Enhancing Lung Cancer Detection: A Hybrid Approach with FbAC-NET and Bi-LRCN
Keywords:
extraction, pre-processing, optimization algorithm, diagnosis, FbAC-NET, lung cancer, BI-LRCNAbstract
Lung cancer remains a significant public health concern, necessitating accurate and efficient methods for early diagnosis. Traditional diagnostic techniques often rely on manual interpretation of medical images, leading to subjective results and increased dependency on the expertise of radiologists. In clinical practice, lung X-ray pictures are also periodically reviewed by field professionals. Here we propose a hybrid model that combines two powerful deep learning architectures. The first component of our model, FbAC-NET, is an advanced neural network based on the Feature-based Adaptive Convolutional Network (FbACN) architecture. FbAC-NET is designed to extract highly discriminative features from lung cancer images, enabling effective classification. Leveraging the advantages of FbAC-NET, we enhance the accuracy of lung cancer classification by incorporating the second component, Bi-LRCN (Bidirectional Long Short-Term Memory Recurrent Convolutional Network). Bi-LRCN is specifically tailored for sequential data analysis, making it suitable for capturing temporal dependencies and patterns in lung cancer progression. By combining the strengths of FbAC-NET and Bi-LRCN, our hybrid model achieves superior performance in multi-classification and detection of lung cancer. We conduct extensive experiments on a sizable dataset of lung cancer images. According to the results, our hybrid model performs superior to other methods. By automating the diagnosis process, our approach has the potential to enhance early detection rates, enabling timely interventions and improving patient outcomes. Utilising a Python experimental design, the system's effectiveness is then evaluated. In the LIDC/IDRI dataset, the suggested classifier performed with performance metrics of 99.89% sensitivity, 99.84% specificity, 99.25% precision, and 99.7% accuracy. For the other dataset the performance metrics were 99.63% sensitivity, 97.9% specificity, 97.6% precision, and 99.8% accuracy.
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Kanavati, F., Toyokawa, G., Momosaki, S., Rambeau, M., Kozuma, Y., Shoji, F., ... & Tsuneki, M. (2020). Weakly-supervised learning for lung carcinoma classification using deep learning. Scientific reports, 10(1), 1-11.
Zhou, T., Lu, H., Yang, Z., Qiu, S., Huo, B., & Dong, Y. (2021). The ensemble deep learning model for novel COVID-19 on CT images. Applied soft computing, 98, 106885.
Wang, Li, Wenlong Ding, Yan Mo, Dejun Shi, Shuo Zhang, Lingshan Zhong, Kai Wang et al. (2021) Distinguishing nontuberculous mycobacteria from Mycobacterium tuberculosis lung disease from CT images using a deep learning framework. European journal of nuclear medicine and molecular imaging 48, 13: 4293-4306.
Singh, Gur Amrit Pal, and P. K. Gupta. (2019) Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans. Neural Computing and Applications, 31, 10: 6863-6877.
Nageswaran, S., Arunkumar, G., Bisht, A. K., Mewada, S., Kumar, J. N. V. R., Jawarneh, M., & Asenso, E. (2022). Lung cancer classification and prediction using machine learning and image processing. BioMed Research International,
Haque, Intisar Rizwan I., and Jeremiah Neubert. (2020) Deep learning approaches to biomedical image segmentation. Informatics in Medicine Unlocked, 18: 100297.
Liu, Zhuo, Chenhui Yao, Hang Yu, and Taihua Wu. (2019) Deep reinforcement learning with its application for lung cancer detection in medical Internet of Things. Future Generation Computer Systems, 97: 1-9.
Farhat, Hanan, George E. Sakr, and Rima Kilany. (2020), Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19. Machine vision and applications, 31, 6: 1-42.
Pradhan, Kanchan, and Priyanka Chawla. (2020), Medical Internet of things using machine learning algorithms for lung cancer detection. Journal of Management Analytics 7, 4: 591-623.
Dong, N., Zhao, L., Wu, C. H., & Chang, J. F. (2020). Inception v3 based cervical cell classification combined with artificially extracted features. Applied Soft Computing, 93, 106311.
Shakeel, P. Mohamed, Mohd Aboobaider Burhanuddin, and Mohamad Ishak Desa. (2019) Lung cancer detection from CT image using improved profuse clustering and deep learning instantaneously trained neural networks. Measurement, 145: 702-712.
