Skip to main content
Springer Nature Link
Log in

OralNet: deep learning fusion for oral cancer identification from lips and tongue images using stochastic gradient based logistic regression

  • Original Article
  • Published:
Network Modeling Analysis in Health Informatics and Bioinformatics Aims and scope Submit manuscript

Abstract

Timely detection of oral cancer plays a critical role in improving survival rates. While traditional biopsy procedures can be invasive and uncomfortable, a more non-intrusive and convenient alternative is fluorescence visualization using optical instruments. This method not only provides real-time results but also facilitates repeat examinations. In the current research, an innovative strategy for oral cancer identification is introduced, utilizing images of lips and tongue. This involves the Feature Fusion Deep Convolution Neural Network with Stochastic Gradient based Logistic Regression for the diagnosis of Oral cancer from lips and tongue images. A benchmark dataset is assembled, and the ReNet-50 model is utilized to extract multilayer convolutional features. Subsequent layers, including pooling, transformation, and fusion layers, are designed to handle hierarchical features across different branches. Finally, the proposed model undergoes training via logistic regression on the extracted data using the Cross Entropy Loss, and the optimizer (Stochastic Gradient Descent with weight decay) is employed to update the model parameters. This type of integration provides a powerful tool for leveraging deep learning capabilities while maintaining interpretability through the logistic regression layer. The outcomes from the experimentation on the Oral Cancer (Lips and Tongue) Images Dataset demonstrate highly competitive classification results, achieving an accuracy of 97.8%, which compares favourably with numerous methods. This research significantly contributes to ongoing endeavours aimed at developing non-invasive and efficient techniques for early oral cancer detection, potentially enhancing patient outcomes and reducing mortality.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

The data set for this work is available in https://www.kaggle.com/shivam17299/oral-cancer-lips-and-tongue-images (Cao et al. 2023).

References

  • Acharya UR, Sree SV, Swapna G, Gupta S, Molinari F, Garberoglio R, Suri JS (2013) Effect of complex wavelet transform filter on thyroid tumor classification in three-dimensional ultrasound. Prod Inst Mech Eng, Part H J Eng Med 227(3):284–292

    Article  Google Scholar 

  • Alabi RO, Almangush A, Elmusrati M, Mäkitie AA (2022) Deep machine learning for oral cancer: from precise diagnosis to precision medicine. Front Oral Health 2:794248

    Article  Google Scholar 

  • Bacanin N et al (2020) Monarch butterfly optimization based convolutional neural network design. Mathematics 8(6):936

    Article  MathSciNet  Google Scholar 

  • Bhushan S, Alshehri M, Keshta I, Chakraverti AK, Rajpurohit J, Abugabah A (2022) An experimental analysis of various machine learning algorithms for hand gesture recognition. Electronics 11(6):968

    Article  Google Scholar 

  • Bochinski E, Senst T, Sikora T (2017) Hyper-parameter optimization for convolutional neural network committees based on evolutionary algorithms IEEE

  • Camalan S, Mahmood H, Binol H, Araújo ALD, Santos-Silva AR, Vargas PA, Gurcan MN (2021) Convolutional neural network-based clinical predictors of oral dysplasia: class activation map analysis of deep learning results. Cancers 13:1291

    Article  Google Scholar 

  • Cao Z, Gao X, Chang Y, Liu G, Pei Y (2023) Improving synthetic CT accuracy by combining the benefits of multiple normalized preprocesses. J Appl Clin Med Phy 24(8):e14004

    Article  Google Scholar 

  • Chaturvedi AK, Engels EA, Anderson WF, Gillison ML (2008) Incidence trends for human papillomavirus-related and-unrelated oral squamous cell carcinomas in the United States. J Clin Oncol 26(4):612–619

    Article  Google Scholar 

  • Civantos FJ, Stoeckli SJ, Takes RP, Woolgar JA, de Bree R, Paleri V, Ferlito A (2010) What is the role of sentinel lymph node biopsy in the management of oral cancer in 2010? Euro Arch Oto-Rhino-Laryngol 267:839–844

