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Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm

  • Pandia Rajan JeyarajEmail author
  • Edward Rajan Samuel Nadar
Original Article – Cancer Research

Abstract

Purpose

Oral cancer is a complex wide spread cancer, which has high severity. Using advanced technology and deep learning algorithm early detection and classification are made possible. Medical imaging technique, computer-aided diagnosis and detection can make potential changes in cancer treatment. In this research work, we have developed a deep learning algorithm for automated, computer-aided oral cancer detecting system by investigating patient hyperspectral images.

Methods

To validate the proposed regression-based partitioned deep learning algorithm, we compare the performance with other techniques by its classification accuracy, specificity, and sensitivity. For the accurate medical image classification objective, we demonstrate a new structure of partitioned deep Convolution Neural Network (CNN) with two partitioned layers for labeling and classify by labeling region of interest in multidimensional hyperspectral image.

Results

The performance of the partitioned deep CNN was verified by classification accuracy. We have obtained classification accuracy of 91.4% with sensitivity 0.94 and a specificity of 0.91 for 100 image data sets training for task classification of cancerous tumor with benign and for task classification of cancerous tumor with normal tissue accuracy of 94.5% for 500 training patterns was obtained.

Conclusions

We compared the obtained results from another traditional medical image classification algorithm. From the obtained result, we identify that the quality of diagnosis is increased by proposed regression-based partitioned CNN learning algorithm for a complex medical image of oral cancer diagnosis.

Keywords

Deep learning algorithm Medical image classification Hyperspectral image data Image labeling Oral cancer diagnosis 

Notes

Acknowledgements

The authors would like to thank the Department of Electrical Engineering, Indian Institute of Technology, Delhi and the Management, Principal of Mepco Schlenk Engineering College, Sivakasi for providing us the state-of-art facilities to carry out this research work.

Compliance with ethical standards

Conflict of interest

The authors confirm that they have no conflict of interest regarding this research article.

