Article Sidebar
Main Article Content
Cancer is a deadly disease that has gained a reputation as a global health concern. Further, lung cancer has been widely reported as the most deadly cancer type globally, while colon cancer comes second. Meanwhile, early detection is one of the primary ways to prevent lung and colon cancer fatalities. To aid the early detection of lung and colon cancer, we propose a computer-aided diagnostic approach that employs a Deep Learning (DL) architecture to enhance the detection of these cancer types from Computed Tomography (CT) images of suspected body parts. Our experimental dataset (LC25000) contains 25000 CT images of benign and malignant lung and colon cancer tissues. We used weights from a pre-trained DL architecture for computer vision, EfficientNet, to build and train a lung and colon cancer detection model. EfficientNet is a Convolutional Neural Network architecture that scales all input dimensions such as depth, width, and resolution at the same time. Our research findings showed detection accuracies of 99.63%, 99.50%, and 99.72% for training, validation, and test sets, respectively.
Article Details
K. Adu, Y. Yu, J. Cai, K. Owusu-Agyemang, B. A. Twumasi, and X. Wang. DHS-CapsNet: Dual horizontal squash capsule networks for lung and colon cancer classification from whole slide histopathological images. International Journal of Imaging Systems and Technology, 31(4):2075-2092, 2021. https://doi.org/10.1002/ima.22569. (Crossref)
O. Attallah, M. F. Aslan, and K. Sabanci. A framework for lung and colon cancer diagnosis via lightweight deep learning models and transformation methods. Diagnostics, 12(12), 2022. https://doi.org/10.3390/diagnostics12122926. (Crossref)
A. A. Borkowski, M. M. Bui, L. B. Thomas, C. P. Wilson, L. A. DeLand, and S. M. Mastorides. LC25000 Lung and colon histopathological image dataset. 2019. https://github.com/tampapath/lung_colon_image_set.
CDC. An update on cancer deaths in the united states. In Center for Disease Control (CDC). 2022. https://www.cdc.gov/cancer/dcpc/research/update-on-cancer-deaths/.
H. Chen, X. Xu, T. Ge, C. Hua, X. Zhu, Q. Wang, Z. Yu, and R. Zhang. A novel tool for the risk assessment and personalized chemo-immunotherapy response prediction of adenocarcinoma and squamous cell carcinoma lung cancer. International Journal of General Medicine, 14:5771-5785, 2021. https://doi.org/10.2147/IJGM.S327641. (Crossref)
M. Chen, S. Huang, Z. Huang, and Z. Zhang. Detection of lung cancer from pathological images using CNN model. In Proc. 2021 IEEE Int. Conf. Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), pages 352-358, Fuzhou, China, 24-26 Sep 2021. https://doi.org/10.1109/CEI52496.2021.9574590. (Crossref)
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. ImageNet: A large-scale hierarchical image database. In Proc. 2009 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pages 248-255, Miami, FL, USA, 20-25 Jun 2009. https://doi.org/10.1109/CVPRW.2009.5206848. (Crossref)
S. Garg and S. Garg. Prediction of lung and colon cancer through analysis of histopathological images by utilizing pre-trained CNN models with visualization of class activation and saliency maps. In Proc. 3rd Artificial Intelligence and Cloud Computing Conf. (AICCC) 2020, ACM International Conference Proceeding Series, pages 38-45. Association for Computing Machinery, Kyoto, Japan, Dec 18-20 2020. https://doi.org/10.1145/3442536.3442543. (Crossref)
Md I. Hasan, Md S. Ali, Md H. Rahman, and Md K. Islam. Automated detection and characterization of colon cancer with deep convolutional neural networks. Journal of Healthcare Engineering, 2022:5269913, 2022. https://doi.org/10.1155/2022/5269913. (Crossref)
B. K. Hatuwal and H. C. Thapa. Lung cancer detection using convolutional neural network on histopathological images. International Journal of Computer Trends and Technology, 68(10):21-24, 2020. https://doi.org/10.14445/22312803/IJCTT-V68I10P104. (Crossref)
