Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks
Open accessAbstract
Background and objective The processing of medical image is an important tool to assist in minimizing the degree of uncertainty of the specialist, while providing specialists with an additional source of detect and diagnosis information. Breast cancer is the most common type of cancer that affects the female population around the world. It is also the most deadly type of cancer among women. It is the second most common type of cancer among all others. The most common examination to diagnose breast cancer early is mammography. In the last decades, computational techniques have been developed with the purpose of automatically detecting structures that maybe associated with tumors in mammography examination. This work presents a computational methodology to automatically detection of mass regions in mammography by using a convolutional neural network.Methods The materials used in this work is the DDSM database. The method proposed consists of two phases: training phase and test phase. The training phase has 2 main steps: (1) create a model to classify breast tissue into dense and non-dense (2) create a model to classify regions of breast into mass and non-mass. The test phase has 7 step: (1) preprocessing; (2) registration; (3) segmentation; (4) first reduction of false positives; (5) preprocessing of regions segmented; (6) density tissue classification (7) second reduction of false positives where regions will be classified into mass and non-mass.Results The proposed method achieved 95.6% of accuracy in classify non-dense breasts tissue and 97,72% accuracy in classify dense breasts. To detect regions of mass in non-dense breast, the method achieved a sensitivity value of 91.5%, and specificity value of 90.7%, with 91% accuracy. To detect regions in dense breasts, our method achieved 90.4% of sensitivity and 96.4% of specificity, with accuracy of 94.8%.Conclusions According to the results achieved by CNN, we demonstrate the feasibility of using convolutional neural networks on medical image processing techniques for classification of breast tissue and mass detection.
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Deep learning is increasingly used in medical imaging for tasks such as automated lesion detection, segmentation and registration to assist clinicians in disease diagnosis and surgical planning.
“In medical imaging, deep learning is increasingly used for tasks such as automated lesion detection, segmentation and registration to assist clinicians in disease diagnosis and surgical planning.”
From Deep Learning Strategies for Ultrasound in Pregnancy (2020) · cites this paper as [17]
Deep learning techniques have the potential to create new screening tools, predict diseases, improve diagnostic accuracy and accelerate clinical tasks, whilst also reducing costs and human error.
“Deep learning techniques have the potential to create new screening tools, predict diseases, improve diagnostic accuracy and accelerate clinical tasks, whilst also reducing costs and human error [9–17].”
From Deep Learning Strategies for Ultrasound in Pregnancy (2020) · cites this paper as [17]
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works
Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012
Ferlay, J., Soerjomataram, I., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., Parkin, D. M., Forman, D., & Bray, F. (2015) · International Journal of Cancer · doi:10.1002/ijc.29210 · Cited by 28,766 · see citing works
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653 · see citing works
Image registration methods: a survey
Zitová, B., & Flusser, J. (2003) · Image and Vision Computing · doi:10.1016/s0262-8856(03)00137-9 · Cited by 6,423 · see citing works
A survey of image registration techniques
Brown, L. G. (1992) · ACM Computing Surveys · doi:10.1145/146370.146374 · Cited by 4,079 · see citing works
Percival, D. B., González, R. C., & Wintz, P. A. (1989) · Technometrics · doi:10.2307/1270383 · Cited by 1,187 · see citing works
THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY
Heath, M. D., Bowyer, K. W., Kopans, D. B., & Moore, R. M. (2007) · Cited by 1,017 · see citing works
Large scale deep learning for computer aided detection of mammographic lesions
Kooi, T., Litjens, G., van Ginneken, B., Gubern-Mérida, A., Sánchez, C. I., Mann, R., den Heeten, A., & Karssemeijer, N. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.07.007 · Cited by 965 · see citing works
Medical image registration: a review
Oliveira, F. P. M., & Tavares, J. M. R. S. (2012) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2012.670855 · Cited by 738 · see citing works
Influence of Computer-Aided Detection on Performance of Screening Mammography
Fenton, J. J., Taplin, S. H., Carney, P. A., Abraham, L., Sickles, E. A., D’Orsi, C. J., Berns, E. A., Cutter, G., Hendrick, R. E., Barlow, W. E., et al. (2007) · New England Journal of Medicine · doi:10.1056/nejmoa066099 · Cited by 619 · see citing works
2-D and 3-D Image Registration: for Medical, Remote Sensing, and Industrial Applications
Goshtasby, A. A. (2005) · Cited by 404 · see citing works
Ma, Z., Tavares, J. M. R. S., Jorge, R. N., & Mascarenhas, T. (2009) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255840903131878 · Cited by 317 · see citing works
Automated Mass Detection in Mammograms Using Cascaded Deep Learning and Random Forests
Dhungel, N., Carneiro, G., & Bradley, A. P. (2015) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2015.7371234 · Cited by 233 · see citing works
Book Review: 2-D and 3-D Image Registration for Medical, Remote Sensing, and Industrial Applications
Ray, L. A. (2005) · Journal of Electronic Imaging · doi:10.1117/1.2041647 · Cited by 224 · see citing works
On developing B-spline registration algorithms for multi-core processors
Shackleford, J., Kandasamy, N., & Sharp, G. C. (2010) · Physics in Medicine and Biology · doi:10.1088/0031-9155/55/21/001 · Cited by 148 · see citing works
Convolutional neural networks for mammography mass lesion classification
Arévalo, J., Gonzalez, F. A., Ramos-Pollán, R., Oliveira, J. L., & Guevara, M. A. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318482 · Cited by 147 · see citing works
Computer-aided diagnosis of breast lesions in medical images
Giger, M. L. (2000) · Computing in Science & Engineering · doi:10.1109/5992.877391 · Cited by 144 · see citing works
Meyer, A. D. S., Garcia, A. A. F., de Souza, A. P., & de Souza, C. L. (2004) · Genetics and Molecular Biology · doi:10.1590/s1415-47572004000100014 · Cited by 142 · see citing works
Tzikopoulos, S. D., Mavroforakis, M., Georgiou, H., Dimitropoulos, N., & Theodoridis, S. (2011) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2010.11.016 · Cited by 138 · see citing works
Automatic detection of breast border and nipple in digital mammograms
Méndez, A. J., Tahoces, P. G., Lado, M. J., Souto, M., Correa, J., & Vidal, J. (1996) · Computer Methods and Programs in Biomedicine · doi:10.1016/0169-2607(96)01724-5 · Cited by 137 · see citing works
Detection of masses in mammogram images using CNN, geostatistic functions and SVM
de Sampaio, W. B., Diniz, E. M., Silva, A. C., de Paiva, A. C., & Gattass, M. (2011) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2011.05.017 · Cited by 133 · see citing works
Self-citationBreast asymmetry and predisposition to breast cancer
Scutt, D., Lancaster, G., & Manning, J. T. (2006) · Breast Cancer Research · doi:10.1186/bcr1388 · Cited by 132 · see citing works
Hu, K., Gao, X., & Li, F. (2010) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2010.2051060 · Cited by 120 · see citing works
A REVIEW ON THE CURRENT SEGMENTATION ALGORITHMS FOR MEDICAL IMAGES
Ma, Z., Tavares, J. M. R. S., & Jorge, R. N. (2009) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0001793501350140 · Cited by 119 · see citing works
Proceedings of the 9th international workshop on Digital Mammography
Krupinski, E. A. (2008) · Cited by 108 · see citing works
Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis
Suzuki, S., Zhang, X., Homma, N., Ichiji, K., Sugita, N., Kawasumi, Y., Ishibashi, T., & Yoshizawa, M. (2016) · Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) · doi:10.1109/sice.2016.7749265 · Cited by 100 · see citing works
Filho, M., Ma, Z., & Tavares, J. M. R. S. (2015) · Journal of Medical Systems · doi:10.1007/s10916-015-0354-8 · Cited by 76 · see citing works
Karnan, M., & Thangavel, K. (2007) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2007.04.007 · Cited by 65 · see citing works
Jodas, D. S., Pereira, A. S., & Tavares, J. M. R. S. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.10.016 · Cited by 63 · see citing works
Bilateral analysis based false positive reduction for computer‐aided mass detection
Wu, Y.-T., Wei, J., Hadjiiski, L. M., Sahiner, B., Zhou, C., Ge, J., Shi, J., Zhang, Y., & Chan, H. (2007) · Medical Physics · doi:10.1118/1.2756612 · Cited by 53 · see citing works
de Sampaio, W. B., Silva, A. C., de Paiva, A. C., & Gattass, M. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.07.046 · Cited by 47 · see citing works
Self-citationMachine Learning and Data Mining in Pattern Recognition
Perner, P., & Leipzig, M. 2. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03070-3 · Cited by 41 · see citing works
Ericeira, D. R., Silva, A. C., de Paiva, A. C., & Gattass, M. (2013) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2013.04.019 · Cited by 39 · see citing works
Self-citationBráz, G., da Rocha, S. V., Gattass, M., Silva, A. C., & de Paiva, A. C. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.07.034 · Cited by 35 · see citing works
Self-citationAnalysis of Structural Similarity in Mammograms for Detection of Bilateral Asymmetry
Casti, P., Mencattini, A., Salmeri, M., & Rangayyan, R. M. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2365436 · Cited by 35 · see citing works
Analysis of Biomedical Images Based on Automated Methods of Image Registration
Tavares, J. M. R. S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-14249-4_3 · Cited by 33 · see citing works
Wang, X., Li, L., Xu, W., Liu, W., Lederman, D., & Zheng, B. (2011) · Academic Radiology · doi:10.1016/j.acra.2011.10.026 · Cited by 20 · see citing works
Evaluation of contrast limited adaptive histogram equalization (CLAHE) enhancement on a FPGA
Ferguson, P. D., Arslan, T., Erdogan, A. T., & Parmley, A. (2008) · IEEE International SOC Conference (SOCC) · doi:10.1109/socc.2008.4641492 · Cited by 20 · see citing works
Mammographic Mass Detection with Statistical Region Merging
Bajger, M., Ma, F., Williams, S., & Bottema, M. J. (2010) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2010.14 · Cited by 15 · see citing works
Neto, O. P. D. S., Carvalho, O. S. F. D., de Sampaio, W. B., Correa, A., & de Paiva, A. C. (2015) · International Conference on Systems, Signals and Image Processing · doi:10.1109/iwssip.2015.7314189 · Cited by 8 · see citing works
Breast cancer detection by Michaelis–Menten constants via linear programming
Blokh, D., Afrimzon, E., Stambler, I., Korech, E., Shafran, Y., Zurgil, N., & Deutsch, M. (2006) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2006.11.003 · Cited by 7 · see citing works
Sun, W., Tseng, T.-L., Zheng, B., Zhang, J., & Qian, W. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2076633 · Cited by 6 · see citing works
Nunes, A., Silva, A. C., & de Paiva, A. C. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03070-3_41 · Cited by 5 · see citing works
Morphology Based Technique for Texture Enhancement and Segmentation
Joseph Prakash, M., Kumar, V., V, & Vinayababu, A. (2013) · Signal & Image Processing An International Journal · doi:10.5121/sipij.2013.4104 · Cited by 5 · see citing works
Kelder, A., Zigel, Y., Lederman, D., & Zheng, B. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7319856 · Cited by 2 · see citing works
Flagged first, then by citations
1 of the 48 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- K. Deguchi , Registration techniques for partially covered image sequence, in: Proceedings of the 8th International Confernce on Pattern Recognition, 1986, pp. 1186-1189 .
