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Razali NF, Isa IS, Sulaiman SN, A. Karim NK, Osman MK. CNN-Wavelet scattering textural feature fusion for classifying breast tissue in mammograms. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
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Razali NF, Isa IS, Sulaiman SN, Abdul Karim NK, Osman MK, Che Soh ZH. Enhancement Technique Based on the Breast Density Level for Mammogram for Computer-Aided Diagnosis. Bioengineering (Basel) 2023; 10:153. [PMID: 36829647 PMCID: PMC9952042 DOI: 10.3390/bioengineering10020153] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 01/04/2023] [Accepted: 01/16/2023] [Indexed: 01/26/2023] Open
Abstract
Mass detection in mammograms has a limited approach to the presence of a mass in overlapping denser fibroglandular breast regions. In addition, various breast density levels could decrease the learning system's ability to extract sufficient feature descriptors and may result in lower accuracy performance. Therefore, this study is proposing a textural-based image enhancement technique named Spatial-based Breast Density Enhancement for Mass Detection (SbBDEM) to boost textural features of the overlapped mass region based on the breast density level. This approach determines the optimal exposure threshold of the images' lower contrast limit and optimizes the parameters by selecting the best intensity factor guided by the best Blind/Reference-less Image Spatial Quality Evaluator (BRISQUE) scores separately for both dense and non-dense breast classes prior to training. Meanwhile, a modified You Only Look Once v3 (YOLOv3) architecture is employed for mass detection by specifically assigning an extra number of higher-valued anchor boxes to the shallower detection head using the enhanced image. The experimental results show that the use of SbBDEM prior to training mass detection promotes superior performance with an increase in mean Average Precision (mAP) of 17.24% improvement over the non-enhanced trained image for mass detection, mass segmentation of 94.41% accuracy, and 96% accuracy for benign and malignant mass classification. Enhancing the mammogram images based on breast density is proven to increase the overall system's performance and can aid in an improved clinical diagnosis process.
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Affiliation(s)
- Noor Fadzilah Razali
- Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Bukit Mertajam 13500, Pulau Pinang, Malaysia
| | - Iza Sazanita Isa
- Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Bukit Mertajam 13500, Pulau Pinang, Malaysia
| | - Siti Noraini Sulaiman
- Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Bukit Mertajam 13500, Pulau Pinang, Malaysia
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA Cawangan Selangor, Puncak Alam Campus, Puncak Alam 42300, Selangor, Malaysia
| | - Noor Khairiah Abdul Karim
- Department of Biomedical Imaging, Advanced Medical and Dental Institute, Universiti Sains Malaysia Bertam, Kepala Batas 13200, Pulau Pinang, Malaysia
- Breast Cancer Translational Research Programme (BCTRP), Advanced Medical and Dental Institute, Universiti Sains Malaysia Bertam, Kepala Batas 13200, Pulau Pinang, Malaysia
| | - Muhammad Khusairi Osman
- Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Bukit Mertajam 13500, Pulau Pinang, Malaysia
| | - Zainal Hisham Che Soh
- Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Bukit Mertajam 13500, Pulau Pinang, Malaysia
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Tensor-Based Learning for Detecting Abnormalities on Digital Mammograms. Diagnostics (Basel) 2022; 12:diagnostics12102389. [PMID: 36292078 PMCID: PMC9601228 DOI: 10.3390/diagnostics12102389] [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: 09/05/2022] [Revised: 09/28/2022] [Accepted: 09/29/2022] [Indexed: 11/17/2022] Open
Abstract
In this study, we propose a tensor-based learning model to efficiently detect abnormalities on digital mammograms. Due to the fact that the availability of medical data is limited and often restricted by GDPR (general data protection regulation) compliance, the need for more sophisticated and less data-hungry approaches is urgent. Accordingly, our proposed artificial intelligence framework utilizes the canonical polyadic decomposition to decrease the trainable parameters of the wrapped Rank-R FNN model, leading to efficient learning using small amounts of data. Our model was evaluated on the open source digital mammographic database INBreast and compared with state-of-the-art models in this domain. The experimental results show that the proposed solution performs well in comparison with the other deep learning models, such as AlexNet and SqueezeNet, achieving 90% ± 4% accuracy and an F1 score of 84% ± 5%. Additionally, our framework tends to attain more robust performance with small numbers of data and is computationally lighter for inference purposes, due to the small number of trainable parameters.
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