Muhammad Ijaz
Abstract
Lung and Colorectal (LC) cancer is life-threatening and rapidly developing cancers. According to World Health Organization (WHO), approximately 4.14 million lung and colorectal cancer cases were newly diagnosed, with 2.7 million fatalities. An International Agency for Research on Cancer (IARC) reported that there will be more than 3 million additional instances of colorectal cancer worldwide between 2020 and 2040. Early diagnosis of LC cancer is very helpful for treatment and can save the precious human life. The conventional diagnosis methods are expensive and time consuming. In this work, we present an accurate and efficient model for the classification of Lung and Colorectal (LC) cancer. We utilize two well-known pre-trained deep learning models, ResNet50 and EfficientNetB0, and fine-tuned the both models based on the addition and removal of layers. After the fine-tuning, manual hit and trail based hyperparameters are initialized. Later on, the deep transfer learning was performed and obtained the trained models. Two different feature vectors have been extracted from both models and fused using a priority based serial approach. To further improve the performance of extracted features, Normal Distribution based Gray Wolf Optimization algorithm is employed and obtained the best features that given as input to five classifiers. The output of these five classifiers is then utilized by soft voting technique to generate the final prediction. Experimental results show that the proposed architecture achieved an overall 98.73% accuracy on LC25000 dataset. Furthermore, prediction time was reduced by 19.14%. Comparison with the state-of-the-art techniques shows that the proposed technique obtained the improved performance results.
- N. C. Institute. (November, 11,2022). What is Cancer? Available: https://www.cancer.gov/about-cancer/understanding/what-is-cancerGoogle Scholar
- E. Morgan, M. Arnold, A. Gini, V. Lorenzoni, C. J. Cabasag, M. Laversanne, et al., "Global burden of colorectal cancer in 2020 and 2040: incidence and mortality estimates from GLOBOCAN," Gut, pp. gutjnl-2022-327736, 2022.Google Scholar
- W. H. Orgnization. (2022, November, 11 2022). Cancer. Available: https://www.who.int/health-topics/cancer#tab=tab_1Google Scholar
- M. Clinic. (November, 11 2022). Symptoms of Cancer. Available: https://www.mayoclinic.org/diseases-conditions/cancer/symptoms-causes/syc-20370588Google Scholar
- J. Corner, J. Hopkinson, D. Fitzsimmons, S. Barclay, and M. Muers, "Is late diagnosis of lung cancer inevitable? Interview study of patients’ recollections of symptoms before diagnosis," Thorax, vol. 60, pp. 314-319, 2005.Google ScholarCross Ref
- S. Das, S. Biswas, A. Paul, and A. Dey, "AI Doctor: An intelligent approach for medical diagnosis," in Industry interactive innovations in science, engineering and technology, ed: Springer, 2018, pp. 173-183.Google Scholar
- A. Chiche and B. Yitagesu, "Part of speech tagging: a systematic review of deep learning and machine learning approaches," Journal of Big Data, vol. 9, pp. 1-25, 2022.Google ScholarCross Ref
- E. Elyan, P. Vuttipittayamongkol, P. Johnston, K. Martin, K. McPherson, C. Jayne, et al., "Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward," Artificial Intelligence Surgery, vol. 2, 2022.Google Scholar
- U. Zahid, I. Ashraf, M. A. Khan, M. Alhaisoni, K. M. Yahya, H. S. Hussein, et al., "BrainNet: optimal deep learning feature fusion for brain tumor classification," Computational Intelligence and Neuroscience, vol. 2022, 2022.Google Scholar
- E. Bayoumi, A. A. Khalaf, and R. R. Gharieb, "Brain Tumor Automatic Detection from MRI Images Using Transfer Learning Model with Deep Convolutional Neural Network," Journal of Advanced Engineering Trends, vol. 41, pp. 19-30, 2021.Google ScholarCross Ref
- K. Sirinukunwattana, S. E. A. Raza, Y.-W. Tsang, D. R. Snead, I. A. Cree, and N. M. Rajpoot, "Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images," IEEE transactions on medical imaging, vol. 35, pp. 1196-1206, 2016.Google Scholar
- R. Selvanambi, J. Natarajan, M. Karuppiah, S. Islam, M. M. Hassan, and G. Fortino, "Lung cancer prediction using higher-order recurrent neural network based on glowworm swarm optimization," Neural Computing and Applications, vol. 32, pp. 4373-4386, 2020.Google ScholarDigital Library
- A. O. de Carvalho Filho, A. C. Silva, A. C. de Paiva, R. A. Nunes, and M. Gattass, "Classification of patterns of benignity and malignancy based on CT using topology-based phylogenetic diversity index and convolutional neural network," Pattern Recognition, vol. 81, pp. 200-212, 2018.Google ScholarDigital Library
- Z. Yuan, M. IzadyYazdanabadi, D. Mokkapati, R. Panvalkar, J. Y. Shin, N. Tajbakhsh, et al., "Automatic polyp detection in colonoscopy videos," in Medical Imaging2017: Image Processing, 2017, pp. 718-727.Google Scholar