Seshadri Ramana, K., Bala Chowdappa, K., Obulesu, O., Mandru, D. B., & Kallam, S. (2022). Deep convolution neural networks learned image classification for early cancer detection using lightweight. Soft Computing, 1-7.
Shakeel, P. Mohamed, M. A. Burhanuddin, and Mohammad Ishak Desa. (2020) Automatic lung cancer detection from CT image using improved deep neural network and ensemble classifier. Neural Computing and Applications: 1-14.
Jiang, Jue, Yu-Chi Hu, Chia-Ju Liu, Darragh Halpenny, Matthew D. Hellmann, Joseph O. Deasy, Gig Mageras, and Harini Veeraraghavan. (2018). Multiple resolution residually connected feature streams for automatic lung tumor segmentation from CT images. IEEE transactions on medical imaging, 38, 1: 134-144.
Suresh, Supriya, and Subaji Mohan. (2020). ROI-based feature learning for efficient true positive prediction using convolutional neural network for lung cancer diagnosis. Neural Computing and Applications, 32, 20: 15989-16009.
Saygılı, A. (2021). A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods. Applied Soft Computing, 105, 107323.
Sivakumar, R., and E. Mohan. "High resolution satellite image enhancement using discrete wavelet transform." International Journal of Applied Engineering Research 13.11 (2018): 9811-9815.
T. Gladstan and E.Mohan, “Novel Approach Object Recognition Using Efficient Support Vector Machine Classifier,” International Journal of Electronics and Communication Engineering and Technology, vol. 8, no. 2, 2017, pp. 81-90.
Rajesh, A., and E. Mohan. "Classification of Mammogram Using Wave Atom Transform and Support Vector Machine Classifier." International Journal of Computer Science and (2016): 467-470.
Mohan, E., R. Sugumar, and K. Venkatachalam. "Automatic brain and tumor segmentation in MRI using fuzzy classification with integrated Bayesian." Int. J. Appl. Eng. Res 9.24 (2014): 25859-25870.
R. Geetha, S. Vidhya, P. J. I. Rani, K. T. MeenaAbarna, K. Sudhaakar and E. Mohan, "Deep Learning Models for Early Diagnosis of Pancreatic Cancer," 2023 4th International Conference on Computation, Automation and Knowledge Management (ICCAKM), Dubai, United Arab Emirates, 2023, pp. 01-06, doi: 10.1109/ICCAKM58659.2023.10449518.
Sugumar, R., and E. Mohan. "Magnetic Resonance Imaging Segmentation for Brain Tumor Detection Using New Robust Global Kernel Fuzzy C-Means Clustering Algorithm (NRGKFCM-F)." International Journal of Applied Engineering Research 9.21 (2014): 10889-10908
Arulananth, T. S., Balaji, L., Baskar, M., Anbarasu, V., & Rao, K. S. (2023). PCA based dimensional data reduction and segmentation for DICOM images. Neural Processing Letters, 55(1), 3-17.
Prasad, S. V. S., Savithri, T. S., & Krishna, I. V. M. (2017). Comparison of accuracy measures for RS image classification using SVM and ANN classifiers. International Journal of Electrical and Computer Engineering, 7(3), 1180.
Lakshmanachari, S., Srihari, C., Ajmera, S., & Nalajala, P. (2017, August). Design and implementation of cloud based patient health care monitoring systems using IoT. In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) (pp. 3713-3717). IEEE.
TS, A. (2019). Human face detection and recognition using contour generation and matching algorithm. Indonesian Journal of Electrical Engineering and Computer Science, 16(2).
Arulananth, T. S., Kuppusamy, P. G., Ayyasamy, R. K., Alhashmi, S. M., Mahalakshmi, M., Vasanth, K., & Chinnasamy, P. (2024). Semantic segmentation of urban environments: Leveraging U-Net deep learning model for cityscape image analysis. Plos one, 19(4), e0300767.
Lakshmanaprabu, S.K., Mohanty, S.N., Shankar, K., Arunkumar, N. and Ramirez, G., (2019). The optimal deep learning model for classification of lung cancer on CT images. Future Generation Computer Systems, 92, 374-382.
Polat, H., & Danaei Mehr, H. (2019). Classification of pulmonary CT images by using hybrid 3D-deep convolutional neural network architecture. Applied Sciences, 9(5), 940.
