Advancing breast cancer diagnosis: Integrating deep transfer learning and U-Net segmentation for precise classification and delineation of ultrasound images
Breast cancer remains one of the leading causes of mortality among women worldwide, highlighting the need for timely and accurate diagnostic strategies. This study investigates the integration of artificial intelligence (AI) techniques, specifically deep transfer learning for classification and U-Ne...
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Elsevier
2025-06-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025011223 |
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| author | Divine Senanu Ametefe Dah John Abdulmalik Adozuka Aliu George Dzorgbenya Ametefe Aisha Hamid Tumani Darboe |
| author_facet | Divine Senanu Ametefe Dah John Abdulmalik Adozuka Aliu George Dzorgbenya Ametefe Aisha Hamid Tumani Darboe |
| author_sort | Divine Senanu Ametefe |
| collection | DOAJ |
| description | Breast cancer remains one of the leading causes of mortality among women worldwide, highlighting the need for timely and accurate diagnostic strategies. This study investigates the integration of artificial intelligence (AI) techniques, specifically deep transfer learning for classification and U-Net for segmentation to improve breast cancer diagnosis using ultrasound imaging. A curated dataset of breast ultrasound images, categorized as normal, benign, or malignant, was used for model evaluation. Three pre-trained convolutional neural networks (CNNs), including VGG16, VGG19, and EfficientNet were implemented within a deep transfer learning framework due to their strong feature extraction capabilities. In parallel, the U-Net model, recognized for its effectiveness in medical image segmentation, was employed to delineate tumour boundaries with high spatial precision. Among the CNN models, VGG19 achieved the best performance, with the highest weighted accuracy, precision, and recall. U-Net attained an average Dice Similarity Coefficient of 85.97 %, underscoring its proficiency in segmenting tumour regions across varying lesion types. These AI-based models offer a robust diagnostic pipeline that improves lesion localization, reduces interobserver variability, and supports clinical decision-making. The approach aligns with Sustainable Development Goal (SDG) 3 by promoting early detection and better health outcomes, and SDG 9 through the adoption of innovative AI technologies in healthcare. However, limitations persist, including computational demands, class imbalance, and the lack of dataset diversity, which may affect generalizability. Addressing these challenges is essential for the safe and effective deployment of AI in real-world clinical settings. |
| format | Article |
| id | doaj-art-c7ea6413b28b4bf39bd33132c67cd758 |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-c7ea6413b28b4bf39bd33132c67cd7582025-08-20T03:13:49ZengElsevierResults in Engineering2590-12302025-06-012610504710.1016/j.rineng.2025.105047Advancing breast cancer diagnosis: Integrating deep transfer learning and U-Net segmentation for precise classification and delineation of ultrasound imagesDivine Senanu Ametefe0Dah John1Abdulmalik Adozuka Aliu2George Dzorgbenya Ametefe3Aisha Hamid4Tumani Darboe5Wireless Communication Technology Group (WiCOT), College of Engineering, School of Electrical Engineering, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor, MalaysiaCollege of Computing, Informatics, and Mathematics, Universiti Teknologi MARA (UiTM), 40150 Puncak Perdana, Selangor, MalaysiaCollege of Built Environment, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor, MalaysiaDepartment of Biotechnology, College of Science, Engineering and Technology, Osun State University, Osogbo, NigeriaSchool of Chemistry and Environment, Faculty of Applied Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, MalaysiaSchool of Business and Public Administration, University of The Gambia, Kanifing Campus, West Coast Region, The Gambia; Corresponding author.Breast cancer remains one of the leading causes of mortality among women worldwide, highlighting the need for timely and accurate diagnostic strategies. This study investigates the integration of artificial intelligence (AI) techniques, specifically deep transfer learning for classification and U-Net for segmentation to improve breast cancer diagnosis using ultrasound imaging. A curated dataset of breast ultrasound images, categorized as normal, benign, or malignant, was used for model evaluation. Three pre-trained convolutional neural networks (CNNs), including VGG16, VGG19, and EfficientNet were implemented within a deep transfer learning framework due to their strong feature extraction capabilities. In parallel, the U-Net model, recognized for its effectiveness in medical image segmentation, was employed to delineate tumour boundaries with high spatial precision. Among the CNN models, VGG19 achieved the best performance, with the highest weighted accuracy, precision, and recall. U-Net attained an average Dice Similarity Coefficient of 85.97 %, underscoring its proficiency in segmenting tumour regions across varying lesion types. These AI-based models offer a robust diagnostic pipeline that improves lesion localization, reduces interobserver variability, and supports clinical decision-making. The approach aligns with Sustainable Development Goal (SDG) 3 by promoting early detection and better health outcomes, and SDG 9 through the adoption of innovative AI technologies in healthcare. However, limitations persist, including computational demands, class imbalance, and the lack of dataset diversity, which may affect generalizability. Addressing these challenges is essential for the safe and effective deployment of AI in real-world clinical settings.http://www.sciencedirect.com/science/article/pii/S2590123025011223Breast cancer diagnosisDeep transfer learningU-net segmentationMedical image classificationArtificial intelligence in healthcareConvolutional neural network (CNN) |
| spellingShingle | Divine Senanu Ametefe Dah John Abdulmalik Adozuka Aliu George Dzorgbenya Ametefe Aisha Hamid Tumani Darboe Advancing breast cancer diagnosis: Integrating deep transfer learning and U-Net segmentation for precise classification and delineation of ultrasound images Results in Engineering Breast cancer diagnosis Deep transfer learning U-net segmentation Medical image classification Artificial intelligence in healthcare Convolutional neural network (CNN) |
| title | Advancing breast cancer diagnosis: Integrating deep transfer learning and U-Net segmentation for precise classification and delineation of ultrasound images |
| title_full | Advancing breast cancer diagnosis: Integrating deep transfer learning and U-Net segmentation for precise classification and delineation of ultrasound images |
| title_fullStr | Advancing breast cancer diagnosis: Integrating deep transfer learning and U-Net segmentation for precise classification and delineation of ultrasound images |
| title_full_unstemmed | Advancing breast cancer diagnosis: Integrating deep transfer learning and U-Net segmentation for precise classification and delineation of ultrasound images |
| title_short | Advancing breast cancer diagnosis: Integrating deep transfer learning and U-Net segmentation for precise classification and delineation of ultrasound images |
| title_sort | advancing breast cancer diagnosis integrating deep transfer learning and u net segmentation for precise classification and delineation of ultrasound images |
| topic | Breast cancer diagnosis Deep transfer learning U-net segmentation Medical image classification Artificial intelligence in healthcare Convolutional neural network (CNN) |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025011223 |
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