely aggressive cancer in women. Its abnormalities can be observed in the form of masses, calcification and lumps. In order to reduce the mortality rate of women its detection is needed at an early stage. The present paper proposes a novel bi-modal extended Huber loss function based refined mask regional convolutional neural network for automatic multi-instance detection and localization of breast cancer. To refine and increase the efficacy of the proposed method three changes are casted. First, a pre-processing step is performed for mammogram and ultrasound breast images. Second, the features of the region proposal network are separately mapped for accurate region of interest. Third, to reduce overfitting and fast convergence, an extended Huber loss function is used at the place of Smooth L1( x) in boundary loss. To extend the functionality of Huber loss, the delta parameter is automated by the aid of median absolute deviation with grid search algorithm. It provides the best optimum value of delta instead of user-based value. The proposed method is compared with pre-existing methods in terms of accuracy, true positive rate, true negative rate, precision, F-score, balanced classification rate, Youden’s index, Jaccard Index and dice coefficient on CBIS-DDSM and ultrasound database. The experimental result shows that the proposed method is a better suited approach for multi-instance detection, localization and classification of breast cancer. It can be used as a diagnostic medium that helps in clinical purposes and leads to a precise diagnosis of breast cancer abnormalities.
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Affiliation(s)
- Pradeep Kumar
- Department of Electronics and Communication Engineering, National Institute of Technology Patna, Patna, Bihar, India
| | - Abhinav Kumar
- Department of Electronics and Communication Engineering, National Institute of Technology Patna, Patna, Bihar, India
| | - Subodh Srivastava
- Department of Electronics and Communication Engineering, National Institute of Technology Patna, Patna, Bihar, India
| | - Yarlagadda Padma Sai
- Department of Electronics and Communication Engineering, VNR VJIET, Hyderabad, Telangana, India
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Belhaj Soulami K, Kaabouch N, Nabil Saidi M. Breast cancer: Classification of suspicious regions in digital mammograms based on capsule network. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103696] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Abstract
Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed.
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Hsieh YH, Hsu FR, Dai ST, Huang HY, Chen DR, Shia WC. Incorporating the Breast Imaging Reporting and Data System Lexicon with a Fully Convolutional Network for Malignancy Detection on Breast Ultrasound. Diagnostics (Basel) 2021; 12:66. [PMID: 35054233 PMCID: PMC8774546 DOI: 10.3390/diagnostics12010066] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 12/21/2021] [Accepted: 12/25/2021] [Indexed: 11/16/2022] Open
Abstract
In this study, we applied semantic segmentation using a fully convolutional deep learning network to identify characteristics of the Breast Imaging Reporting and Data System (BI-RADS) lexicon from breast ultrasound images to facilitate clinical malignancy tumor classification. Among 378 images (204 benign and 174 malignant images) from 189 patients (102 benign breast tumor patients and 87 malignant patients), we identified seven malignant characteristics related to the BI-RADS lexicon in breast ultrasound. The mean accuracy and mean IU of the semantic segmentation were 32.82% and 28.88, respectively. The weighted intersection over union was 85.35%, and the area under the curve was 89.47%, showing better performance than similar semantic segmentation networks, SegNet and U-Net, in the same dataset. Our results suggest that the utilization of a deep learning network in combination with the BI-RADS lexicon can be an important supplemental tool when using ultrasound to diagnose breast malignancy.
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Affiliation(s)
- Yung-Hsien Hsieh
- Department of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, Taiwan; (Y.-H.H.); (F.-R.H.); (S.-T.D.)
| | - Fang-Rong Hsu
- Department of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, Taiwan; (Y.-H.H.); (F.-R.H.); (S.-T.D.)
| | - Seng-Tong Dai
- Department of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, Taiwan; (Y.-H.H.); (F.-R.H.); (S.-T.D.)
| | - Hsin-Ya Huang
- Comprehensive Breast Cancer Center, Changhua Christian Hospital, Changhua 500, Taiwan;
| | - Dar-Ren Chen
- Comprehensive Breast Cancer Center, Changhua Christian Hospital, Changhua 500, Taiwan;
- School of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
| | - Wei-Chung Shia
- Department of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, Taiwan; (Y.-H.H.); (F.-R.H.); (S.-T.D.)
- Molecular Medicine Laboratory, Department of Research, Changhua Christian Hospital, Changhua 500, Taiwan
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Irfan R, Almazroi AA, Rauf HT, Damaševičius R, Nasr EA, Abdelgawad AE. Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion. Diagnostics (Basel) 2021; 11:1212. [PMID: 34359295 PMCID: PMC8304124 DOI: 10.3390/diagnostics11071212] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 04/16/2021] [Accepted: 04/27/2021] [Indexed: 12/15/2022] Open
Abstract
Breast cancer is becoming more dangerous by the day. The death rate in developing countries is rapidly increasing. As a result, early detection of breast cancer is critical, leading to a lower death rate. Several researchers have worked on breast cancer segmentation and classification using various imaging modalities. The ultrasonic imaging modality is one of the most cost-effective imaging techniques, with a higher sensitivity for diagnosis. The proposed study segments ultrasonic breast lesion images using a Dilated Semantic Segmentation Network (Di-CNN) combined with a morphological erosion operation. For feature extraction, we used the deep neural network DenseNet201 with transfer learning. We propose a 24-layer CNN that uses transfer learning-based feature extraction to further validate and ensure the enriched features with target intensity. To classify the nodules, the feature vectors obtained from DenseNet201 and the 24-layer CNN were fused using parallel fusion. The proposed methods were evaluated using a 10-fold cross-validation on various vector combinations. The accuracy of CNN-activated feature vectors and DenseNet201-activated feature vectors combined with the Support Vector Machine (SVM) classifier was 90.11 percent and 98.45 percent, respectively. With 98.9 percent accuracy, the fused version of the feature vector with SVM outperformed other algorithms. When compared to recent algorithms, the proposed algorithm achieves a better breast cancer diagnosis rate.
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Affiliation(s)
- Rizwana Irfan
- Department of Information Technology, College of Computing and Information Technology at Khulais, University of Jeddah, Jeddah 21959, Saudi Arabia; (R.I.); (A.A.A.)
| | - Abdulwahab Ali Almazroi
- Department of Information Technology, College of Computing and Information Technology at Khulais, University of Jeddah, Jeddah 21959, Saudi Arabia; (R.I.); (A.A.A.)
| | - Hafiz Tayyab Rauf
- Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
| | - Robertas Damaševičius
- Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland;
| | - Emad Abouel Nasr
- Industrial Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia; (E.A.N.); (A.E.A.)
| | - Abdelatty E. Abdelgawad
- Industrial Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia; (E.A.N.); (A.E.A.)
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