Heuristically optimized weighted feature fusion with adaptive cascaded deep network: a novel breast cancer detection framework using mammogram images

References

• Ahmed AM, Rashid TA, Saeed SAM. 2020. Cat swarm optimization algorithm: a survey and performance evaluation. Comput Intell Neurosci. 2020:1–20. doi: 10.1155/2020/4854895.
   Web of Science ®Google Scholar
• Beeravolu AR, Azam S, Jonkman M, Shanmugam B, Kannoorpatti K, Anwar A. 2021. Preprocessing of breast cancer images to create datasets for deep-CNN. IEEE Acces. 9:33438–33463. doi: 10.1109/ACCESS.2021.3058773.
   Web of Science ®Google Scholar
• Brammya G, Praveena S, Preetha NSN, Ramya R. 2019. Deer hunting optimization algorithm: a new nature-inspired meta-heuristic paradigm. Computer J. doi: 10.1093/comjnl/bxy133.
   Google Scholar
• Carneiro G, Nascimento J, Bradley AP. 2017. Automated analysis of unregistered multi-view mammograms with deep learning. IEEE Trans Med Imaging. 36(11):2355–2365. doi: 10.1109/TMI.2017.2751523.
   PubMed Web of Science ®Google Scholar
• Chouhan N, Khan A, Shah JZ, Hussnain M, Khan MW. 2021. Deep convolutional neural network and emotional learning based breast cancer detection using digital mammography. Comput Biol Med. 132:104318. doi: 10.1016/j.compbiomed.2021.104318.
   PubMed Web of Science ®Google Scholar
• Dehghani M, Trojovsky P. 2022. Hybrid leader based optimization: a new stochastic optimization algorithm for solving optimization applications. Sci Rep. 12(1). doi: 10.1038/s41598-022-09514-0.
   Web of Science ®Google Scholar
• Ekta S, Saxena V. 2021. Breast cancer diagnosis from mammographic images using optimized feature selection and neural network architecture. Int J Imaging Syst Tech. 31(1):253–269. doi: 10.1002/ima.22467.
   Web of Science ®Google Scholar
• Elmoufidi A. 2022. Deep multiple instance learning for automatic breast cancer assessment using digital mammography. IEEE Trans Instrum Meas. 71(4503813):1–13. doi: 10.1109/TIM.2022.3177141.
   Google Scholar
• Gao M, Fessler JA, Chan H-P. 2021. Deep convolutional neural network with adversarial training for denoising digital breast tomosynthesis images. IEEE Trans Med Imaging. 40(7):1805–1816. doi: 10.1109/TMI.2021.3066896.
   PubMed Web of Science ®Google Scholar
• Geweid GGN, Abdallah MA. 2019. A novel approach for breast cancer investigation and recognition using M-Level set-based optimization functions. IEEE Acces. 7:136343–136357. doi: 10.1109/ACCESS.2019.2941990.
   Web of Science ®Google Scholar
• Hamed G, Marey M, Amin SE, Tolba MF. 2021. Automated breast cancer detection and classification in full field digital mammograms using two full and cropped detection paths approach. IEEE Acces. 9:116898–116913. doi: 10.1109/ACCESS.2021.3105924.
   Web of Science ®Google Scholar
• Hochreiter S, Schmidhuber J. 1997. Long short-term memory. Neural Comput. 9(8):1735–1780. doi: 10.1162/neco.1997.9.8.1735.
   PubMed Web of Science ®Google Scholar
• Jiang G, Wei J, Xu Y, He Z, Zeng H, Wu J, Qin G, Chen W, Lu Y. 2021. Synthesis of mammogram from digital breast tomosynthesis using deep convolutional neural network with gradient guided cGans. IEEE Trans Med Imaging. 40(8):2080–2091. doi: 10.1109/TMI.2021.3071544.
   PubMed Web of Science ®Google Scholar
• Kalita DJ, Singh VP, Kumar V. 2022. Detection of breast cancer through mammogram using Wavelet-based LBP features and IWD feature selection technique. Sn Comput Sci. 3(2):175. doi: 10.1007/s42979-022-01071-7.
   Google Scholar
• Kaur P, Singh G, Kaur P. 2019. Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification. Inform Med Unlocked. 16:100151. doi: 10.1016/j.imu.2019.01.001.
   Google Scholar
• Lee J, Nishikawa RM. 2022. Identifying Women with mammographically- occult breast cancer leveraging GAN-Simulated mammograms. IEEE Trans Med Imaging. 41(1):225–236. doi: 10.1109/TMI.2021.3108949.
   PubMed Web of Science ®Google Scholar
• Li X, Ma X, Xiao F, Wang F, Zhang S. 2020. Application of gated Recurrent unit (GRU) neural network for smart batch production prediction. Energies. 13(22):6121. doi: 10.3390/en13226121.
   Web of Science ®Google Scholar
• Loizidou K, Skouroumouni G, Nikolaou C, Pitris C. 2020. An automated breast micro-calcification detection and classification technique using temporal subtraction of mammograms. IEEE Acces. 8:52785–52795. doi: 10.1109/ACCESS.2020.2980616.