- A. Masood, B. Sheng, P. Li, X. Hou, X. Wei, J. Qin, et al., "Computer-assisted decision support system in pulmonary cancer detection and stage classification on CT images," Journal of biomedical informatics, vol. 79, pp. 117-128, 2018.Google ScholarDigital Library
- X. Mo, K. Tao, Q. Wang, and G. Wang, "An efficient approach for polyps detection in endoscopic videos based on faster R-CNN," in 2018 24th international conference on pattern recognition (ICPR), 2018, pp. 3929-3934.Google Scholar
- G. Urban, P. Tripathi, T. Alkayali, M. Mittal, F. Jalali, W. Karnes, et al., "Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy," Gastroenterology, vol. 155, pp. 1069-1078. e8, 2018.Google ScholarCross Ref
- M. Akbari, M. Mohrekesh, S. Rafiei, S. R. Soroushmehr, N. Karimi, S. Samavi, et al., "Classification of informative frames in colonoscopy videos using convolutional neural networks with binarized weights," in 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC), 2018, pp. 65-68.Google Scholar
- S. Suresh and S. Mohan, "ROI-based feature learning for efficient true positive prediction using convolutional neural network for lung cancer diagnosis," Neural Computing and Applications, vol. 32, pp. 15989-16009, 2020.Google ScholarDigital Library
- M. Masud, G. Muhammad, M. S. Hossain, H. Alhumyani, S. S. Alshamrani, O. Cheikhrouhou, et al., "Light deep model for pulmonary nodule detection from CT scan images for mobile devices," Wireless Communications and Mobile Computing, vol. 2020, 2020.Google Scholar
- P. M. Shakeel, M. Burhanuddin, and M. I. Desa, "Automatic lung cancer detection from CT image using improved deep neural network and ensemble classifier," Neural Computing and Applications, pp. 1-14, 2020.Google Scholar
- S. U. K. Bukhari, A. Syed, S. K. A. Bokhari, S. S. Hussain, S. U. Armaghan, and S. S. H. Shah, "The histological diagnosis of colonic adenocarcinoma by applying partial self supervised learning," MedRxiv, 2020.Google Scholar
- S. Mangal, A. Chaurasia, and A. Khajanchi, "Convolution neural networks for diagnosing colon and lung cancer histopathological images," arXiv preprint arXiv:2009.03878, 2020.Google Scholar
- B. K. Hatuwal and H. C. Thapa, "Lung cancer detection using convolutional neural network on histopathological images," Int. J. Comput. Trends Technol, vol. 68, pp. 21-24, 2020.Google ScholarCross Ref
- S. Mehmood, T. M. Ghazal, M. A. Khan, M. Zubair, M. T. Naseem, T. Faiz, et al., "Malignancy Detection in Lung and Colon Histopathology Images Using Transfer Learning with Class Selective Image Processing," IEEE Access, vol. 10, pp. 25657-25668, 2022.Google ScholarCross Ref
- W. Sun, B. Zheng, and W. Qian, "Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis," Computers in biology and medicine, vol. 89, pp. 530-539, 2017.Google Scholar
- P. M. Shakeel, A. Tolba, Z. Al-Makhadmeh, and M. M. Jaber, "Automatic detection of lung cancer from biomedical data set using discrete AdaBoost optimized ensemble learning generalized neural networks," Neural Computing and Applications, vol. 32, pp. 777-790, 2020.Google ScholarDigital Library
- M. Tan and Q. Le, "Efficientnet: Rethinking model scaling for convolutional neural networks," in International conference on machine learning, 2019, pp. 6105-6114.Google Scholar
- M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, et al., "TensorFlow: large-scale machine learning on heterogeneous distributed systems (2016)," arXiv preprint arXiv:1603.04467, vol. 52, 2015.Google Scholar
- K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.Google Scholar
- S. Mirjalili, S. Mirjalili, and A. Lewis, "Grey Wolf Optimizer Adv Eng Softw 69: 46–61," ed: ed, 2014.Google Scholar
- B. Xue, M. Zhang, W. N. Browne, and X. Yao, "A survey on evolutionary computation approaches to feature selection," IEEE Transactions on Evolutionary Computation, vol. 20, pp. 606-626, 2015.Google ScholarDigital Library
- T. K. Ho, "Random decision forests," in Proceedings of 3rd international conference on document analysis and recognition, 1995, pp. 278-282.Google Scholar
- T. Hastie, R. Tibshirani, J. H. Friedman, and J. H. Friedman, The elements of statistical learning: data mining, inference, and prediction vol. 2: Springer, 2009.Google ScholarCross Ref
- H. Selvaraj, S. T. Selvi, D. Selvathi, and L. Gewali, "Brain MRI slices classification using least squares support vector machine," International Journal of Intelligent Computing in Medical Sciences & Image Processing, vol. 1, pp. 21-33, 2007.Google ScholarCross Ref
- H. PS, P. BANNIGIDAD, and M. HIREMATH, "DIGITAL MICROSCOPIC IMAGE ANALYSIS OF VIRUS PARTICLES."Google Scholar
- D. H. Wolpert, "Stacked generalization," Neural networks, vol. 5, pp. 241-259, 1992.Google ScholarDigital Library
- A. A. Borkowski, M. M. Bui, L. B. Thomas, C. P. Wilson, L. A. DeLand, and S. M. Mastorides, "Lung and colon cancer histopathological image dataset (lc25000)," arXiv preprint arXiv:1912.12142, 2019.Google Scholar
- 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, vol. 21, p. 748, 2021Google ScholarCross Ref
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