Exploration of Various Deep Learning Models for Increased Accuracy in Automatic Polyp Detection

  • Ariel E. Isidro
  • Arnel C. Fajardo
  • Alexander A. Hernandez
  • Technological Institute of the Philippines
Published in

  • This paper is created to explore deep learning models and algorithms that results in highest accuracy in detecting polyp on colonoscopy images. Previous studies implemented deep learning using convolution neural network (CNN) algorithm in detecting polyp and non- polyp. Other studies used dropout, and data augmentation algorithm but mostly not checking the overfitting, thus, include more than four-layer models. Rulei Yu et.al from the Institute of Software, Chinese Academy of Sciences said that transfer learning is better talking about performance or improving the previous used algorithm. Most especially in applying the transfer learning in feature extraction. Series of experiments were conducted with only a minimum of 4 CNN layers applying previous used models and identified the model that produce the highest percentage accuracy of 98% among the other models that apply transfer learning. Further studies could use different optimizer to a different CNN models to increase accuracy.

  • Colon cancer, Overfitting, Dropout, Deep Learning, Transfer Learning

Cite As
  • APA 7th Edition:
    Isidro, A., Fajardo, A., & Hernandez, A. (2020). Exploration of Various Deep Learning Models for Increased Accuracy in Automatic Polyp Detection. Innovatus, 3(1), 37-49.
  • Harvard:
    Isidro, A., Fajardo, A. and Hernandez, A., 2020. Exploration of Various Deep Learning Models for Increased Accuracy in Automatic Polyp Detection. Innovatus, 3(1), pp.37-49.
  • IEEE:
    [1] A. Isidro, A. Fajardo and A. Hernandez, "Exploration of Various Deep Learning Models for Increased Accuracy in Automatic Polyp Detection", Innovatus, vol. 3, no. 1, pp. 37-49, 2020.

