MRFE-CNN: multi-route feature extraction model for breast tumor segmentation in Mammograms using a convolutional neural network

• Abdelhafiz, D., Yang, C., Ammar, R., & Nabavi, S. (2019). Deep convolutional neural networks for mammography: Advances, challenges and applications. BMC Bioinformatics, 20(11), 1–20

  Google Scholar 

• Aghamohammadi, A., Ranjbarzadeh, R., Naiemi, F., Mogharrebi, M., Dorosti, S., & Bendechache, M. (2021). TPCNN: Two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach. Expert Systems with Applications, 183, 115406

  Google Scholar 

• Aleem, S., Kumar, T., Little, S., Bendechache, M., Brennan, R., & McGuinness, K. (2021). Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets

• Ali, E., Caputo, A., Lawless, S., & Conlan, O. (2021). Where Should I Go? A Deep Learning Approach to Personalize Type-Based Facet Ranking for POI Suggestion. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 13080 LNCS: 207–215

• Almutiry, O., Iqbal, K., Hussain, S., Mahmood, A., & Dhahri, H. (2021). Underwater images contrast enhancement and its challenges: a survey. Multimedia Tools and Applications, 2021, 1–26.

  Google Scholar 

• Alsheh Ali, M., Czene, K., Hall, P., & Humphreys, K. (2019). Association of microcalcification clusters with short-term invasive breast cancer risk and breast cancer risk factors. Scientific Reports 9(1): 1–8

  Google Scholar 

• Arjmand, A., Meshgini, S., Afrouzian, R., & Farzamnia, A. (2019). Breast tumor segmentation using K-Means clustering and cuckoo search optimization. In 2019 9th International Conference on Computer and Knowledge Engineering, ICCKE 2019: 305–308

• Ashraf, A. B., Gavenonis, S. C., Daye, D., Mies, C., Rosen, M. A., & Kontos, D. (2013). A multichannel markov random field framework for tumor segmentation with an application to classification of gene expression-based breast cancer recurrence risk. IEEE Transactions on Medical Imaging, 32(4), 637–648.

  Google Scholar 

• Azary, H., & Abdoos, M. (2020). A Semi-supervised method for tumor segmentation in mammogram images. Journal of Medical Signals and Sensors, 10(1), 12–18.

  Google Scholar 

• Bengio, Y. (2012). Practical recommendations for gradient-based training of deep architectures. (pp 437–478). Springer, Berlin.

• Budak, Ü., Cömert, Z., Rashid, Z. N., Şengür, A., & Çıbuk, M. (2019). Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images. Applied Soft Computing Journal, 85, 105765.

  Google Scholar 

• Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers and Electrical Engineering, 40(1), 16–28.

  Google Scholar 

• Chang, Y., Jung, C., Ke, P., Song, H., & Hwang, J. (2018). Automatic contrast-limited adaptive histogram equalization with dual gamma correction. IEEE Access, 6, 11782–11792.

  Google Scholar 

• Chen, S. D., & Ramli, A. R. (2003). Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Transactions on Consumer Electronics, 49(4), 1310–1319.

  Google Scholar 

• Choi, M. S., Choi, B. S., Chung, S. Y., Kim, N., Chun, J., Kim, Y. B. … Kim, J. S. (2020). Clinical evaluation of atlas- and deep learning-based automatic segmentation of multiple organs and clinical target volumes for breast cancer. Radiotherapy and Oncology, 153, 139–145.

  Google Scholar 

• Das, S., De, S., Bhattacharyya, S., & Hassanien, A. E. (2019). Color MRI image segmentation using quantum-inspired modified genetic algorithm-based FCM. In  Advances in Intelligent Systems and Computing, (pp. 151–164). Springer.

• de Neto, A., Santos, S. R., da Silva Rocha, G. L., Bendechache, E., Rosati, M., Lynn, P., T., & Takako Endo, P. (2020). Detecting human activities based on a multimodal sensor data set using a bidirectional long short-term memory model: a case study. In Studies in Systems, Decision and Control, (pp. 31–51). Springer.

• Debelee, T. G., Schwenker, F., Ibenthal, A., & Yohannes, D. (2019). Survey of deep learning in breast cancer image analysis. Evolving Systems , 11(1), 143–163.

  Google Scholar 

• DeSantis, C. E., Ma, J., Goding Sauer, A., Newman, L. A., & Jemal, A. (2017). Breast cancer statistics, 2017, racial disparity in mortality by state. CA: A Cancer Journal for Clinicians, 67(6), 439–448.

  Google Scholar 

• Dhal, K. G., Ray, S., Das, A., & Das, S. (2018). A Survey on Nature-Inspired Optimization Algorithms and Their Application in Image Enhancement Domain. Archives of Computational Methods in Engineering , 26(5), 1607–1638.