Ali, I., Muzammil, M., Haq, I. U., Khaliq, A. A., & Abdullah, S. (2020). Efficient lung nodule classification using transferable texture convolutional neural network. IEEE Access, 8, 175859-175870.
Lin, C. J., Jeng, S. Y., & Chen, M. K. (2020). Using 2D CNN with Taguchi parametric optimization for lung cancer recognition from CT images. Applied Sciences, 10(7), 2591.
Yu, H., Zhou, Z., & Wang, Q. (2020). Deep Learning Assisted Predict of Lung Cancer on Computed Tomography Images Using the Adaptive Hierarchical Heuristic Mathematical Model. IEEE Access, 8, 86400-86410.
Asuntha, A., & Srinivasan, A. (2020). Deep learning for lung cancer detection and classification. Multimedia Tools and Applications, 79(11), 7731-7762.
Wang, W., & Charkborty, G. (2021). Automatic prognosis of lung cancer using heterogeneous deep learning models for nodule detection and eliciting its morphological features. Applied Intelligence, 51(4), 2471-2484.
Shafi, I., Din, S., Khan, A., Díez, I. D. L. T., Casanova, R. D. J. P., Pifarre, K. T., & Ashraf, I. (2022). An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network. Cancers, 14(21), 5457.
Ren, Z., Zhang, Y., & Wang, S. (2022). A Hybrid Framework for Lung Cancer Classification. Electronics, 11(10), 1614.
Jacobs, C., van Rikxoort, E. M., Murphy, K., Prokop, M., Schaefer-Prokop, C. M., & van Ginneken, B. (2016). Computer-aided detection of pulmonary nodules: a comparative study using the public LIDC/IDRI database. European radiology, 26(7), 2139-2147.
Sirazitdinov, I., Kholiavchenko, M., Mustafaev, T., Yixuan, Y., Kuleev, R., & Ibragimov, B. (2019). Deep neural network ensemble for pneumonia localization from a large-scale chest x-ray database. Computers & electrical engineering, 78, 388-399.
Lakshmanaprabu, S. K., Mohanty, S. N., Shankar, K., Arunkumar, N., & Ramirez, G. (2019). Optimal deep learning model for classification of lung cancer on CT images. Future Generation Computer Systems, 92, 374-382.
Khan, M. A., Rubab, S., Kashif, A., Sharif, M. I., Muhammad, N., Shah, J. H., & Satapathy, S. C. (2020). Lungs cancer classification from CT images: An integrated design of contrast based classical features fusion and selection. Pattern Recognition Letters, 129, 77-85.
Toğaçar, M., Ergen, B., & Cömert, Z. (2020). Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. Biocybernetics and Biomedical Engineering, 40(1), 23-39.
Lin, C. J., Jeng, S. Y., & Chen, M. K. (2020). Using 2D CNN with Taguchi parametric optimization for lung cancer recognition from CT images. Applied Sciences, 10(7), 2591.
Shakeel, P. M., Burhanuddin, M. A., & Desa, M. I. (2020). Automatic lung cancer detection from CT image using improved deep neural network and ensemble classifier. Neural Computing and Applications, 1-14.
Motamed, S., Rogalla, P., & Khalvati, F. (2021). Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images. Informatics in Medicine Unlocked, 27, 100779.
Rajasenbagam, T., Jeyanthi, S., & Pandian, J. A. (2021). Detection of pneumonia infection in lungs from chest X-ray images using deep convolutional neural network and content-based image retrieval techniques. Journal of Ambient Intelligence and Humanized Computing, 1-8.
Chouhan, V., Singh, S. K., Khamparia, A., Gupta, D., Tiwari, P., Moreira, C., & De Albuquerque, V. H. C. (2020). A novel transfer learning based approach for pneumonia detection in chest X-ray images. Applied Sciences, 10(2), 559.
Elshennawy, N. M., & Ibrahim, D. M. (2020). Deep-pneumonia framework using deep learning models based on chest X-ray images. Diagnostics, 10(9), 649.
Wu, H., Xie, P., Zhang, H., Li, D., & Cheng, M. (2020). Predict pneumonia with chest X-ray images based on convolutional deep neural learning networks. Journal of Intelligent & Fuzzy Systems, 39(3), 2893-2907.
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