Deep learning in mammography images segmentation and classification: Automated CNN approach
In this work, a new framework for breast cancer image segmentation and classification is proposed. Different models including InceptionV3, DenseNet121, ResNet50, VGG16 and MobileNetV2 models, are applied to classify Mammographic Image Analysis Society (MIAS), Digital Database for Screening Mammograp...
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Format: | Article |
Language: | English |
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Elsevier
2021-10-01
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Series: | Alexandria Engineering Journal |
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Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016821002027 |
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author | Wessam M. Salama Moustafa H. Aly |
author_facet | Wessam M. Salama Moustafa H. Aly |
author_sort | Wessam M. Salama |
collection | DOAJ |
description | In this work, a new framework for breast cancer image segmentation and classification is proposed. Different models including InceptionV3, DenseNet121, ResNet50, VGG16 and MobileNetV2 models, are applied to classify Mammographic Image Analysis Society (MIAS), Digital Database for Screening Mammography (DDSM) and the Curated Breast Imaging Subset of DDSM (CBIS-DDSM) into benign and malignant. Moreover, the trained modified U-Net model is utilized to segment breast area from the mammogram images. This method will aid as a radiologist's assistant in early detection and improve the efficiency of our system. The Cranio Caudal (CC) vision and Mediolateral Oblique (MLO) view are widely used for the identification and diagnosis of breast cancer. The accuracy of breast cancer diagnosis will be improved as the number of views is increased. Our proposed frame work is based on MLO view and CC view to enhance the system performance. In addition, the lack of tagged data is a big challenge. Transfer learning and data augmentation are applied to overcome this problem. Three mammographic datasets; MIAS, DDSM and CBIS-DDSM, are utilized in our evaluation. End-to-end fully convolutional neural networks (CNNs) are introduced in this paper. The proposed technique of applying data augmentation with modified U-Net model and InceptionV3 achieves the best result, specifically with the DDSM dataset. This achieves 98.87% accuracy, 98.88% area under the curve (AUC), 98.98% sensitivity, 98.79% precision, 97.99% F1 score, and a computational time of 1.2134 s on DDSM datasets. |
first_indexed | 2024-12-24T04:41:53Z |
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id | doaj.art-3edf7e99ba4141eb9977fe3ff3c8ed0f |
institution | Directory Open Access Journal |
issn | 1110-0168 |
language | English |
last_indexed | 2024-12-24T04:41:53Z |
publishDate | 2021-10-01 |
publisher | Elsevier |
record_format | Article |
series | Alexandria Engineering Journal |
spelling | doaj.art-3edf7e99ba4141eb9977fe3ff3c8ed0f2022-12-21T17:14:48ZengElsevierAlexandria Engineering Journal1110-01682021-10-0160547014709Deep learning in mammography images segmentation and classification: Automated CNN approachWessam M. Salama0Moustafa H. Aly1Department of Basic Science, Faculty of Engineering, Pharos University, Alexandria, Egypt; Corresponding author at: Department of Electronics and Communications, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt.Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria, EgyptIn this work, a new framework for breast cancer image segmentation and classification is proposed. Different models including InceptionV3, DenseNet121, ResNet50, VGG16 and MobileNetV2 models, are applied to classify Mammographic Image Analysis Society (MIAS), Digital Database for Screening Mammography (DDSM) and the Curated Breast Imaging Subset of DDSM (CBIS-DDSM) into benign and malignant. Moreover, the trained modified U-Net model is utilized to segment breast area from the mammogram images. This method will aid as a radiologist's assistant in early detection and improve the efficiency of our system. The Cranio Caudal (CC) vision and Mediolateral Oblique (MLO) view are widely used for the identification and diagnosis of breast cancer. The accuracy of breast cancer diagnosis will be improved as the number of views is increased. Our proposed frame work is based on MLO view and CC view to enhance the system performance. In addition, the lack of tagged data is a big challenge. Transfer learning and data augmentation are applied to overcome this problem. Three mammographic datasets; MIAS, DDSM and CBIS-DDSM, are utilized in our evaluation. End-to-end fully convolutional neural networks (CNNs) are introduced in this paper. The proposed technique of applying data augmentation with modified U-Net model and InceptionV3 achieves the best result, specifically with the DDSM dataset. This achieves 98.87% accuracy, 98.88% area under the curve (AUC), 98.98% sensitivity, 98.79% precision, 97.99% F1 score, and a computational time of 1.2134 s on DDSM datasets.http://www.sciencedirect.com/science/article/pii/S1110016821002027MammographyBreast cancerSegmentationDeep learningU-NetTransfer learning |
spellingShingle | Wessam M. Salama Moustafa H. Aly Deep learning in mammography images segmentation and classification: Automated CNN approach Alexandria Engineering Journal Mammography Breast cancer Segmentation Deep learning U-Net Transfer learning |
title | Deep learning in mammography images segmentation and classification: Automated CNN approach |
title_full | Deep learning in mammography images segmentation and classification: Automated CNN approach |
title_fullStr | Deep learning in mammography images segmentation and classification: Automated CNN approach |
title_full_unstemmed | Deep learning in mammography images segmentation and classification: Automated CNN approach |
title_short | Deep learning in mammography images segmentation and classification: Automated CNN approach |
title_sort | deep learning in mammography images segmentation and classification automated cnn approach |
topic | Mammography Breast cancer Segmentation Deep learning U-Net Transfer learning |
url | http://www.sciencedirect.com/science/article/pii/S1110016821002027 |
work_keys_str_mv | AT wessammsalama deeplearninginmammographyimagessegmentationandclassificationautomatedcnnapproach AT moustafahaly deeplearninginmammographyimagessegmentationandclassificationautomatedcnnapproach |