    Article  Google Scholar 

  • Das N, Hussain E, Mahanta LB (2020) Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional neural network. Neural Netw 128:47–60

    Article  Google Scholar 

  • Das M, Dash R, Mishra SK (2023) Automatic detection of oral squamous cell carcinoma from histopathological images of oral mucosa using deep convolutional neural network. Int J Environ Res Public Health 20(3):2131

    Article  Google Scholar 

  • Ding B, Zhang Z, Liang Y, Wang W, Hao S, Meng Z, Lv Y (2021) Detection of dental caries in oral photographs taken by mobile phones based on the YOLOv3 algorithm. Ann Trans Med 9(21):1622

    Article  Google Scholar 

  • Fan KM, Rimal J, Zhang P, Johnson NW (2022) Stark differences in cancer epidemiological data between GLOBOCAN and GBD: emphasis on oral cancer and wider implications. E Clin Med 54:10163

    Google Scholar 

  • Fu Q, Chen Y, Li Z et al (2020) A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: a retrospective study. E Clin Med 27:100558. https://doi.org/10.1016/j.eclinm.2020.100558

    Article  Google Scholar 

  • Gan Y, Tsay D, Amir SB, Marboe CC, Hendon CP (2016) Automated classification of optical coherence tomography images of human atrial tissue. J Biomed Opt 21(10):101407–101407

    Article  Google Scholar 

  • García-Pola M, Pons-Fuster E, Suárez-Fernández C, Seoane-Romero J, Romero-Méndez A, López-Jornet P (2021) Role of artificial intelligence in the early diagnosis of oral cancer. Scoping Rev Cancers 13(18):4600

    Google Scholar 

  • Goswami M, Maheshwari M, Baruah P D, Singh A, Gupta R (2021) Automated detection of oral cancer and dental caries using convolutional neural network. In 2021 9th international conference on reliability, infocom technologies and optimization (trends and future directions) (ICRITO) (pp 1–5), IEEE

  • Gupta B, Bray F, Kumar N, Johnson NW (2017) Associations between oral hygiene habits, diet, tobacco and alcohol and risk of oral cancer: a case–control study from India. Cancer Epidemiol 51:7–14. https://doi.org/10.1016/j.canep.2017.09.003

    Article  Google Scholar 

  • Hameed KS, Abubacker KS, Banumathi A, Ulaganathan G (2021) Immunohistochemical analysis of oral cancer tissue images using support vector machine. Measurement 173:108476

    Article  Google Scholar 

  • He K, Zhang X, Ren S, Sun J (Ed) (2016) Deep residual learning for image recognition. 2016 IEEE conference on computer vision and pattern recognition (CVPR), 27–30 June 2016

  • Hu T et al (2021) Real-time COVID-19 diagnosis from X-Ray images using deep CNN and extreme learning machines stabilized by chimp optimization algorithm. Biomed Signal Process Control 68:102764

    Article  Google Scholar 

  • Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv:1602.07360.

  • Ilhan B, Lin K, Guneri P, Wilder-Smith P (2020) Improving oral cancer outcomes with imaging and artificial intelligence. J Dent Res 99(3):241–248

    Article  Google Scholar 

  • Ilhan B, Guneri P, Wilder-Smith P (2021) The contribution of artificial intelligence to reducing the diagnostic delay in oral cancer. Oral Oncol 116:105254

    Article  Google Scholar 

  • Jeyaraj PR, Samuel Nadar ER (2019) Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm. J Cancer Res Clin Oncol 145:829–837

    Article  Google Scholar 

  • Jubair F et al (2022) A novel lightweight deep convolutional neural network for early detection of oral cancer. Oral Dis 28(4):1123–1130