Cited by
Sorted by citations
Zhou, J., Zhang, Y., Chang, K., Lee, K. E., Wang, O., Li, J., Lin, Y., Pan, Z., Chang, P., Chow, D. S., et al. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26981 · Cited by 217 · see citing works
Houssami, N., Kirkpatrick-Jones, G., Noguchi, N., & Lee, C. I. (2019) · Expert Review of Medical Devices · doi:10.1080/17434440.2019.1610387 · Cited by 185 · see citing works
Automated Breast Cancer Detection in Digital Mammograms of Various Densities via Deep Learning
Suh, Y. J., Jung, J., & Cho, B. (2020) · Journal of Personalized Medicine · doi:10.3390/jpm10040211 · Cited by 135 · see citing works
Kidney segmentation from computed tomography images using deep neural network
da Cruz, L. B., Araújo, J. D. L., Ferreira, J. L., Diniz, J. O. B., Silva, A. C., de Almeida, J. D. S., de Paiva, A. C., & Gattass, M. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103906 · Cited by 124 · see citing works
Self-citationBreast cancer diagnosis from histopathological images using textural features and CBIR
Carvalho, E. D., Filho, A. O. C., Silva, R., Araújo, F. H. D., Diniz, J. O. B., Silva, A. C., de Paiva, A. C., & Gattass, M. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101845 · Cited by 113 · see citing works
Self-citationZebari, D. A., Ibrahim, D. A., Zeebaree, D. Q., Mohammed, M. A., Haron, H., Zebari, N. A., Damaševičius, R., & Maskeliūnas, R. (2021) · Applied Sciences · doi:10.3390/app112412122 · Cited by 111 · see citing works
Malebary, S. J., & Hashmi, A. (2021) · IEEE Access · doi:10.1109/access.2021.3071297 · Cited by 105 · see citing works
Deep Learning-Based Artificial Intelligence for Mammography
Yoon, J. H., & Kim, E. (2021) · Korean Journal of Radiology · doi:10.3348/kjr.2020.1210 · Cited by 88 · see citing works
Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning
Zeiser, F. A., da Costa, C. A., Zonta, T., Marques, N. M. C., Roehe, A. V., Moreno, M., & da Rosa Righi, R. (2020) · Journal of Digital Imaging · doi:10.1007/s10278-020-00330-4 · Cited by 86 · see citing works
MommiNet-v2: Mammographic multi-view mass identification networks
Yang, Z., Cao, Z., Zhang, Y., Tang, Y., Lin, X. S., Ouyang, R., Wu, M., Han, M., Xiao, J. Z., Huang, L., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102204 · Cited by 83 · see citing works
Deep convolutional neural networks for computer-aided breast cancer diagnostic: a survey
Oza, P., Sharma, P., Patel, S., & Kumar, P. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06804-y · Cited by 82 · see citing works
Dias, D., da Cruz, L. B., Diniz, J. O. B., da Silva, G. L. F., Bráz, G., Silva, A. C., de Paiva, A. C., Nunes, R. A., & Gattass, M. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115452 · Cited by 69 · see citing works
Self-citationLiu, Y., Zhang, F., Zhang, Q., Wang, S., Wang, Y., & Yu, Y. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00387 · Cited by 65 · see citing works
Diniz, J. O. B., Quintanilha, D. B. P., Neto, A. C. S., da Silva, G. L. F., Ferreira, J. L., Netto, S. M. B., Araújo, J. D. L., da Cruz, L. B., Silva, T. F. B., Martins, C. M. D. S., et al. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11153-y · Cited by 55 · see citing works
Self-citationBreast mass detection in digital mammography based on anchor-free architecture
Cao, H., Pu, S., Tan, W., & Tong, J. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106033 · Cited by 48 · see citing works
Liu, Y., Zhang, F., Chen, C., Wang, S., Wang, Y., & Yu, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3085783 · Cited by 48 · see citing works
Esophagus segmentation from planning CT images using an atlas-based deep learning approach
Diniz, J. O. B., Ferreira, J. L., Diniz, P. H. B., Silva, A. C., & de Paiva, A. C. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105685 · Cited by 38 · see citing works
Diniz, J. O. B., Diniz, P. H. B., Valente, T. L. A., Silva, A. C., & Paiva, A. C. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.01.005 · Cited by 34 · see citing works
Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network
Diniz, P. H. B., Valente, T. L. A., Diniz, J. O. B., Silva, A. C., Gattass, M., Ventura, N., Muniz, B. C., & Gasparetto, E. L. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.04.011 · Cited by 34 · see citing works