References

  1. Baljit Singh K, Singh AP, P (2016) Classification of clustered microcalcifications using MLFFBP-ANN and SVM. Egyptian Infor J 17(1):11–20CrossRefGoogle Scholar
  2. Bradley J, Erickson MD, Panagiotis K et al (2018) Deep learning in radiology: does one size fit all? J Am Coll Rad 15(3):521–526CrossRefGoogle Scholar
  3. Christodoulidis S, Anthimopoulos M, Ebner L et al (2017) Multisource transfer learning with convolutional neural networks for lung pattern analysis. IEEE J Biomed Health Inf 21(1):76–84CrossRefGoogle Scholar
  4. Chudgar AV, Conant EF, Weinstein SP (2017) Assessment of disease extent on contrast-enhanced MRI in breast cancer detected at digital breast tomosynthesis versus digital mammography alone. Clin Radiol 72(7):573–579CrossRefGoogle Scholar
  5. Deepak Kumar J, Surendra Bilouhan D, Kumar RC (2018) An approach for hyperspectral image classification by optimizing SVM using self-organizing map. J Comput Sci 25(1):252–259Google Scholar
  6. Dey D, Chatterjee B, Dalai S, Munshi S, Chakravorti S (2017) A deep learning framework using convolution neural network for classification of impulse fault patterns in transformers with increased accuracy. IEEE Trans Dielectr Electr Insul 24(6):3894–3897CrossRefGoogle Scholar
  7. Dou Q, Chen H, Yu L et al (2016) Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks. IEEE Trans Med Imaging 35(5):1182–1195.  https://doi.org/10.1109/TMI.2016.2528129 CrossRefGoogle Scholar
  8. Ge H, Wang L, Liu Y, Li C (2018) Hyperspectral image classification based on adaptive-weighted LLE and clustering-based FSVMs. IET Image Proc 12(6):941–947CrossRefGoogle Scholar
  9. Gregory P, Way VF, Sanchez K La (2018) Machine learning detects pan-cancer ras pathway activation in the cancer genome Atlas. Cell Reports 23(1):172–180CrossRefGoogle Scholar
  10. He H, Ma Y (2012) Imbalanced learning: foundations, algorithms and applications, 1st edn. Wiley, New YorkGoogle Scholar
  11. Heba M, El-Dahshan ESA, El-Horbaty ESM, Abdel-Badeeh M (2018) Classification using deep learning neural networks for brain tumors. Future Comput Inf J 3(1):68–71CrossRefGoogle Scholar
  12. Hijazi H, Chan C (2012) A classification framework applied to cancer gene expression profiles. J Healthc Eng 4(4):255–284Google Scholar
  13. Hinton G, Deng L, Yu D et al (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29(6):82–97CrossRefGoogle Scholar
  14. Huang W, Xiao L, Wei Z et al (2015) A new pan-sharpening method with deep neural networks. IEEE Geosci Remote Sens Lett 12(5):1037–1041CrossRefGoogle Scholar
  15. Jie Z, Shufang W, Xizhao W, Guoqing Y, Liyan M (2018) Multi-image matching for object recognition. IET Compt Vis 12(3):350–356CrossRefGoogle Scholar
  16. Jin KH, McCann MT, Froustey E et al (2017) Deep convolutional neural network for inverse problems in imaging. IEEE Trans Image Process 26(9):4509–4522CrossRefGoogle Scholar
  17. Kalantari N, Ramamoorthi R (2017) Deep high dynamic range imaging of dynamic scenes. ACM Trans Graph 36(4):1–12CrossRefGoogle Scholar
  18. Kiranyaz S, Ince T, Gabbouj M (2016) Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans Biomed Eng 63(3):664–675CrossRefGoogle Scholar
  19. Kourou K, Exarchos TP, Exarchos KP et al (2015) Machine learning applications in cancer prognosis and prediction. Comput Struct Biotech J 13(1):8–17CrossRefGoogle Scholar
  20. Lustberg T, van Soest J, Mark G et al (2018) Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer. Radio Onco 126:312–317CrossRefGoogle Scholar
  21. Mathews SM, Kambhamettu C, Barner KE (2018) A novel application of deep learning for single-lead ECG classification. Comput Biol Med 99(1):53–62CrossRefGoogle Scholar
  22. Murray G, Rourke CO, Hogan J, Fenton FE (2016) Detecting internet search activity for mouth cancer in Ireland. British J of Oral Maxillofacial Surg 54(2):163–165CrossRefGoogle Scholar
  23. Ordonez FJ, Roggen D (2016) Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1):1–25CrossRefGoogle Scholar
  24. Palsson F, Sveinsson J, Ulfarsson M (2017) Multispectral and hyperspectral image fusion using a 3-d-convolutional neural network. IEEE Geosci Remote Sens Lett 14(5):639–643CrossRefGoogle Scholar
  25. Philippe M, Vincent N, Christophe M, Alex L (2018) Survey on deep learning for radiotherapy. Compt Biol Med 98:126–146CrossRefGoogle Scholar
  26. Prochzazka A, Vaseghi S, Charvatova H et al (2017) Cycling segments multimodal analysis and classification using neural networks. Appl Sci 7(6):581–591CrossRefGoogle Scholar
  27. Wang C, Gong L, Yu Q et al (2017) DLAU: a scalable deep learning accelerator unit on FPGA. IEEE Trans Comput Aided Des Integr Circuits Syst 36(3):513–517Google Scholar
  28. Yuan Y, Lin J, Wang Q (2015) Hyperspectral image classification via multitask joint sparse representation and stepwise MRF optimization. IEEE Trans Cybern 46(12):2966–2977CrossRefGoogle Scholar
  29. Zhihuai X, Zhenhua G, Chengshan Q (2018) Palmprint gender classification by convolutional neural network. IET Compt Vis 12(4):476–483CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical and Electronics EngineeringMepco Schlenk Engineering College (Autonomous)SivakasiIndia

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