• Feng, Y., Dong, F., Xia, X., Hu, C. H., Fan, Q., Hu, Y. … Mutic, S. (2017). An adaptive Fuzzy C-means method utilizing neighboring information for breast tumor segmentation in ultrasound images. Medical Physics, 44(7), 3752–3760.

  Google Scholar 

• Garg, M., & Dhiman, G. (2020). A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants. Neural Computing and Applications , 33(4), 1311–1328

• Ghoushchi, S. J., Ranjbarzadeh, R., Dadkhah, A. H., Pourasad, Y., & Bendechache, M. (2021a). An extended approach to predict retinopathy in diabetic patients using the genetic algorithm and fuzzy C-means. BioMed Research International 2021: 1–13.

• Ghoushchi, S. J., Ranjbarzadeh, R., Najafabadi, S. A., Osgooei, E., & Tirkolaee, E. B. (2021b). An extended approach to the diagnosis of tumour location in breast cancer using deep learning. Journal of Ambient Intelligence and Humanized Computing.

• Hai, J., Qiao, K., Chen, J., Tan, H., Xu, J., Zeng, L. … Yan, B. (2019). Fully convolutional densenet with multiscale context for automated breast tumor segmentation. Journal of Healthcare Engineering 2019.

• Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., & Larochelle, H. (2017). Brain tumor segmentation with deep neural networks. Medical Image Analysis, 35, 18–31.

  Google Scholar 

• Hizukuri, A., Nakayama, R., Nara, M., Suzuki, M., & Namba, K. (2020). Computer-Aided Diagnosis Scheme for Distinguishing Between Benign and Malignant Masses on Breast DCE-MRI Images Using Deep Convolutional Neural Network with Bayesian Optimization. Journal of Digital Imaging , 34(1), 116–123.

• Ho, D. J., Yarlagadda, D. V. K., D’Alfonso, T. M., Hanna, M. G., Grabenstetter, A., Ntiamoah, P. … Fuchs, T. J. (2021). Deep Multi-Magnification Networks for multi-class breast cancer image segmentation. Computerized Medical Imaging and Graphics, 88, 101866.

  Google Scholar 

• Hojatimalekshah, A., Uhlmann, Z., Glenn, N. F., Hiemstra, C. A., Tennant, C. J., Graham, J. D. … Enterkine, J. (2021). Tree canopy and snow depth relationships at fine scales with terrestrial laser scanning. Cryosphere, 15(5), 2187–2209.

  Google Scholar 

• Hu, A., & Razmjooy, N. (2021). Brain tumor diagnosis based on metaheuristics and deep learning. International Journal of Imaging Systems and Technology, 31(2), 657–669.

  Google Scholar 

• Hussain, S., Xi, X., Ullah, I., Wu, Y., Ren, C., Lianzheng, Z. … Yin, Y. (2020). Contextual level-set method for breast tumor segmentation. IEEE Access, 8, 189343–189353.

  Google Scholar 

• Jafarian, A. H., Kooshkiforooshani, M., Rasoliostadi, A., & Roshan, N. M. (2019). Vascular mimicry expression in invasive ductal carcinoma; a new technique for prospect of aggressiveness. Iranian Journal of Pathology, 14(3), 232–235.

  Google Scholar 

• Kansal, S., Purwar, S., & Tripathi, R. K. (2018). Image contrast enhancement using unsharp masking and histogram equalization. Multimedia Tools and Applications , 77(20), 26919–26938

• Kumar, T., Park, J., Ali, M. S., Uddin, S., Ko, A. F. M., J.H., & Bae, S. H. (2021). Binary-Classifiers-Enabled Filters for Semi-Supervised Learning. IEEE Access:1–1.

• Lei, Y., He, X., Yao, J., Wang, T., Wang, L., Li, W. … Yang, X. (2021). Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R-CNN. Medical Physics, 48(1), 204–214.

  Google Scholar 

• Li, S., Jin, W., Li, L., & Li, Y. (2018). An improved contrast enhancement algorithm for infrared images based on adaptive double plateaus histogram equalization. Infrared Physics & Technology, 90, 164–174.

  Google Scholar 

• Li, Y., Lan, C., Xing, J., Zeng, W., Yuan, C., & Liu, J. (2016). Online human action detection using joint classification-regression recurrent neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9911 LNCS: 203–220.

• Liu, J., & Shi, Y. (2011). Image feature extraction method based on shape characteristics and its application in medical image analysis. In Communications in Computer and Information Science, (pp. 172–178). Springer, Berlin.

• Liu, Q., Liu, Z., Yong, S., Jia, K., & Razmjooy, N. (2020). Computer-aided breast cancer diagnosis based on image segmentation and interval analysis. Automatika, 61(3), 496–506.