Self-citationJarosik, P., Klimonda, Z., Lewandowski, M., & Byra, M. (2020) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2020.04.002 · Cited by 34 · see citing works
Deep Learning Strategies for Ultrasound in Pregnancy
Diniz, P. H. B., Yin, Y., & Collins, S. (2020) · EMJ Reproductive Health · doi:10.33590/emjreprohealth/20-00100 · Cited by 32 · see citing works
From Unilateral to Bilateral Learning: Detecting Mammogram Masses with Contrasted Bilateral Network
Liu, Y., Zhou, Z. X., Zhang, S., Luo, L., Zhang, Q., Zhang, F., Li, X., Wang, Y., & Yu, Y. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32226-7_53 · Cited by 32 · see citing works
Mass detection in mammograms by bilateral analysis using convolution neural network
Li, Y., Zhang, L., Chen, H., & Cheng, L. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105518 · Cited by 29 · see citing works
Neural network model based on global and local features for multi-view mammogram classification
Xia, L., An, J., Ma, C., Hou, H., Hou, Y., Cui, L., Jiang, X., Li, W., & Gao, Z. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.03.028 · Cited by 29 · see citing works
Wong, D., Gandomkar, Z., Wu, W., Zhang, G., Gao, W., He, X., Wang, Y., & Reed, W. M. (2020) · Journal of Medical Radiation Sciences · doi:10.1002/jmrs.385 · Cited by 29 · see citing works
An Analysis of Machine Learning Classifiers in Breast Cancer Diagnosis
Teixeira, F., Montenegro, J. L. Z., da Costa, C. A., & Righi, R. D. R. (2019) · Latin American Computer Conference (CLEI) · doi:10.1109/clei47609.2019.235094 · Cited by 27 · see citing works
Hanis, T. M., Islam, M. A., & Musa, K. I. (2022) · Diagnostics · doi:10.3390/diagnostics12071643 · Cited by 25 · see citing works
Diniz, J. O. B., Ferreira, J. L., Cortes, O. A. C., Silva, A. C., & de Paiva, A. C. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116632 · Cited by 22 · see citing works
A Multi-View Deep Evidential Learning Approach for Mammogram Density Classification
Gudhe, N. R., Sudah, M., Sund, R., Kosma, V., Behravan, H., & Mannermaa, A. (2024) · IEEE Access · doi:10.1109/access.2024.3399204 · Cited by 22 · see citing works
Xue, H., Wang, S.-Y., Cui, L., & Hong, K. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.18.20436 · Cited by 19 · see citing works
MommiNet: Mammographic Multi-view Mass Identification Networks
Yang, Z., Cao, Z., Zhang, Y., Han, M., Xiao, J. Z., Huang, L., Wu, S., Ma, J., & Chang, P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59725-2_20 · Cited by 19 · see citing works
Shimokawa, D., Takahashi, K., Kurosawa, D., Takaya, E., Oba, K., Yagishita, K., Fukuda, T., Tsunoda, H. S., & Ueda, T. (2022) · Radiological Physics and Technology · doi:10.1007/s12194-022-00686-y · Cited by 19 · see citing works
Optimized hyperbolic tangent function-based contrast-enhanced mammograms for breast mass detection
Laishram, R., & Rabidas, R. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118994 · Cited by 18 · see citing works
Recent trends on mammogram breast density analysis using deep learning models: neoteric review
Idas, S. J. P., Hemalatha, K., Naveenkumar, J., & Devadas, T. J. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11232-8 · Cited by 18 · see citing works
Enhancement Technique Based on the Breast Density Level for Mammogram for Computer-Aided Diagnosis
Razali, N. F., Isa, I. S., Sulaiman, S. N., Karim, N. K. A., Osman, M. K., & Soh, Z. H. C. (2023) · Bioengineering · doi:10.3390/bioengineering10020153 · Cited by 16 · see citing works
Basha, A. A., Vivekanandan, S., Mubarakali, A., & Alqahtani, A. S. (2023) · Measurement · doi:10.1016/j.measurement.2023.113551 · Cited by 15 · see citing works
Compare and contrast: Detecting mammographic soft-tissue lesions with C 2 -Net
Liu, Y., Zhou, C., Zhang, F., Zhang, Q., Wang, S., Zhou, J. H., Sheng, F., Wang, X., Liu, W., Wang, Y., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101999 · Cited by 13 · see citing works
Mammogram mass segmentation and detection using Legendre neural network-based optimal threshold
Sarangi, S., Rath, N. P., & Sahoo, H. K. (2021) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-021-02348-4 · Cited by 11 · see citing works
Malliori, A., & Pallikarakis, N. E. (2022) · Health and Technology · doi:10.1007/s12553-022-00693-4 · Cited by 10 · see citing works
