Highlights
- •We developed a promoted version of InceptionV3 network to classify breast lesions.
- •The main promotion is converting InceptionV3 modules to residual inception modules.
- •Five datasets (3 public and 2 private) were used for training and evaluation.
- •The model were compared with 24 CNN architectures.
- •It achieved the best results in terms of accuracy, AUC, RMSE, Cronbach's α, f1, etc.
Abstract
Purpose
Method
Results
Conclusions
Keywords
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- Cancer statistics, 2021.CA Cancer J Clin. 2021; 71: 7-33https://doi.org/10.3322/CAAC.21654
- Breast ultrasound region of interest detection and lesion localisation.Artif Intell Med. 2020; 107: 101880
- Novel techniques for sentinel lymph node biopsy in breast cancer: a systematic review.Lancet Oncol. 2014; 15: e351-e362https://doi.org/10.1016/S1470-2045(13)70590-4
- Preoperative axillary US in early-stage breast cancer: Potential to prevent unnecessary axillary lymph node dissection.Radiology. 2018; 288: 55-63https://doi.org/10.1148/RADIOL.2018171987/ASSET/IMAGES/LARGE/RADIOL.2018171987.TBL4.JPEG
Xueyi Zheng Zhao Yao Yini Huang Yanyan Yu Yun Wang Yubo Liu et al. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer Nat Commun 11 1.
Samantha Bove Maria Colomba Comes Vito Lorusso Cristian Cristofaro Vittorio Didonna Gianluca Gatta et al. A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients Sci Rep 12 1.
- Deep learning-based computer-aided diagnosis in screening breast ultrasound to reduce false-positive diagnoses.Sci Rep. 2021; 11: 1-11
- Discrimination of breast tumors in ultrasonic images using an ensemble classifier based on the AdaBoost algorithm with feature selection.IEEE Trans Med Imaging. 2010; 29: 598-609https://doi.org/10.1109/TMI.2009.2022630
- Artificial intelligence in medicine.Metabolism. 2017; 69S: S36-S40https://doi.org/10.1016/J.METABOL.2017.01.011
- Overview of radiomics in breast cancer diagnosis and prognostication.Breast. 2020; 49: 74-80https://doi.org/10.1016/J.BREAST.2019.10.018
- Towards Breast Ultrasound Image Segmentation using Multi-resolution Pixel Descriptors.in: Proc 21st Int Conf Pattern Recognit. IEEE, 2012: 2833-2836
Sadek I, Elawady M, Stefanovski V. Automated Breast Lesion Segmentation in Ultrasound Images 2016. doi:10.48550/arxiv.1609.08364.
Shi X, Cheng HD, Hu L. MASS DETECTION AND CLASSIFICATION IN BREAST ULTRASOUND IMAGES USING FUZZY SVM. Proc 9th Jt Conf Inf Sci JCIS 2006 2006;2006:253–6. doi:10.2991/JCIS.2006.257.
- Automated breast cancer diagnosis based on machine learning algorithms.J Healthc Eng. 2019; 2019: 1-11
- Classification of benign and malignant breast masses based on shape and texture features in sonography images.J Med Syst. 2012; 36: 1621-1627https://doi.org/10.1007/S10916-010-9624-7
- Computer-aided diagnosis of glaucoma using fundus images: a review.Comput Methods Programs Biomed. 2018; 165: 1-12
- Deep learning versus traditional machine learning methods for aggregated energy demand prediction.IEEE PES Innov Smart Grid Technol Conf Eur. 2017; 2017: 1-6
- Computer-aided classification of breast masses using speckle features of automated breast ultrasound images.Med Phys. 2012; 39: 6465-6473https://doi.org/10.1118/1.4754801
- Improving classification performance of breast lesions on ultrasonography.Pattern Recognit. 2015; 48: 1125-1136https://doi.org/10.1016/J.PATCOG.2014.06.006
- Classification of breast lesions using segmented quantitative ultrasound maps of homodyned K distribution parameters.Med Phys. 2016; 43: 5561-5569
- Ultrasound RF time series for classification of breast lesions.IEEE Trans Med Imaging. 2015; 34: 652-661
- The MAGIC-5 project: medical applications on a grid infrastructure connection.IEEE Nucl Sci Symp Conf Rec. 2004; 3: 1902-1906https://doi.org/10.1109/NSSMIC.2004.1462616
- Current challenges of implementing artificial intelligence in medical imaging.Phys Med. 2022; 100: 12-17https://doi.org/10.1016/j.ejmp.2022.06.003
- A survey on deep learning in medical image analysis.Med Image Anal. 2017; 42: 60-88
- NF-RCNN: Heart localization and right ventricle wall motion abnormality detection in cardiac MRI.Phys Med. 2020; 70: 65-74
- Early prediction of neoadjuvant chemotherapy response by exploiting a transfer learning approach on breast DCE-MRIs.Sci Reports. 2021; 11: 1-12https://doi.org/10.1038/s41598-021-93592-z
- A novel CNN algorithm for pathological complete response prediction using an I-SPY TRIAL breast MRI database.Magn Reson Imaging. 2020; 73: 148-151https://doi.org/10.1016/J.MRI.2020.08.021
- Machine learning framework for automatic image quality evaluation involving a mammographic American College of Radiology phantom.Phys Med. 2022; 102: 1-8
- Deep learning.Nature. 2015; 521: 436-444https://doi.org/10.1038/nature14539
- Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images.Comput Electr Eng. 2018; 70: 871-882https://doi.org/10.1016/J.COMPELECENG.2018.01.033
- Breast cancer classification in automated breast ultrasound using multiview convolutional neural network with transfer learning.Ultrasound Med Biol. 2020; 46: 1119-1132https://doi.org/10.1016/J.ULTRASMEDBIO.2020.01.001
- Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset.Neurocomputing. 2016; 194: 87-94https://doi.org/10.1016/J.NEUCOM.2016.01.074
- An experimental study on breast lesion detection and classification from ultrasound images using deep learning architectures.BMC Med Imaging. 2019; 19: 1-9https://doi.org/10.1186/S12880-019-0349-X/TABLES/2
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the Inception Architecture for Computer Vision. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2015;2016-Decem:2818–26. doi:10.48550/arxiv.1512.00567.
Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size 2016. doi:10.48550/arxiv.1602.07360.
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely Connected Convolutional Networks. Proc - 30th IEEE Conf Comput Vis Pattern Recognition, CVPR 2017 2016;2017-Janua:2261–9. doi:10.48550/arxiv.1608.06993.
Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications 2017. doi:10.48550/arxiv.1704.04861.
Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition. ArXiv:14091556 2014.
Xie S, Girshick R, Dollár P, Tu Z, He K. Aggregated Residual Transformations for Deep Neural Networks. Proc - 30th IEEE Conf Comput Vis Pattern Recognition, CVPR 2017 2016;2017-Janua:5987–95. doi:10.48550/arxiv.1611.05431.
Szegedy C, Ioffe S, Vanhoucke V, Alemi AA. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. 31st AAAI Conf Artif Intell AAAI 2017 2016:4278–84. doi:10.48550/arxiv.1602.07261.
- Deep residual learning for image recognition.IEEE Conf Comput Vis pattern Recognit. 2016; : 770-778
- Dataset of breast ultrasound images.Data Br. 2020; 28: 104863
- Automated breast ultrasound lesions detection using convolutional neural networks.IEEE J Biomed Heal Informatics. 2018; 22: 1218-1226
Stanislav Makhanov. Ultrasound Images 2012. http://onlinemedicalimages.com/index.php/en/site-map.
- A scoping review of transfer learning research on medical image analysis using ImageNet.Comput Biol Med. 2021; 128: 104115
Chollet F. Xception: Deep Learning with Depthwise Separable Convolutions. Proc - 30th IEEE Conf Comput Vis Pattern Recognition, CVPR 2017 2016;2017-Janua:1800–7. doi:10.48550/arxiv.1610.02357.
- How to define end-diastole and end-systole?: Impact of timing on strain measurements.JACC Cardiovasc Imaging. 2015; 8: 148-157https://doi.org/10.1016/J.JCMG.2014.10.010
Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, et al. This paper is included in the Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16). Open access to the Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation is sponsored by US n.d.
Ting KM. Confusion Matrix. Encycl Mach Learn Data Min 2017:260.
- Cronbach’s alpha.Medsurg Nurs. 2014; 20: 1357-1359https://doi.org/10.1007/978-94-007-0753-5_622
Fürnkranz J, Chan PK, Craw S, Sammut C, Uther W, Ratnaparkhi A, et al. Mean Squared Error. Encycl Mach Learn 2011:653. doi:10.1007/978-0-387-30164-8_528.
Breast Cancer: Statistics | Cancer.Net. CancerNet n.d. https://www.cancer.net/cancer-types/breast-cancer/statistics (accessed August 13, 2022).
- Convolutional Neural Networks based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mRMR.Comput Biol Med. 2021; 133104407https://doi.org/10.1016/J.COMPBIOMED.2021.104407
- Breast ultrasound tumor image classification using image decomposition and fusion based on adaptive multi-model spatial feature fusion.Comput Methods Programs Biomed. 2021; 208106221https://doi.org/10.1016/J.CMPB.2021.106221
- A Comparison between the online prediction models cancermath and PREDICT as prognostic tools in thai breast cancer patients.Cancer Manag Res. 2020; 12: 5549-5559https://doi.org/10.2147/CMAR.S258143
Annarita Fanizzi Domenico Pomarico Angelo Paradiso Samantha Bove Sergio Diotaiuti Vittorio Didonna et al. Predicting of Sentinel Lymph Node Status in Breast Cancer Patients with Clinically Negative Nodes: A Validation Study Cancers 13 2 352.
- ImageNet large scale visual recognition challenge.Int J Comput Vis. 2015; 115: 211-252
- Transfer learning of pre-trained Inception-v3 model for colorectal cancer lymph node metastasis classification.IEEE Int Conf Mechatronics Autom. 2018; 2018: 1650-1654
- An efficient technique for CT scan images classification of COVID-19.J Intell Fuzzy Syst. 2021; 40: 5225-5238
- COVID-19 diagnosis from chest X-ray images using convolutional neural network (CNN) and InceptionV3.Int Conf Inf Technol. 2021; 2021: 799-804
- Efficient analysis of diabetic retinopathy on retinal fundus images using deep learning techniques with inception v3 architecture.J Green Eng. 2020; 10: 9615-9625