    Article  Google Scholar 

  • Jurczyszyn K, Gedrange T, Kozakiewicz M (2020) Theoretical background to automated diagnosing of oral leukoplakia: a preliminary report. J Healthcare Eng. https://doi.org/10.1155/2020/8831161

    Article  Google Scholar 

  • Krishnan MMR, Acharya UR, Chakraborty C, Ray AK (2011) Automated diagnosis of oral cancer using higher order spectra features and local binary pattern: a comparative study. Technol Cancer Res Treat 10(5):443–455

    Article  Google Scholar 

  • Krishnan MMR, Venkatraghavan V, Acharya UR, Pal M, Paul RR, Min LC, Chakraborty C (2012) Automated oral cancer identification using histopathological images: a hybrid feature extraction paradigm. Micron 43:352–364

    Article  Google Scholar 

  • Laprise C, Shahul HP, Madathil SA, Thekkepurakkal AS, Castonguay G, Varghese I, Shiraz S, Allison P, Schlecht NF, Rousseau MC, Franco EL, Nicolau B (2016) Periodontal diseases and risk of oral cancer in Southern India: results from the HeNCe Life study. Int J Canc 139:1512–1519

    Article  Google Scholar 

  • Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z et al (2021) Swin transformer: hierarchical vision Transformer using Shifted Windows. ArXiv.abs/2103.14030

  • Lu S, Wang S-H, Zhang Y-D (2021) Detection of abnormal brain in MRI via improved AlexNet and ELM optimized by chaotic bat algorithm. Neural Comput & Appl 33(17):10799–10811

    Article  Google Scholar 

  • Lu S-Y et al (2022) TBNet: a context-aware graph network for tuberculosis diagnosis. Comput Methods Programs Biomed 214:106587

    Article  Google Scholar 

  • Nanditha B R, Geetha A, Chandrashekar H S, Dinesh M S & Murali S (2021) An ensemble deep neural network approach for oral cancer screening.

  • Nguyen T, Nguyen G, Nguyen BM (2020) EO-CNN: an enhanced CNN model trained by equilibrium optimization for traffic transportation prediction. Proced Comput Sci 176:800–809

    Article  Google Scholar 

  • Özmen EE, Kölüş T, İçen V (2023) A Novel Method for the Detection of Oral Cancers. Deep Learning 2(23):97

    Google Scholar 

  • Pan X, Zhang T, Yang Q, Yang D, Rwigema JC, Qi XS (2020) Survival prediction for oral tongue cancer patients via probabilistic genetic algorithm optimized neural network models. Br J Radiol 93(1112):20190825

    Article  Google Scholar 

  • Pande P, Shrestha S, Park J, Serafino MJ, Gimenez-Conti I, Brandon J, Jo JA (2014) Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch. J Biomed Optics 19(8):086022–086022

    Article  Google Scholar 

  • Parkavi A, Tiriyar Y, Borthakur P J, Patil P & Haleem M B (2023) Deep learning techniques for the detection and classification of oral cancer using histopathological images. In: 2023 international conference on circuit power and computing technologies (ICCPCT) (pp 1625–1630) IEEE

  • https://pragativadi.com/india-spent-approximately-rs-2386-crores-in-2020-on-oral-cancer-treatment-study/

  • Rahman MS, Ingole N, Roblyer D, Stepanek V, Richards-Kortum R, Gillenwater A, Chaturvedi P (2010) Evaluation of a low-cost, portable imaging system for early detection of oral cancer. Head Neck Oncol 2(1):1–8

    Article  Google Scholar 

  • Ray S (2018) Disease classification within dermascopic images using features extracted by resnet50 and classification through deep forest. arXiv preprint arXiv:1807.05711

  • Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031

    Article  Google Scholar 

  • Sezavar A, Farsi H, Mohamadzadeh S (2019) A modified grasshopper optimization algorithm combined with cnn for content based image retrieval. Int J Eng 32(7):924–930

