A Deep Supervision-Enhanced U-Net Model for Laryngeal Cancer Early Detection
Keywords:
U-Net Architecture, Dice Loss, Laryngeal Cancer, Stochastic Learning, Segmentation, Training rate and ValidationAbstract
In this study, a modified U-Net architecture was used to segment the laryngeal nodule completely automatically while being closely monitored. Diagnosing laryngeal cancer is difficult since the larynx is complicated and the illness only slightly alters it. Deep learning algorithms have shown promise in medical image processing, including the diagnosis of cancer. U-Net is a well-liked deep learning architecture for picture segmentation. The outcomes are compared to those obtained using other contemporary methods and the original U-Net that was published in order to arrive at the original conclusions.
In order to investigate how this affects the segmentation of Laryngeal nodules overall, this work additionally substitutes deconvolution layers for the up-sampling layers in both networks. During training, data augmentation was used right away. Initially, distortion and rotation combinations are used because of the poor quality of the source photos. This resulted in a limited data enrichment technique for combinations. Using the same parameter settings, the network is trained by substituting deconvolutional layers for all of the upsampling layers. There is greater dice overlap when comparing the suggested method for Laryngeal nodule segmentation to the state-of-the-art.
Downloads
References
Johnson DE, Burtness B, Leemans CR, Lui VW, Bauman JE, Grandis JR. Head and neck squamous cell carcinoma. Nature reviews Disease primers. 2020 Nov 26;6(1):92.
Hoffman HT, Porter K, Karnell LH, Cooper JS, Weber RS, Langer CJ, Ang KK, Gay G, Stewart A, Robinson RA. Laryngeal cancer in the United States: changes in demographics, patterns of care, and survival. The Laryngoscope. 2006 Sep;116(S111):1-3.
Forastiere AA, Ismaila N, Lewin JS, Nathan CA, Adelstein DJ, Eisbruch A, Fass G, Fisher SG, Laurie SA, Le QT, O’Malley B. Use of larynx-preservation strategies in the treatment of laryngeal cancer: American Society of Clinical Oncology clinical practice guideline update. Journal of Clinical Oncology. 2018 Apr 10;36(11):1143-69.
Miller KD, Siegel RL, Lin CC, Mariotto AB, Kramer JL, Rowland JH, Stein KD, Alteri R, Jemal A. Cancer treatment and survivorship statistics, 2016. CA: a cancer journal for clinicians. 2016 Jul;66(4):271-89.
Swets JA. Measuring the accuracy of diagnostic systems. Science. 1988 Jun 3;240(4857):1285-93.
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nature medicine. 2019 Jan;25(1):44-56.
Kermany DS, Goldbaum M, Cai W, Valentim CC, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F, Dong J. Identifying medical diagnoses and treatable diseases by image-based deep learning. cell. 2018 Feb 22;172(5):1122-31.
Lu D, Weng Q. A survey of image classification methods and techniques for improving classification performance. International journal of Remote sensing. 2007 Mar 1;28(5):823-70.
Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J. Unet++: A nested u-net architecture for medical image segmentation. InDeep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4 2018 (pp. 3-11). Springer International Publishing.
Hasan MM, Islam MU, Sadeq MJ, Fung WK, Uddin J. Review on the evaluation and development of artificial intelligence for COVID-19 containment. Sensors. 2023 Jan 3;23(1):527.
Abdusalomov AB, Mukhiddinov M, Whangbo TK. Brain tumor detection based on deep learning approaches and magnetic resonance imaging. Cancers. 2023 Aug 18;15(16):4172.
Dou Q, So TY, Jiang M, Liu Q, Vardhanabhuti V, Kaissis G, Li Z, Si W, Lee HH, Yu K, Feng Z. Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study. NPJ digital medicine. 2021 Mar 29;4(1):60.
Akbari H, Halig LV, Schuster DM, Osunkoya A, Master V, Nieh PT, Chen GZ, Fei B. Hyperspectral imaging and quantitative analysis for prostate cancer detection. Journal of biomedical optics. 2012 Jul 1;17(7):076005-.
Li C, Chen H, Li X, Xu N, Hu Z, Xue D, Qi S, Ma H, Zhang L, Sun H. A review for cervical histopathology image analysis using machine vision approaches. Artificial Intelligence Review. 2020 Oct;53:4821-62.
Sahoo PK, Mishra S, Panigrahi R, Bhoi AK, Barsocchi P. An improvised deep-learning-based mask R-CNN model for laryngeal cancer detection using CT images. Sensors. 2022 Nov 15;22(22):8834.
Esmaeili N. Computer-Aided-Diagnosis for Laryngeal Lesion Assessment: A Feature Extraction and Machine Learning Approach Applied on Enhanced Contact Endoscopy Images (Doctoral dissertation, Technische Universität München).
Hershey S, Chaudhuri S, Ellis DP, Gemmeke JF, Jansen A, Moore RC, Plakal M, Platt D, Saurous RA, Seybold B, Slaney M. CNN architectures for large-scale audio classification. In2017 ieee international conference on acoustics, speech and signal processing (icassp) 2017 Mar 5 (pp. 131-135). IEEE.
Han Y, Ma Y, Wu Z, Zhang F, Zheng D, Liu X, Tao L, Liang Z, Yang Z, Li X, Huang J. Histologic subtype classification of non-small cell lung cancer using PET/CT images. European journal of nuclear medicine and molecular imaging. 2021 Feb;48:350-60.
Ibtehaz N, Rahman MS. MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural networks. 2020 Jan 1;121:74-87.
Kang DH, Cha YJ. Efficient attention-based deep encoder and decoder for automatic crack segmentation. Structural Health Monitoring. 2022 Sep;21(5):2190-205.
Nixon M, Aguado A. Feature extraction and image processing for computer vision. Academic press; 2019 Nov 17.
Badrinarayanan V, Kendall A, Cipolla R. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence. 2017 Jan 2;39(12):2481-95.
Zhou Z, Siddiquee MM, Tajbakhsh N, Liang J. Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging. 2019 Dec 13;39(6):1856-67.
Litjens G, Kooi T, Bejnordi BE, Setio AA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI. A survey on deep learning in medical image analysis. Medical image analysis. 2017 Dec 1;42:60-88.
Ker J, Wang L, Rao J, Lim T. Deep learning applications in medical image analysis. Ieee Access. 2017 Dec 29;6:9375-89
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