   Web of Science ®Google Scholar
• Mencattini A, Salmeri M, Lojacono R, Frigerio M, Caselli F. 2008. Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing. IEEE Trans Instrum Meas. 57(7):1422–1430. doi: 10.1109/TIM.2007.915470.
   Web of Science ®Google Scholar
• Mirjalili S. 2015. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowl Based Syst. 89:228–249. doi: 10.1016/j.knosys.2015.07.006.
   Web of Science ®Google Scholar
• Muduli D, Dash R, Majhi B. 2020. Automated breast cancer detection in digital mammograms: a moth flame optimization based ELM approach. Biomed Signal Process Control. 59:101912. doi: 10.1016/j.bspc.2020.101912.
   Web of Science ®Google Scholar
• Nyayapathi N, Lim R, Zhang H, Zheng W, Wang Y, Tiao M, Oh KW, Fan XC, Bonaccio E, Takabe K, et al. 2020. Dual Scan Mammoscope (DSM)—A new portable photoacoustic breast Imaging system with scanning in Craniocaudal Plane. IEEE Trans Biomed Eng. 67(5):1321–1327. doi:10.1109/TBME.2019.2936088.
   PubMed Web of Science ®Google Scholar
• Polakowski WE, Cournoyer DA, Rogers SK, DeSimio MP, Ruck DW, Hoffmeister JW, Raines RA. 1997. Computer-aided breast cancer detection and diagnosis of masses using difference of Gaussians and derivative-based feature saliency. IEEE Trans Med Imaging. 16(6):811–819. doi: 10.1109/42.650877.
   PubMed Web of Science ®Google Scholar
• Ramani R, Vanitha NS, Valarmathy S. 2013. The pre-processing techniques for breast cancer detection in mammography images. Inter J Image Grap Sign Proc. 5(5):47–54. doi: 10.5815/ijigsp.2013.05.06.
   Google Scholar
• Rezk NM, Purnaprajna M, Nordström T, Ul-Abdin Z. 2020. Recurrent neural networks: an embedded computing perspective. IEEE Acces. 8:57967–57996. doi: 10.1109/ACCESS.2020.2982416.
   Web of Science ®Google Scholar
• Saber A, Sakr M, Abo-Seida OM, Keshk A, Chen H. 2021. A novel deep-learning model for automatic detection and classification of breast cancer using the Transfer-learning technique. IEEE Acces. 9:71194–71209. doi: 10.1109/ACCESS.2021.3079204.
   Google Scholar
• Sameti M, Ward RK, Morgan-Parkes J, Palcic B. 2009. Image feature extraction in the last screening mammograms prior to detection of breast cancer. IEEE J Sel Top Signal Process. 3(1):46–52. doi: 10.1109/JSTSP.2008.2011163.
   Web of Science ®Google Scholar
• Tai S, Chen Z, Tsai W. 2014. An automatic mass detection system in mammograms based on complex texture features. IEEE J Biomed Health Inform. 18(2):618–627. doi: 10.1109/JBHI.2013.2279097.
   PubMed Web of Science ®Google Scholar
• Toz G, Erdoğmuş P. 2021. A novel hybrid image segmentation method for detection of suspicious regions in mammograms based on Adaptive multi-thresholding (HCOW. IEEE Acces. 9:85377–85391. doi: 10.1109/ACCESS.2021.3089077.
   Web of Science ®Google Scholar
• Vidyarthi A, Shad J, Sharma S, Agarwal P. 2019. Classification of breast microscopic Imaging using hybrid CLAHE-CNN deep architecture. Proceedings of the 2019 Twelfth International Conference on Contemporary Computing (IC3); Noida, India. 1–5.
   Google Scholar
• Vijayarajeswari R, Parthasarathy P, Vivekanandan S, Basha AA. 2019. Classification of mammogram for early detection of breast cancer using SVM classifier and Hough transform. Measurement. 146:800–805. doi: 10.1016/j.measurement.2019.05.083.
   Web of Science ®Google Scholar
• Wang Z, Li M, Wang H, Jiang H, Yao Y, Zhang H, Xin J. 2019. Breast cancer detection using extreme learning machine based on feature fusion with CNN deep features. IEEE Acces. 7:105146–105158. doi: 10.1109/ACCESS.2019.2892795.
   Google Scholar
• Zhang X, Zhang Y, Han EY, Jacobs N, Han Q, Wang X, Liu J. 2018. Classification of whole mammogram and tomosynthesis images using deep convolutional neural networks. IEEE Trans Nanobioscience. 17(3):237–242. doi: 10.1109/TNB.2018.2845103.
   PubMed Web of Science ®Google Scholar
• Zhen L, Chan AK. 2001. An artificial intelligent algorithm for tumor detection in screening mammogram. IEEE Trans Med Imaging. 20(7):559–567. doi: 10.1109/42.932741.
   PubMed Web of Science ®Google Scholar
• Zheng J, Lin D, Gao Z, Wang S, He M, Fan J. 2020. Deep learning assisted efficient AdaBoost algorithm for breast cancer detection and early diagnosis. IEEE Acces. 8:96946–96954. doi: 10.1109/ACCESS.2020.2993536.
   Google Scholar