International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Sugondo Hadiyoso, Suci Aulia, Indrarini Dyah Irawati

School of Applied Science, Telkom University, Bandung, West Java, 40257, Indonesia


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Cancer is a non-contagious disease that is the leading cause of death globally. The most common types of cancer with high mortality are lung and colon cancer. One of the efforts to reduce cases of death is early diagnosis followed by medical therapy. Tissue sampling and clinical pathological examination are the gold standard in cancer diagnosis. However, in some cases, pathological examination of tissue to the cell level requires high accuracy, depending on the contrast of the pathological image, and the experience of the clinician. Therefore, we need an image processing approach combined with artificial intelligence for automatic classification. In this study, a method is proposed for automatic classification of lung and colon cancer based on a deep learning approach. The object of the image that is classified is the histopathological image of normal tissue, benign cancer, and malignant cancer. Convolutional neural network (CNN) with VGG16 architecture and Contrast Limited Adaptive Histogram Equalization (CLAHE) were employed for demonstration of classification on 25000 histopathological images. The simulation results show that the proposed method is able to classify with a maximum accuracy of 98.96%. The system performance using CLAHE shows a higher detection accuracy than without using CLAHE and is consistent for all epoch scenarios. The comparative study shows that the proposed method outperforms some previous studies. With this proposed method, it is hoped that it can help clinicians in diagnosing cancer automatically, with low cost, high accuracy, and fast processing on large datasets.

Keywords: Cancer, Classification, Deep learning, Histopathological, CNN.

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  1. Abbas, M.A., Bukhari, S.U.K., Syed, A., Shah, S.S. 2020. The histopathological diagnosis of adenocarcinoma squamous cells carcinoma of lungs by artificial intelligence: A comparative study of convolutional neural networks, MedRxiv, 1–13.

  2. Ali, M., Ali, R. 2021. Multi-input dual-stream capsule network for improved lung and colon cancer classification. Diagnostics, 11, 1–18.

  3. Ayad, H., Ghindawi, I.W., Kadhm, M.S. 2020. Lung segmentation using proposed deep learning architecture. International Journal of Online and Biomedical Engineering, 16, 141–147.

  4. Borkowski, A.A., Bui, M.M., Thomas, L.B., Wilson, C.P., DeLand, L.A., Mastorides, S.M. 2019. Lung and colon cancer histopathological image dataset (LC25000). ArXiv:1912.12142v1, 1–2.

  5. Chang, Y., Abu-Amara, F. 2021. An efficient hybrid classifier for cancer detection. International Journal of Online and Biomedical Engineering, 17, 76–97.

  6. Elnakib, A., Amer, H.M., Abou-Chadi, F.E.Z. 2020. Early lung cancer detection using deep learning optimization. International Journal of Online and Biomedical Engineering, 16, 82–94.

  7. Hatuwal, B.K., Thapa, H.C. 2020. Lung cancer detection using convolutional neural network on histopathological images. International Journal of Computer Trends and Technology, 68, 21–24.

  8. Irawati, I.D., Hadiyoso, S., Fahmi, A. 2021. Compressive sensing in lung cancer images for telemedicine application. ACM International Conference Proceeding Series, 55–61.

  9. Kalaivani, N., Manimaran, N., Sophia, S., D. Devi, D. 2020. Deep learning based lung cancer detection and classification. IOP Conference Series: Materials Science and Engineering, 994, 1–5.

  10. Khalid Bukhari, S.U., Syed, A., Arsalan Bokhari, S.K., Hussain, S.S., Armaghan, S.U., Hussain Shah, S.S. 2020. The histological diagnosis of colonic adenocarcinoma by applying partial self supervised learning. MedRxiv, 2020, 1–11.

  11. Li, Y.T., Guo, J.I. 2018. A VGG-16 based Faster RCNN Model for PCB error inspection in industrial AOI applications. IEEE International Conference on Consumer Electronics-Taiwan, 1–2.

  12. Ma, J., Fan, X., Yang, S.X., Zhang, X., Zhu, X. 2018. Contrast limited adaptive histogram equalization-based fusion in YIQ and HSI color spaces for underwater image enhancement. International Journal of Pattern Recognition and Artificial Intelligence, 32, 1–26.

  13. Maheshan, M.S., Harish, B.S., Nagadarshan, N. 2018. On the use of image enhancement technique towards robust sclera segmentation. Procedia Computer Science, 143, 466–473.

  14. Mamdouh, R., El-Khamisy, N., Amer, K., Riad, A., El-Bakry, H.M. 2021. A new model for image segmentation based on deep learning. International Journal of Online and Biomedical Engineering, 17, 28–47.

  15. Mangal, S., Chaurasia, A., Khajanchi, A. 2020. Convolution neural networks for diagnosing colon and lung cancer histopathological images. Computer Science, Engineering ArXiv, 2020, 1–10.

  16. Masud, M., Sikder, N., Nahid, A. Al, Bairagi, A.K., Alzain, M.A. 2021. A machine learning approach to diagnosing lung and colon cancer using a deep learning‐based classification framework. Sensors (Switzerland), 21, 1–21.

  17. Buddha, H.K., Meka, J.S., Choppala, P. 2020. OCR image enhancement & implementation by using CLAHE algorithm. Mukt Shabd Journal, 9, 3595–3599.

  18. Nishio, M., Nishio, M., Jimbo, N., Nakane, K. 2021. Homology-based image processing for automatic classification of histopathological images of lung tissue. Cancers, 13, 1–12.

  19. Pizer, S.M., Johnston, R.E., Ericksen, J.P., Yankaskas, B.C., Muller, K.E. 1990. Contrast-limited adaptive histogram equalization: Speed and effectiveness. Proceedings of the First Conference on Visualization in Biomedical Computing, 1990, 337–345.

  20. Saif, A., Qasim, Y.R.H., Al-Sameai, H.A.M.H., Farhan Ali, O.A., Hassan, A.A.M. 2020. Multi paths technique on convolutional neural network for lung cancer detection based on histopathological images. International Journal of Advanced Networking and Applications, 12, 4549–4554.

  21. Saleh, A.Y., Chin, C.K., Penshie, V., Al-Absi, H.R.H. 2021. Lung cancer medical images classification using hybrid cnn-svm. International Journal of Advances in Intelligent Informatics, 7, 151–162.

  22. Sepasian, M., Balachandran, W., Mares, C. 2008. Image Enhancement for fingerprint minutiae-based algorithms using CLAHE, Standard deviation analysis and sliding neighborhood. Lecture Notes in Engineering and Computer Science, 2173, 1199–1203.

  23. Sharma, P., Bora, K., Kasugai, K., Balabantaray, B.K. 2020. Two stage classification with CNN for colorectal cancer detection. Oncologie, 22, 129–145.

  24. Siegel, R.L., Miller, K.D., Jemal, A. 2017. Cancer statistics, 2017. CA Cancer Journal for Clinicians, 67, 7–30.

  25. Stimper, V., Bauer, S., Ernstorfer, R., Schölkopf, B., Xian, R.P. 2019. Multidimensional contrast limited adaptive histogram equalization. IEEE Access, 7, 165437–165447.


Received: 2022-02-07
Revised: 2022-12-05
Accepted: 2022-12-16
Available Online: 2023-02-07

Cite this article:

Hadiyoso, S., Aulia, S., Irawati, I.D. Diagnosis of lung and colon cancer based on clinical pathology images using convolutional neural network and CLAHE framework. International Journal of Applied Science and Engineering, 20, 2022004.

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