Multiple-level thresholding for breast mass detection
Yu, X., Wang, S., & Zhang, Y. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2022.11.006 · Cited by 9 · see citing works
Data Engineering for Machine Learning in Women's Imaging and Beyond
Chen, C., Chou, S.-H. S., Brattain, L. J., Lehman, C. D., & Samir, A. E. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.18.20464 · Cited by 9 · see citing works
Bhalla, D., Rangarajan, K., Chandra, T., Banerjee, S., & Arora, C. (2023) · Indian journal of radiology and imaging - new series/Indian journal of radiology and imaging/Indian Journal of Radiology & Imaging · doi:10.1055/s-0043-1775737 · Cited by 9 · see citing works
Diniz, J. O. B., Dias, D. A., da Cruz, L. B., Marques, R. C. S., Gomes, D. L., Cortes, O. A. C., Filho, A. O. D. C., & Quintanilha, D. B. P. (2024) · doi:10.5753/sbcas.2024.2155 · Cited by 8 · see citing works
Mammographic Mass Detection by Bilateral Analysis Based on Convolution Neural Network
Zhang, L., Li, Y., Chen, H., & Cheng, L. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803761 · Cited by 8 · see citing works
Advancing Artificial Intelligence in Sensors, Signals, and Imaging Informatics
Hsu, W. W. Y., Baumgärtner, C., Deserno, T. M., & Section Editors for the IMIA Yearbook Section on Sensors, S. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677943 · Cited by 7 · see citing works
Mohamed, A., Fakhry, S., & Basha, T. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9872011 · Cited by 6 · see citing works
GFNet: A Deep Learning Framework for Breast Mass Detection
Yu, X., Zhu, Z., Alon, Y., Guttery, D. S., & Zhang, Y. (2023) · Electronics · doi:10.3390/electronics12071583 · Cited by 6 · see citing works
Hegdé, J. (2020) · Journal of medical imaging · doi:10.1117/1.jmi.7.2.022410 · Cited by 5 · see citing works
Automatic Detection of Malignant Masses in Digital Mammograms Based on a MCET-HHO Approach
Rodríguez-Esparza, E., Zanella-Calzada, L. A., Zaldívar, D., & Galvan-Tejada, C. E. (2020) · Studies in computational intelligence · doi:10.1007/978-3-030-40977-7_15 · Cited by 5 · see citing works
Razali, N. F., Isa, I. S., Sulaiman, S. N., Karim, N. K. A., & Osman, M. K. (2022) · Journal of Electrical & Electronic Systems Research · doi:10.24191/jeesr.v21i1.009 · Cited by 4 · see citing works
Symmetry-Aware Siamese Network: Exploiting Pathological Asymmetry for Chest X-Ray Analysis
Schneider, H., Yildiz, E. C., Biesner, D., Layer, Y. C., Wulff, B., Nowak, S., Theis, M., Sprinkart, A. M., Attenberger, U. I., & Sifa, R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44216-2_14 · Cited by 3 · see citing works
Improving Colorectal Cancer Diagnosis Using MIRNet and InceptionV3 on Histopathological Images
Ribeiro, N. P., Teles, F. R. S., Diniz, J. O. B., da Cruz, L. B., Dias, D., Bráz, G., de Almeida, J. D. S., & de Paiva, A. C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-79035-5_22 · Cited by 3 · see citing works
Torres, W. D. O., Filho, A. O. D. C., de Sousa, A. D., & Silva, F. A. (2018) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc.2018.8538658 · Cited by 3 · see citing works
Ma, Y., & Peng, Y. (2022) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2022.01.001 · Cited by 3 · see citing works
Dhamodharan, S., & Pichai, S. (2022) · The Journal of Analysis · doi:10.1007/s41478-022-00499-7 · Cited by 3 · see citing works
A Review of CAD systems for Breast Mass Detection in Mammography Based on Deep LearningPreprint
Mohammadi, S., & Livani, M. A. (2023) · Preprints.org · doi:10.20944/preprints202305.1832.v1 · Cited by 3 · see citing works
Bilateral Information-Guided Diagnosis of Breast Masses in Mammography Using Vision Transformer
Zeng, T., Zeng, Y., Zhang, Z., Zhang, X., Ichiji, K., Chou, S., Bukovský, I., Vrba, J., & Homma, N. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3626200 · Cited by 2 · see citing works
Breast image mass detection model based on multi-scale feature reinforcement network
Wang, X., Li, Y., Guo, H., He, R., Quan, J., Wang, L., & Guo, L. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106655 · Cited by 2 · see citing works
Vision Transformer-Based Breast Mass Diagnosis in Mammography Using Bilateral Information
Zeng, T., Zhang, Z., Zeng, Y., Zhang, X., Ichiji, K., & Homma, N. (2024) · IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) · doi:10.1109/iicaiet62352.2024.10730097 · Cited by 2 · see citing works
