PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Dual-modality synthetic mammogram construction for breast lesion detection using U-DARTS

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Multimodal image fusion is an emergent research area for cancer detection. It provides a wide variety of visual qualities for the accurate medical diagnosis. However, this process requires accurate registration of each image modality for its efficient and effective use. To address the aforementioned issue, a novel synthetic mammogram construction model is proposed. An image enhancement approach is applied to enhance the image quality. Dual-modality structural feature (DMSF) based mapping function is designed to transform a mammogram from a thermal image segment. This paper also proposes modified Differentiable ARchiTecture Search (DARTS) named as (U-DARTS) to detect and classify the breast lesion. In U-DARTS, a stochastic gradient descent optimizer is used. The proposed approach is tested over DMR and INbreast datasets. The results exhibit a significant improvement in the performance of the proposed model over the existing techniques. The validation and testing accuracies of 98% and 91%, respectively, are achieved. Overall, the proposed approach establishes supremacy in so far as the mammogram construction and further lesion detection are concerned.
Słowa kluczowe
Twórcy
  • Department of Electronics and Communication, Delhi Technological University, Delhi, India
autor
  • Department of Electronics and Communication, Delhi Technological University, Delhi, India
autor
  • Department of Computer Science Engineering, National Institute of Technology, Hamirpur, India
Bibliografia
  • [1] US Preventive Services Task Force. Screening for breast cancer: US Preventive Services Task Force recommendation statement. Ann Intern Med 2009;151(10):716–1236.
  • [2] Schwartz LM, Woloshin S, Sox HC, Fischhoff B, Welch HG. US women’s attitudes to false positive mammography results and detection of ductal carcinoma in situ: cross sectional survey. BMJ 2000;320(7250):1635–40.
  • [3] Lehman CD, Arao RF, Sprague BL, Lee JM, Buist DS, Kerlikowske K, et al. National performance benchmarks for modern screening digital mammography: update from the Breast Cancer Surveillance Consortium. Radiology 2017;283(1):49–58.
  • [4] Ibrahim A, Mohammed S, Ali HA, Hussein SE. Breast cancer segmentation from thermal images based on chaotic salp swarm algorithm. IEEE Access 2020;8:122121–34.
  • [5] Nelson TR, Cerviño LI, Boone JM, Lindfors KK. Classification of breast computed tomography data. Med Phys 2008;35(3):1078–86.
  • [6] Jalalian A, Mashohor S, Mahmud R, Karasfi B, Saripan MI, Ramli AR. Computer-assisted diagnosis system for breast cancer in computed tomography laser mammography (CTLM). J Digit Imaging 2017;30(6):796–811.
  • [7] Jaglan P, Dass R, Duhan M. Breast cancer detection techniques: issues and challenges. J Inst Eng (India): Series B 2019;100(4):379–86.
  • [8] Li J, Cheng L, Xia T, Ni H, Li J. Multi-Scale Fusion U-Net for the Segmentation of Breast Lesions. IEEE Access 2021;9:137125–39.
  • [9] Pawar M, Talbar S. Local entropy maximization based image fusion for contrast enhancement of mammogram. J King Saud Univ - Comput Inf Sci 2021;33(2):150–60.
  • [10] Alsaedi D, Melnikov A, Muzaffar K, Mandelis A, Ramahi OM. 2021. A Microwave-Thermography Hybrid Technique for Breast Cancer Detection. IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology.
  • [11] Hopp T, Duric N, Ruiter NV. Image fusion of Ultrasound Computer Tomography volumes with X-ray mammograms using a biomechanical model based 2D/3D registration. Comput Med Imaging Graph 2015;40:170–81.
  • [12] Dassault Systèmes, Abaqus 6.11 online documentation; 2011.
  • [13] Pizer SM, Zimmerman JB, Staab EV. Adaptive grey level assignment in CT scan display. J Comput Assist Tomogr 1984;8(2):300–5.
  • [14] Yang H, Sun J, Carass A, Zhao C, Lee J, Prince JL, et al. Unsupervised MR-to-CT synthesis using structure-constrained cycleGAN. IEEE Trans Med Imaging 2020;39(12):4249–61.
  • [15] Liu H, Simonyan K, Yang Y. 2018. Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055.
