IR-GAN: improved generative adversarial networks for infrared breast image segmentation

References

• Hassan NM, Hamad S, Mahar K. Mammogram breast cancer CAD systems for mass detection and classification: a review. Multimed Tools Appl. 2022;81(14):20043–20075. doi: 10.1007/s11042-022-12332-1
   Web of Science ®Google Scholar
• Subasi A, Kandpal AD, Raj KA, et al. Breast cancer detection from mammograms using artificial intelligence. In: Applications of artificial intelligence in medical imaging. Elsevier; 2022. p. 109–136. doi: 10.1016/B978-0-443-18450-5.00005-0
   Google Scholar
• Wilson AN, Gupta KA, Koduru BH, et al. Recent advances in thermal imaging and its applications using machine learning: a review. IEEE Sensors J. 2023 Feb 15;23(4):3395–3407.
   Web of Science ®Google Scholar
• Dalmia, A, Kakileti, ST, Manjunath G. Exploring deep learning networks for tumour segmentation in infrared images. Quant Infrared Thermogr J. 2020;17(3):153–168. doi: 10.1080/17686733.2019.1619355
   Web of Science ®Google Scholar
• Özdil A, Yilmaz B. Medical infrared thermal image based fatty liver classification using machine and deep learning. Quant Infrared Thermogr J. 2023:1–18. doi: 10.1080/17686733.2022.2158678
   Web of Science ®Google Scholar
• Hesamian MH, Jia W, He X, et al. Deep learning techniques for medical image segmentation: achievements and challenges. J Digit Imaging. 2019;32(4):582–596. doi: 10.1007/s10278-019-00227-x
   PubMed Web of Science ®Google Scholar
• Antonelli L, De Simone V, di Serafino D. A view of computational models for image segmentation. Ann Dell’Universita’ di Ferrara. 2022;68(2):277–294. doi:10.1007/s11565-022-00417-6
   Google Scholar
• Usha Rani Gogoi MKB, Majumdar G. MMSHRs: a morphology model of suspicious hyperthermic regions for degree of severity prediction from breast thermograms. Quant Infrared Thermogr J. 2023;20(4):157–181. doi:10.1080/17686733.2022.2097614
   Web of Science ®Google Scholar
• Sánchez-Ruiz D, Olmos-Pineda I, Olvera-López JA. Automatic region of interest segmentation for breast thermogram image classification. Pattern Recognit Lett. 2020;135:72–81. doi:10.1016/j.patrec.2020.03.025
   Web of Science ®Google Scholar
• Mahnoosh Zarei AR, Hamidpour SSF. Breast cancer segmentation based on modified Gaussian mean shift algorithm for infrared thermal images. Comput Methods Biomech Biomed Eng Imaging Vis. 2021;9(6):574–580. doi:10.1080/21681163.2021.1897884
   Web of Science ®Google Scholar
• Sharma R, Sharma JB, Maheshwari R, et al. Early anomaly prediction in breast thermogram by hybrid model consisting of superpixel segmentation, sparse feature descriptors and extreme learning machine classifier. Biomed Signal Process Control. 2021;70:103011. doi:10.1016/j.bspc.2021.103011
   Web of Science ®Google Scholar
• Hakim AS, Awale RN. Extraction of hottest blood vessels from breast thermograms using state-of-the-art image segmentation methods. Quant Infrared Thermogr J. 2022;19(5):347–365. doi: 10.1080/17686733.2021.1974209
   Web of Science ®Google Scholar
• Venkatachalam N, Shanmugam L, Heltin GC, et al. Enhanced segmentation of inflamed ROI to improve the accuracy of identifying benign and malignant cases in breast thermogram. J Oncol. 2021;2021:1–17. Hindawi. doi: 10.1155/2021/5566853
   Web of Science ®Google Scholar
• Gupta S, Sadiq M, Gupta M, et al. Semi-automatic segmentation of breast cancer for mammograms based on watershed segmentation. In: Proceedings of the 5th national conference for nation development; New Delhi, India; 2011.