  • B. Taha, J. Dias, and N. Werghi, “Convolutional neural networkasa feature extractor for automatic polyp detection,” Proc. - Int. Conf. Image Process. ICIP, vol. 2017-Septe, no. 1, pp. 2060–2064, 2018.
  • M. Ismail, A. A. Farag, R. Falk, and G. W. Dryden, “ENHANCED AUTOMATIC COLON SEGMENTATION FOR BETTER CANCER DIAGNOSIS University of Louisville , Louisville , USA Medical Imaging , Jewish Hospital , Abraham Flexner Way , Louisville , USA Division of Gastroenterology / Hepatology , University of Louisville ,” pp. 91–94, 2014.
  • A. Goodfellow, Ian; Bengio, Yoshua; Courville, “Deep Learning,” 人工知能学会誌, vol. 28, no. 5, 2013.
  • D. G. Vinsard et al., “Quality assurance of computer-aided detection and diagnosis in colonoscopy,” Gastrointest. Endosc., vol. 90, no. 1, pp. 55–63, 2019.
  • A. Shustanov and P. Yakimov, “CNN Design for Real-Time Traffic Sign Recognition,” Procedia Eng., vol. 201, pp. 718–725, 2017.
  • A. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Comput. Electron. Agric., vol. 147, no. February, pp. 70–90, 2018.
  • D. E. Diamantis, D. K. Iakovidis, and A. Koulaouzidis, “Look-behind fully convolutional neural network for computer-aided endoscopy,” Biomed. Signal Process. Control, vol. 49, pp. 192–201, 2019.
  • T. Liu et al., “Recent advances in convolutional neural networks,” Pattern Recognit., vol. 77, pp. 354–377, 2017.
  • M. Oquab, L. Bottou, I. Laptev, and J. Sivic, “Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 1717–1724.
  • X. Zhang et al., “Real-time gastric polyp detection using convolutional neural networks,” pp. 1–16, 2019.
  • A. A. Hernandez, “Classification of Fish Species with Augemented Data using Deep Convolutional Neural Network,” pp. 2–7, 2016.
  • N. Reddy, A. Rattani, and R. Derakhshani, “Comparison of Deep Learning Models for Biometric-based Mobile User Authentication.”
  • H. Wu and X. Gu, “Towards dropout training for convolutional neural networks,” Neural Networks, vol. 71, pp. 1–10, 2015.
  • H. Salehinejad and S. Valaee, “Ising-Dropout: A Regularization Method for Training and Compression of Deep Neural Networks,” pp. 1–5, 2019.
  • N. Tajbakhsh, S. R. Gurudu, and J. Liang, “Automated polyp detection in colonoscopy videos using shape and context information,” IEEE Trans. Med. Imaging, vol. 35, no. 2, pp. 630–644, 2016.
  • G. Urban et al., “Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy,” Gastroenterology, vol. 155, no. 4, pp. 1069-1078.e8, 2018.
  • O. Bardhi, D. Sierra-Sosa, B. Garcia-Zapirain, and A. Elmaghraby, “Automatic colon polyp detection using Convolutional encoder-decoder model,” 2017 IEEE Int. Symp. Signal Process. Inf. Technol. ISSPIT 2017, pp. 445–448, 2018.
  • R. Zhang, Y. Zheng, C. C. Y. Poon, D. Shen, and J. Y. W. Lau, “Polyp detection during colonoscopy using a regression-based convolutional neural network with a tracker,” Pattern Recognit., vol. 83, pp. 209–219, 2018.
  • T. Aoki et al., “Automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network,” Gastrointest. Endosc., vol. 89, no. 2, pp. 357-363.e2, 2019.
  • T. Doel et al., “NiftyNet: a deep-learning platform for medical imaging,” Comput. Methods Programs Biomed., vol. 158, pp. 113–122, 2018.
  • L. Yuan, Z. Qu, Y. Zhao, H. Zhang, and Q. Nian, “A convolutional neural network based on TensorFlow for face recognition,” Proc. 2017 IEEE 2nd Adv. Inf. Technol. Electron. Autom. Control Conf. IAEAC 2017, pp. 525–529, 2017.
  • C. T. Sari and C. Gunduz-Demir, “Unsupervised Feature Extraction via Deep Learning for Histopathological Classification of Colon Tissue Images,” IEEE Trans. Med. Imaging, vol. PP, no. c, p. 1, 2018.
  • D. Graupe, Deep Learning Neural Networks, Design and Case Studies. World Scientific Publishing Co. Pte. Ltd., 2016.
  • J. Bernal, F. J. Sánchez, G. Fernández-Esparrach, D. Gil, C. Rodríguez, and F. Vilariño, “WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians,” Comput. Med. Imaging Graph., vol. 43, pp. 99–111, 2015.
  • S. Dipanjan, R. Bali, and T. Ghosh, Hands-On Transfer Learning with Python. Packt Publishing, 2018.
  • A. Isidro and A. Fajardo, “Optimized Image Extracting Algorithm from Original Image using Ground Truth,” 2019 Int. Conf. ICT Smart Soc., 2019.
  • S. Zhang et al., “Computerized Medical Imaging and Graphics An investigation of CNN models for differentiating malignant from benign lesions using small pathologically proven datasets,” Comput. Med. Imaging Graph., vol. 77, p. 101645, 2019.
  • Y. Cong, S. Wang, J. Liu, J. Cao, Y. Yang, and J. Luo, “Deep sparse feature selection for computer aided endoscopy diagnosis,” Pattern Recognit., vol. 48, no. 3, pp. 907–917, 2015.
  • D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” pp. 1–15, 2014.
  • A. Sai Bharadwaj Reddy and D. Sujitha Juliet, Transfer Learning with ResNet-50 for Malaria Cell Image Classification.
  • Q. Li et al., “Colorectal polyp segmentation using a fully convolutional neural network,” Proc. - 2017 10th Int. Congr. Image Signal Process. Biomed. Eng. Informatics, CISP-BMEI 2017, vol. 2018-Janua, pp. 1–5, 2018.
  • E. Ribeiro, A. Uhl, G. Wimmer, and M. Häfner, “Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification,” vol. 2016, no.d, 2016.

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