Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms
Abstract
Background and objective Deep learning detection and classification from medical imagery are key components for computer-aided diagnosis (CAD) systems to efficiently support physicians leading to an accurate diagnosis of breast lesions. Methods In this study, an integrated CAD system of deep learning detection and classification is proposed aiming to improve the diagnostic performance of breast lesions. First, a deep learning YOLO detector is adopted and evaluated for breast lesion detection from entire mammograms. Then, three deep learning classifiers, namely regular feedforward CNN, ResNet-50, and InceptionResNet-V2, are modified and evaluated for breast lesion classification. The proposed deep learning system is evaluated over 5-fold cross-validation tests using two different and widely used databases of digital X-ray mammograms: DDSM and INbreast. Results The evaluation results of breast lesion detection show the capability of the YOLO detector to achieve overall detection accuracies of 99.17% and 97.27% and F1-scores of 99.28% and 98.02% for DDSM and INbreast datasets, respectively. Meanwhile, the YOLO detector could predict 71 frames per second (FPS) at the testing time for both DDSM and INbreast datasets. Using detected breast lesions, the classification models of CNN, ResNet-50, and InceptionResNet-V2 achieve promising average overall accuracies of 94.50%, 95.83%, and 97.50%, respectively, for the DDSM dataset and 88.74%, 92.55%, and 95.32%, respectively, for the INbr…
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Al-Antari et al. [94] achieved an accuracy of 0.985 on CBIS-DDSM and DDSM using original X-ray mammographic data.
“| Al-Antari et al. [94] | Original X-ray mammographic data | X-ray | CBIS-DDSM and DDSM | 0.985 (ACC) |”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [94]
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A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
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Self-citation
- Authors
- Al-antari, M. A., Han, S.-M., & Kim, T.-S.
- Affiliations
- Kyung Hee University (KR)
- Corresponding author
- Kim, Tae-Seong
- ORCID
- Kim, Tae-Seong
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 196
- Pages
- 105584
- ISSN
- 0169-2607 (ISSN-L)
- OpenAlex
- W3033750579
- Cited by
- 264
- References
- 43
- Citations by year
- 2026: 12 · 2025: 34 · 2024: 43 · 2023: 65 · 2022: 61 · 2021: 38 · 2020: 11
- FWCI
- 17.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computer Methods and Programs in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Korean government (NRF-2019R1A2C1003713)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Digital Radiography and Breast Imaging
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- breast lesion detection, breast lesion classification, computer-aided diagnosis, digital mammography, YOLO, ResNet50, InceptionResNetV2, convolutional neural networks, breast cancer diagnosis, DDSM, INbreast
- MeSH terms
- Machine Learning, Deep Learning, Breast Neoplasms, Computers, Humans, Mammography, X-Rays, Neural Networks, Computer
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