  • [16] Wolterink JM, Dinkla AM, Savenije MH, Seevinck PR, van den Berg, C.A. and Išgum, I., 2017, September. Deep MR to CT synthesis using unpaired data. In International workshop on simulation and synthesis in medical imaging (pp. 14-23). Springer, Cham.
  • [17] Zhang Z, Yang L, Zheng Y. Translating and segmenting multimodal medical volumes with cycle-and shape-consistency generative adversarial network. In: In Proceedings of the IEEE conference on computer vision and pattern Recognition. p. 9242–51.
  • [18] Yang H, Sun J, Carass A, Zhao C, Lee J, Xu Z, Prince J. 2018. Unpaired brain MR-to-CT synthesis using a structure-constrained CycleGAN. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (pp. 174-182). Springer, Cham.
  • [19] Ge Y, Xue Z, Cao T, Liao S. 2019, March. Unpaired whole-body MR to CT synthesis with correlation coefficient constrained adversarial learning. In Medical Imaging 2019: Image Processing (Vol. 10949, p. 1094905). International Society for Optics and Photonics.
  • [20] Al-Antari MA, Al-Masni MA, Choi MT, Han SM, Kim TS. A fully integrated computer-aided diagnosis system for digital X- ray mammograms via deep learning detection, segmentation, and classification. Int J Med Inf 2018;117:44–54.
  • [21] Agarwal R, Díaz O, Yap MH, Lladó X, Martí R. Deep learning for mass detection in Full Field Digital Mammograms. Comput Biol Med 2020;121.
  • [22] Chanda PB, Sarkar SK. Detection and classification of breast cancer in mammographic images using efficient image segmentation technique. In: Advances in control, signal processing and energy systems. Singapore: Springer; 2020. p. 107–17.
  • [23] Aly GH, Marey M, El-Sayed SA, Tolba MF. YOLO Based Breast Masses Detection and Classification in Full-Field Digital Mammograms. Comput Methods Programs Biomed 2021;200.
  • [24] Hamed G, Marey M, Amin SE, Tolba MF. Automated breast cancer detection and classification in full field digital mammograms using two full and cropped detection paths approach. IEEE Access 2021;9:116898–913.
  • [25] Visual Lab. A Methodology for Breast Disease Computer-Aided Diagnosis using dynamic thermography. Available Online: http://visual.ic.uff.br/en/proeng (accessed on 11 July 2019).
  • [26] Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS. Inbreast: toward a full-field digital mammographic database. Acad Radiol 2012;19(2):236–48.
  • [27] Sammut C, Webb GI, eds., Encyclopedia of machine learning. Springer Science & Business Media; 2011.
  • [28] Naidu V, Raol J. Pixel-level image fusion using wavelets and principal component analysis. Defence Science Journal 2008;58(3):338–52.
  • [29] Rajinikanth V, Kadry S, Taniar D, Damaševičius R, Rauf HT. 2021, March. Breast-cancer detection using thermal images with marine-predators-algorithm selected features. In 2021 seventh international conference on bio signals, images, and instrumentation (ICBSII) (pp. 1-6). IEEE.
  • [30] Irfan R, Almazroi AA, Rauf HT, Damaševičius R, Nasr EA, Abdelgawad AE. Dilated semantic segmentation for breast ultrasonic lesion detection using parallel feature fusion. Diagnostics 2021;11(7):1212.
  • [31] Kadry S, Damaševičius R, Taniar D, Rajinikanth V, Lawal IA. 2021, March. Extraction of tumour in breast MRI using joint thresholding and segmentation–A study. In 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII) (pp. 1-5). IEEE.
  • [32] Zebari DA, Ibrahim DA, Zeebaree DQ, Mohammed MA, Haron H, Zebari NA. Damaševičius, R. and Maskeliūnas, R., 2021. MIAS, DDSM, INbreast, and BCDR Applied Sciences, 11(24), p.12122.
  • [33] Zeebaree DQ, Abdulazeez AM, Zebari DA, Haron H, Hamed HNA. Multi-level fusion in ultrasound for cancer detection based on uniform LBP features. Computers, Materials & Continua 2021;66(3):3363–82.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bf96c35d-6972-4310-b59a-3995931eb121
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.