   Google Scholar
• Thamil Selvi J, Priya E, Rajinikanth V. Signal and Image Processing Techniques for the Development of Intelligent Healthcare Systems. Singapore: Springer Nature; 2021. p. 87–102. doi:10.1007/978-981-15-6141-2_5
   Google Scholar
• Houssein EH, Emam MM, Ali AA. An efficient multilevel thresholding segmentation method for thermography breast cancer imaging based on improved chimp optimization algorithm. Expert Syst Appl. 2021;185:115651. doi:10.1016/j.eswa.2021.115651
   Web of Science ®Google Scholar
• Raja N, Rajinikanth V, Fernandes SL, et al. Segmentation of breast thermal images using Kapur’s entropy and hidden Markov random field. J Med Imaging Heal Inf. 2017;7(8):1825–1829. doi:10.1166/jmihi.2017.2267
   Web of Science ®Google Scholar
• Li Y, Xiao N, Ouyang W. Improved generative adversarial networks with reconstruction loss. Neurocomputing. 2019;323:363–372. doi:10.1016/j.neucom.2018.10.014
   Web of Science ®Google Scholar
• Li C, Xia W, Yan Y, et al. Segmenting objects in day and night: edge-conditioned CNN for thermal image semantic segmentation. IEEE Trans Neural Networks Learn Syst. 2021;32(7):3069–3082. doi: 10.1109/TNNLS.2020.3009373
   PubMed Web of Science ®Google Scholar
• Kakileti ST, Manjunath G, Madhu HJ. Cascaded CNN for view independent breast segmentation in thermal images. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Berlin, Germany; 2019. p. 6294–6297.
   Google Scholar
• Janssens O, Schulz R, Slavkovikj V, et al. Thermal image based fault diagnosis for rotating machinery. Infrared Phys & Technol. 2015;73:78–87. doi:10.1016/j.infrared.2015.09.004
   Web of Science ®Google Scholar
• Liu X, Song L, Liu S, et al. A review of deep-learning-based medical image segmentation methods. Sustainability. 2021;13(3):1224. doi:10.3390/su13031224
   Web of Science ®Google Scholar
• Protopapadakis E, Doulamis A, Doulamis N, et al. Stacked autoencoders driven by semi-supervised learning for building extraction from near infrared remote sens imagery. Remote Sens. 2021;13(3):371. doi: 10.3390/rs13030371
   Google Scholar
• Lou A, Guan S, Loew M. Cfpnet-m: a light-weight encoder-decoder based network for multimodal biomedical image real-time segmentation. Comput Biol Med. 2023;154:106579. doi:10.1016/j.compbiomed.2023.106579
   PubMed Web of Science ®Google Scholar
• Tayel MB, Elbagoury AM. Breast infrared thermography segmentation based on adaptive tuning of a fully convolutional network. Curr Med Imaging. 2020;16(5):611–621. doi: 10.2174/1573405615666190503142031
   PubMed Web of Science ®Google Scholar
• Hou J, Zhang D, Wu W, et al. A generative adversarial network for infrared and visible image fusion based on semantic segmentation. Entropy. 2021;23(3):376. doi: 10.3390/e23030376
   PubMed Web of Science ®Google Scholar
• Mambou SJ, Maresova P, Krejcar O, et al. Breast cancer detection using infrared thermal imaging and a deep learning model. Sensors. 2018;18(9):2799. doi: 10.3390/s18092799
   PubMed Web of Science ®Google Scholar
• Sánchez-Cauce R, Pérez-Martín J, Luque M. Multi-input convolutional neural network for breast cancer detection using thermal images and clinical data. Comput Methods Programs Biomed. 2021;204:106045. doi: 10.1016/j.cmpb.2021.106045
   PubMed Web of Science ®Google Scholar
• Allugunti VR. Breast cancer detection based on thermographic images using machine learning and deep learning algorithms. Int J Eng Comput Sci. 2022;4(1):49–56. doi:10.33545/26633582.2022.v4.i1a.68
   Google Scholar
• Zuluaga-Gomez J, Al Masry Z, Benaggoune K, et al. A CNN-based methodology for breast cancer diagnosis using thermal images. Comput Methods Biomech Biomed Eng. 2021;9(2):131–145. doi: 10.1080/21681163.2020.1824685
   Web of Science ®Google Scholar
• Gomathi P, Muniraj C, Periasamy PS. Digital infrared thermal imaging system based breast cancer diagnosis using 4D U-Net segmentation. Biomed Signal Process Control. 2023;85:104792. doi:10.1016/j.bspc.2023.104792
   Web of Science ®Google Scholar