Diniz, J. O. B., Ribeiro, N. P., Dias, D., da Cruz, L. B., da Silva, G. L. F., Gomes, D. L., de Paiva, A. C., & Silva, A. C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-79035-5_7 · Cited by 2 · see citing works
Kumar, M. G., Kocharla, S., Yaswanth, N., Swamy, T. V. N., Prasad, U., & Vamsee, T. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107540 · Cited by 2 · see citing works
Performance Analysis of Image Enhancement Techniques for Mammogram Images
Mrunalini, A. R., & Premaladha, J. (2019) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-3-030-00665-5_158 · Cited by 2 · see citing works
Computer aided diagnosis system for breast density classification in mammograms
Shrinithi, S., Vijayan, D., & Lavanya, R. V. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2318/1/012039 · Cited by 2 · see citing works
A Locating Model for Pulmonary Tuberculosis Diagnosis in RadiographsPreprint
Liu, J., Liu, J., Liu, Y., Yang, R., Lv, D., Cai, Z., & Cui, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.09900 · Cited by 2 · see citing works
Ho, P.-S., Tsai, H., Liu, I., Lee, Y.-Y., & Chan, S. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110581 · Cited by 1 · see citing works
Kim, H. J., Chae, E. Y., Eom, H. J., Choi, W. J., Shin, H. J., Cha, J. H., & Kim, H. H. (2026) · European Radiology · doi:10.1007/s00330-026-12493-9 · Cited by 1 · see citing works
False Positive Reduction in Mammographic Mass Detection
Shrinithi, S., Lavanya, R. V., & Vijayan, D. (2023) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-39811-7_5 · Cited by 1 · see citing works
Rezayi, S., Nilashi, M., Esmaeeli, E., Ramezanghorbani, N., Arji, G., Ahmadi, H., Shahmoradi, L., & Zahmatkeshan, M. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11215-4 · Cited by 1 · see citing works
Hanis, T. M., Islam, M. A., & Musa, K. I. (2021) · Research Square · doi:10.21203/rs.3.rs-970393/v1
Methods for segmentation of spinal cord and esophagus in radiotherapy planning computed tomography
Diniz, J. O. B., Silva, A. C., & de Paiva, A. C. (2021) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.5753/sibgrapi.est.2021.20009
Breast Cancer: Automatic Detection from Mammogram Images by Utilizing Deep Learning Methods
Satapathy, S. K., Parmar, D., & Rajput, N. S. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-9235-5_22
Amin, A., Acharya, U. D., Siddalingaswamy, P. C., Prakashini, K., & Mathew, S. (2024) · Research Square · doi:10.21203/rs.3.rs-4181414/v1
BiGAM-Net: A bilateral gated fusion framework for imbalanced breast imaging
Haque, A. K. A., Li, X., Li, Y., & Yuan, X. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132746
Robertson, C., Cooper, D., Duggan, S. N., Manson, P. D., Imamura, M., Hernández, R. A., Clarke, M. J., Treweek, S. P., & Brazzelli, M. G. (2026) · Journal of Medical Screening · doi:10.1177/09691413261447057
- Authors
- Bandeira Diniz, J. O., Bandeira Diniz, P. H., Azevedo Valente, T. L., Corrêa Silva, A., de Paiva, A. C., & Gattass, M.
- Affiliations
- Pontifícia Universidade Católica do Rio de Janeiro (BR)
- ORCID
- Gattass, Marcelo
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2018-03
- Volume
- 156
- Pages
- 191-207
- ISSN
- 0169-2607 (ISSN-L)
- OpenAlex
- W2784257876
- Cited by
- 74
- References
- 48
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 6 · 2023: 8 · 2022: 12 · 2021: 12 · 2020: 13 · 2019: 11 · 2018: 2
- FWCI
- 5.35
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Computer Methods and Programs in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Digital Radiography and Breast Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- mammography, breast mass detection, breast density, convolutional neural networks, breast tissue classification, similarity index, DDSM database, false positive reduction, breast cancer screening
- MeSH terms
- Breast Density, Algorithms, Breast, Breast Neoplasms, Diagnosis, Computer-Assisted, False Positive Reactions, Female, Humans, Image Processing, Computer-Assisted, Mammography, Radiographic Image Interpretation, Computer-Assisted, Sensitivity and Specificity, Reproducibility of Results, Models, Statistical, Neural Networks, Computer
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