  Google Scholar 

• Mahmood, A., Bennamoun, M., An, S., Sohel, F., Boussaid, F., Hovey, R. … Fisher, R. B. (2017). Deep Learning for Coral Classification.  In Handbook of Neural Computation, (pp. 383–401). Elsevier Inc.

• Mousavi, S. M., Asgharzadeh-Bonab, A., & Ranjbarzadeh, R. (2021). Time-frequency analysis of eeg signals and glcm features for depth of anesthesia monitoring. Computational Intelligence and Neuroscience 2021: 1–14.

• Munir, K., Elahi, H., Ayub, A., Frezza, F., & Rizzi, A. (2019). Cancer diagnosis using deep learning: A bibliographic review. Cancers, 11(9), 1235.

  Google Scholar 

• Naiemi, F., Ghods, V., & Khalesi, H. (2021). A novel pipeline framework for multi oriented scene text image detection and recognition. Expert Systems with Applications, 170, 114549.

  Google Scholar 

• Nensa, F., Demircioglu, A., & Rischpler, C. (2019). Artificial intelligence in nuclear medicine. Journal of Nuclear Medicine, 60(9), 29S–37S.

  Google Scholar 

• Niaz, A., Memon, A. A., Rana, K., Joshi, A., Soomro, S., Kang, J. S., & Choi, K. N. (2020). Inhomogeneous image segmentation using hybrid active contours model with application to breast tumor detection. IEEE Access, 8, 186851–186861.

  Google Scholar 

• Osapoetra, L. O., Chan, W., Tran, W., Kolios, M. C., & Czarnota, G. J. (2020). Comparison of methods for texture analysis of QUS parametric images in the characterization of breast lesions. PLoS ONE, 15(12), e0244965.

  Google Scholar 

• Park, Y., & Guldmann, J. M. (2020). Measuring continuous landscape patterns with Gray-Level Co-Occurrence Matrix (GLCM) indices: An alternative to patch metrics? Ecological Indicators, 109, 105802.

  Google Scholar 

• Patil, R. S., & Biradar, N. (2020). Automated mammogram breast cancer detection using the optimized combination of convolutional and recurrent neural network. Evolutionary Intelligence , 14(4), 1459–1474.

• Rangayyan, R. M., & Nguyen, T. M. (2007). Fractal analysis of contours of breast masses in mammograms. Journal of Digital Imaging, 20(3), 223–237.

  Google Scholar 

• Ranjbarzadeh, R., Bagherian Kasgari, A., Jafarzadeh Ghoushchi, S., Anari, S., Naseri, M., & Bendechache, M. (2021a). Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images. Scientific Reports, 11(1), 10930.

  Google Scholar 

• Ranjbarzadeh, R., Jafarzadeh Ghoushchi, S., Bendechache, M., Amirabadi, A., Ab Rahman, M. N., Saadi, B. … Kooshki Forooshani, M. (2021b). Lung Infection Segmentation for COVID-19 Pneumonia Based on a Cascade Convolutional Network from CT Images. BioMed Research International 2021: 1–16.

• Ranjbarzadeh, R., & Saadi, S. B. (2020). Automated liver and tumor segmentation based on concave and convex points using fuzzy c-means and mean shift clustering. Measurement: Journal of the International Measurement Confederation 150.

• Rao, B. S. (2020). Dynamic Histogram Equalization for contrast enhancement for digital images. Applied Soft Computing Journal, 89, 106114.

  Google Scholar 

• Razmjooy, N., & Razmjooy, S. (2021). Skin melanoma segmentation using neural networks optimized by quantum invasive weed optimization algorithm. In Lecture Notes in Electrical Engineering, (pp. 233–250). Springer, Deutschland GmbH.

• Razmjooy, N., Razmjooy, S., Vahedi, Z., Estrela, V. V., & de Oliveira, G. G. (2021). Skin Color segmentation based on artificial neural network improved by a modified grasshopper optimization algorithm. In Lecture Notes in Electrical Engineering, (pp. 169–185). Springer.  Deutschland GmbH.

• Rustam, Z., & Hartini, S. (2019). Classification of breast cancer using fast fuzzy clustering based on Kernel. IOP Conference Series: Materials Science and Engineering, 546(5).

• Saadi, S. B., Ranjbarzadeh, R., Kazemi, O., Amirabadi, A., Ghoushchi, S. J., Kazemi, O. … Bendechache, M. (2021). Osteolysis: A literature review of basic science and potential computer-based image processing detection methods. Computational Intelligence and Neuroscience 2021.

• Sarıgül, M., Ozyildirim, B. M., & Avci, M. (2019). Differential convolutional neural network. Neural Networks, 116, 279–287

  Google Scholar 

• Sarosa, S. J. A., Utaminingrum, F., & Bachtiar, F. A. (2018). Mammogram Breast Cancer Classification Using Gray-Level Co-Occurrence Matrix and Support Vector Machine. 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings, (pp. 54–59). Institute of Electrical and Electronics Engineers Inc.