• Carlos de Carvalho E, Martins Coelho A, Conci A, et al. U-Net convolutional neural networks for breast IR imaging segmentation on frontal and lateral view. Comput Methods Biomech Biomed Eng Imaging Vis. 2023 May;11(3):311–316. doi: 10.1080/21681163.2022.2040053
   Web of Science ®Google Scholar
• Olota M, Alsadoon A, Alsadoon OH, et al. Modified anisotropic diffusion and level-set segmentation for breast cancer. Multimed Tools Appl. 2023;1–23. doi: 10.1007/s11042-023-16021-5
   Web of Science ®Google Scholar
• da Queiroz KFFC, de Queiroz Júnior JRA, Dourado H, et al. Automatic segmentation of region of interest for breast thermographic image classification. Res Biomed Eng. 2023;39(1):199–208. doi:10.1007/s42600-023-00265-z
   Google Scholar
• Etehadtavakol M, Emrani Z, Ng EYK. Improved extraction of the extreme thermal regions of breast IR images. In: Pal K, Ari S, Bit A, Bhattacharyya S, editors. Advanced methods in biomedical signal processing and analysis. London: Elsevier; 2023. p. 243–256. doi:10.1016/C2020-0-02228-0
   Google Scholar
• Venkatachalam N, Shanmugam L, Heltin Genitha C, et al. Automated breast boundary segmentation to improve the accuracy of identifying abnormalities in breast thermograms. IETE J Res. 2023;1–10. doi: 10.1080/03772063.2023.2194277
   Web of Science ®Google Scholar
• Senalp FM, Ceylan M. A new approach for super-resolution and classification applications on neonatal thermal images. Quant Infrared Thermogr J. 1–18. doi: 10.1080/17686733.2023.2179282
   Web of Science ®Google Scholar
• Tian L, Wang Z, Liu W, et al. An improved generative adversarial network with modified loss function for crack detection in electromagnetic nondestructive testing. Complex Intell Syst. 2022;8(1):467–476. doi: 10.1007/s40747-021-00477-9
   Web of Science ®Google Scholar
• Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks. Commun ACM. 2020;63(11):139–144. doi: 10.1145/3422622
   Web of Science ®Google Scholar
• Yuan L, Yu S, Yang Z, et al. A data balancing approach based on generative adversarial network. Futur Gener Comput Syst. 2023;141:768–776. doi:10.1016/j.future.2022.12.024
   Web of Science ®Google Scholar
• Ruan Q, Wu Q, Yao J, et al. An efficient tongue segmentation model based on u-net framework. Int J Pattern Recognit Artif Intell. 2021;35(16):2154035. doi:10.1142/S0218001421540355
   Web of Science ®Google Scholar
• Drozdzal M, Vorontsov E, Chartrand G, et al. The importance of skip connections in biomedical image segmentation. In: Gustavo C, editor. International workshop on deep learning in medical image analysis, international workshop on large-scale annotation of biomedical data and expert label synthesis. Vol. 1. Cham, Switzerland: Springer; 2016. p. 179–187. doi:10.1007/978-3-319-46976-8
   Google Scholar
• Demir U, Unal G. Patch-based image inpainting with generative adversarial networks. arXiv Prepr. 2018;1:1–28. arXiv180307422.
   Google Scholar
• Misra D. Mish: a self regularized non-monotonic neural activation function. arXiv Prepr arXiv190808681. 2019.
   Google Scholar
• Yarotsky D. Error bounds for approximations with deep ReLU networks. Neural Networks. 2017;94:103–114. doi:10.1016/j.neunet.2017.07.002
   PubMed Web of Science ®Google Scholar
• da Silva LF, Saade DCM, Sequeiros GO, et al. A new database for breast research with infrared image. J Med Imaging Heal Informatics. 2014;4(1):92–100. doi: 10.1166/jmihi.2014.1226
   Web of Science ®Google Scholar
• Krishna S, George B. An affordable solution for the recognition of abnormality in breast thermogram. Multimed Tools Appl. 2021;80(18):28303–28328. doi:10.1007/s11042-021-11082-w
   Web of Science ®Google Scholar
• Dey A, Rajan S. Red-plane Asymmetry Analysis of Breast Thermograms for Cancer Detection. 2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA); Messina, Italy; 2022. p. 1–6. doi:10.1109/MeMeA54994.2022.9856520
   Google Scholar
• Rosca M, Lakshminarayanan B, Warde-Farley D, et al. Variational approaches for auto-encoding generative adversarial networks. arXiv Prepr arXiv170604987. 2017.
   Google Scholar