    Google Scholar 

  • Skandarajah A, Sunny SP, Gurpur P, Reber CD, D’Ambrosio MV, Raghavan N, Fletcher D (2017) Mobile microscopy as a screening tool for oral cancer in India: a pilot study. PloS one 12(11):e0188440

    Article  Google Scholar 

  • Tanriver G, Tekkesin MS, Ergen O (2021) Automated detection and classification of oral lesions using deep learning to detect oral potentially malignant disorders. Cancers (Basel) 13(11):2766. https://doi.org/10.3390/cancers13112766

    Article  Google Scholar 

  • Tzougas G, Kutzkov K (2023) Enhancing logistic regression using neural networks for classification in actuarial learning. Algorithms 16(2):99

    Article  Google Scholar 

  • Warin K, Limprasert W, Suebnukarn S, Jinaporntham S, Jantana P (2021) Automatic classification and detection of oral cancer in photographic images using deep learning algorithms. J Oral Pathol Med 50(9):911–918. https://doi.org/10.1111/jop.13227

    Article  Google Scholar 

  • Warin K, Limprasert W, Suebnukarn S, Jinaporntham S, Jantana P, Vicharueang S (2022) AI-based analysis of oral lesions using novel deep convolutional neural networks for early detection of oral cancer. PLoS ONE 17(8):e0273508

    Article  Google Scholar 

  • Warnakulasuriya S, Chen THH (2022) Areca nut and oral cancer: evidence from studies conducted in humans. J Dent Res 101(10):1139–1146

    Article  Google Scholar 

  • Welikala RA, Remagnino P, Lim JH, Chan CS, Rajendran S, Kallarakkal TG, Barman SA (2020) Automated detection and classification of oral lesions using deep learning for early detection of oral cancer. IEEE Access 8:132677–132693

    Article  Google Scholar 

  • Wen L, Li X, Gao L (2020) A transfer convolutional neural network for fault diagnosis based on ResNet-50. Neural Comput Appl 32:6111–6124

    Article  Google Scholar 

  • World Health Organization (2013) Oral health surveys: basic methods. World Health Organization.

  • Xu S et al (2019) An early diagnosis of oral cancer based on three-dimensional convolutional neural networks. IEEE Access 7:158603–158611

    Article  Google Scholar 

  • Ye Y, Huang Q, Rong Y, Yu X, Liang W, Chen Y, Xiong S (2023) Field detection of small pests through stochastic gradient descent with genetic algorithm. Comput Electron Agric 206:107694

    Article  Google Scholar 

  • You W, Hao A, Li S, Wang Y, Xia B (2020) Deep learning-based dental plaque detection on primary teeth: a comparison with clinical assessments. BMC Oral Health 20:1–7

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of IT, School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, 613401, India

    Pradeepa Sampath

  2. Department of CSE, School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, 613401, India

    N. Sasikaladevi

  3. Department of Artificial Intelligence and Data Science, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, 641202, India

    S. Vimal

  4. Department of Artificial Intelligence and Data Science, Ramco Institute of Technology, Rajapalayam, Tamil Nadu, India

    M. Kaliappan

Authors
  1. Pradeepa Sampath
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. N. Sasikaladevi
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. S. Vimal
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. M. Kaliappan
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

PS,SN,VS and KM have conceptualized the study and participated in design of the study, and manuscript review, while PS,SN and VS have participated in data analysis, design of the study, manuscript preparation, literature search, and manuscript review.

Corresponding authors

Correspondence to Pradeepa Sampath or S. Vimal.

Ethics declarations

Conflict of interest

The authors suggest no conflict of interest. The study’s design, data collection, analysis, or interpretation; the preparation of the paper; or the choice to publish the findings were all made independently of the funders.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sampath, P., Sasikaladevi, N., Vimal, S. et al. OralNet: deep learning fusion for oral cancer identification from lips and tongue images using stochastic gradient based logistic regression. Netw Model Anal Health Inform Bioinforma 13, 24 (2024). https://doi.org/10.1007/s13721-024-00459-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13721-024-00459-0

Keywords