• Sharif, M. I., Li, J. P., Naz, J., & Rashid, I. (2020). A comprehensive review on multi-organs tumor detection based on machine learning. Pattern Recognition Letters, 131, 30–37.

  Google Scholar 

• Shen, L., He, M., Shen, N., Yousefi, N., Wang, C., & Liu, G. (2020). Optimal breast tumor diagnosis using discrete wavelet transform and deep belief network based on improved sunflower optimization method. Biomedical Signal Processing and Control, 60, 101953.

  Google Scholar 

• Shrivastava, A., Chaudhary, A., Kulshreshtha, D., Singh, P., V., & Srivastava, R. (2017). Automated digital mammogram segmentation using Dispersed Region Growing and Sliding Window Algorithm. 2017 2nd International Conference on Image, Vision and Computing, ICIVC 2017, (pp. 366–370). Institute of Electrical and Electronics Engineers Inc.

• Singh, V. K., Rashwan, H. A., Romani, S., Akram, F., Pandey, N., Sarker, M. M. K. … Torrents-Barrena, J. (2020). Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network. Expert Systems with Applications, 139, 112855.

  Google Scholar 

• Sun, Y. S., Zhao, Z., Yang, Z. N., Xu, F., Lu, H. J., Zhu, Z. Y. … Zhu, H. P. (2017). Risk Factors and Preventions of Breast Cancer. International Journal of Biological Sciences, 13(11), 1387.

  Google Scholar 

• Tello-Mijares, S., Woo, F., & Flores, F. (2019). Breast Cancer Identification via Thermography Image Segmentation with a Gradient Vector Flow and a Convolutional Neural Network. Journal of Healthcare Engineering 2019.

• Tripathy, S., & Swarnkar, T. (2020). Unified Preprocessing and Enhancement Technique for Mammogram Images. Procedia Computer Science, (pp. 285–292). Elsevier B.V

• Tsochatzidis, L., Koutla, P., Costaridou, L., & Pratikakis, I. (2021). Integrating segmentation information into CNN for breast cancer diagnosis of mammographic masses. Computer Methods and Programs in Biomedicine, 200, 105913.

  Google Scholar 

• Valizadeh, A., Jafarzadeh Ghoushchi, S., Ranjbarzadeh, R., & Pourasad, Y. (2021). Presentation of a Segmentation Method for a Diabetic Retinopathy Patient’s Fundus Region Detection Using a Convolutional Neural Network. Computational Intelligence and Neuroscience 2021: 1–14.

• Wahab, N., Khan, A., & Lee, Y. S. (2017). Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection. Computers in Biology and Medicine, 85, 86–97.

  Google Scholar 

• Wan, M., Gu, G., Qian, W., Ren, K., Chen, Q., & Maldague, X. (2018). Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction. Remote Sensing 2018, 10(5), 682.

  Google Scholar 

• Wang, J., Heng, Y. J., Eliassen, A. H., Tamimi, R. M., Hazra, A., Carey, V. J. … Hankinson, S. E. (2017). Alcohol consumption and breast tumor gene expression. Breast Cancer Research, 19(1), 1–15.

  Google Scholar 

• Wu, P. C., Cheng, F. C., & Chen, Y. K. (2010). A weighting mean-separated sub-histogram equalization for contrast enhancement. 2010 International Conference on Biomedical Engineering and Computer Science, ICBECS 2010.

• Xi, X., Shi, H., Han, L., Wang, T., Ding, H. Y., Zhang, G. … Yin, Y. (2017). Breast tumor segmentation with prior knowledge learning. Neurocomputing, 237, 145–157.

  Google Scholar 

• Xian, M., Zhang, Y., Cheng, H. D., Xu, F., Zhang, B., & Ding, J. (2018). Automatic breast ultrasound image segmentation: A survey. Pattern Recognition, 79, 340–355.

  Google Scholar 

• Zebari, D. A., Zeebaree, D. Q., Abdulazeez, A. M., Haron, H., & Hamed, H. N. A. (2020). Improved threshold based and trainable fully automated segmentation for breast cancer boundary and pectoral muscle in mammogram images. IEEE Access, 8, 1–20.

  Google Scholar 

• Zeebaree, D. Q., Haron, H., Abdulazeez, A. M., & Zebari, D. A. (2019). Machine learning and Region Growing for Breast Cancer Segmentation. 2019 International Conference on Advanced Science and Engineering, ICOASE 2019, (pp. 88–93). Institute of Electrical and Electronics Engineers Inc

• Zhang, J., Saha, A., Zhu, Z., & Mazurowski, M. A. (2019). Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics. IEEE Transactions on Medical Imaging, 38(2), 435–447.

  Google Scholar 

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