M. Masud, N. Sikder, A. A. Nahid, A. K. Bairagi, and M. A. Alzain. A machine learning approach to diagnosing lung and colon cancer using a deep learning‐based classification framework. Sensors, 21(3):748, 2021. https://doi.org/10.3390/s21030748. (Crossref)
F. Maulidina, Z. Rustam, and J. Pandelaki. Lung Cancer Classification using Support Vector Machine and Hybrid Particle Swarm Optimization-Genetic Algorithm. In Proc. 2021 Int. Conf. Decision Aid Sciences and Application (DASA), pages 751-755, Sakheer, Bahrain, 07-08 Dec 2021. https://doi.org/10.1109/DASA53625.2021.9682259. (Crossref)
S. Mehmood, T. M. Ghazal, M. A. Khan, M. Zubair, M. T. Naseem, T. Faiz, and M. Ahmad. Malignancy detection in lung and colon histopathology images using transfer learning with class selective image processing. IEEE Access, 10:25657-25668, 2022. https://doi.org/10.1109/ACCESS.2022.3150924. (Crossref)
K. Pradhan and P. Chawla. Medical I nternet of things using machine learning algorithms for lung cancer detection. Journal of Management Analytics, 7(4):591-623, 2020. https://doi.org/10.1080/23270012.2020.1811789. (Crossref)
Y. Qasim, H. Al-Sameai, O. Ali, and A. Hassan. Convolutional neural networks for automatic detection of colon adenocarcinoma based on histopathological images. In F. Saeed, F. Mohammed, and A. Al-Nahari, editors, Innovative Systems for Intelligent Health Informatics: Proc. Int. Conf. Reliable Information and Communication Technology (IRICT), volume 72 of Lecture Notes on Data Engineering and Communications Technologies, pages 19-28. Springer, Cham, 22-23 Dec 2021. https://doi.org/10.1007/978-3-030-70713-2_3. (Crossref)
D. Sarwinda, A. Bustamam, R. H. Paradisa, T. Argyadiva, and W. Mangunwardoyo. Analysis of deep feature extraction for colorectal cancer detection. In Proc. 2020 4th Int. Conf. Informatics and Computational Sciences (ICICoS), pages 1-5, Semarang, Indonesia, 10-11 Nov 2020. https://doi.org/10.1109/ICICoS51170.2020.9298990. (Crossref)
S. Shandilya and S. R. Nayak. Analysis of lung cancer by using deep neural network. In M. Mishra, R. Sharma, Rathore A. K., J. Nayak, and B. Naik, editors, Proc. 2nd Conf. Innovation in Electrical Power Engineering, Communication, and Computing Technology (IEPCCT 2021), volume 814 of Lecture Notes in Electrical Engineering, pages 427-436, Bhubaneswar, India, 24–26 Sep 2022. Springer, Singapore. https://doi.org/10.1007/978-981-16-7076-3_37. (Crossref)
Md A. Talukder, Md M. Islam, Md A. Uddin, A. Akhter, K. F. Hasan, and M. A. Moni. Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning. Expert Systems with Applications, 205:117695, 2022. https://doi.org/10.1016/j.eswa.2022.117695. (Crossref)
Z. Tan, Y. Yang, J. Wan, G. Guo, and S. Z. Li. Deeply-learned hybrid representations for facial age estimation. In Proc. 28th Int. Joint Conf. Artificial Intelligence (IJCAI), pages 3548-3554, Macao, China, 10-16 Aug 2019. https://doi.org/10.24963/ijcai.2019/492. (Crossref)
Z. Tasnim, S. Chakraborty, F. M. J. M. Shamrat, A. N. Chowdhury, H. A. Nuha, A. Karim, S. B. Zahir, and Md M. Billah. Deep learning predictive model for colon cancer patient using CNN-based classification. International Journal of Advanced Computer Science and Applications, 12(8):687-696, 2021. https://doi.org/10.14569/IJACSA.2021.0120880. (Crossref)
M. Toğaçar. Disease type detection in lung and colon cancer images using the complement approach of inefficient sets. Computers in Biology and Medicine, 137:104827, 2021. https://doi.org/10.1016/j.compbiomed.2021.104827. (Crossref)
WHO. Cancer: Key facts, 7 Feb 2022. https://www.who.int/news-room/fact-sheets/detail/cancer.
M. Yildirim and A. Cinar. Classification with respect to colon adenocarcinoma and colon benign tissue of colon histopathological images with a new CNN model: MA_ColonNET. International Journal of Imaging Systems and Technology, 32(1):155-162, 2022. https://doi.org/10.1002/ima.22623. (Crossref)