Deep Radial Recurrent Feedforward Neural Nets (DRRFNN): A Stacked L2L Learning Model for Lung Cancer Patients' Data
Deep Radial Recurrent Feedforward Neural Nets (DRRFNN): A Stacked L2L Learning Model for Lung Cancer Patients` Data
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Authors : Ajni K Ajai, A. Anitha
|DOI : 10.14445/22315381/IJETT-V70I5P221|
How to Cite?
Ajni K Ajai, A. Anitha, "Deep Radial Recurrent Feedforward Neural Nets (DRRFNN): A Stacked L2L Learning Model for Lung Cancer Patients' Data," International Journal of Engineering Trends and Technology, vol. 70, no. 5, pp. 194-200, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I5P221
Artificial intelligence (AI) is one of the latest advances in the early detection of lung cancer. Researchers expect that employing AI to sustain lung cancer detection could complete the methodology faster and more effectively and eventually help to predict more patients at a premature step. Deep learning has been validated as a prevalent and more productive approach in numerous medical imaging diagnosis fields. This research designates the deep learning and python programming language to frame highly accurate lung cancer classification and prognosis. Researchers portray a precise stacked L2L model termed Deep Radial Recurrent Feedforward Neural Nets (DRRFNN). The proposed method DRRFNN manifests adequate attainment on Lung cancer data compared with six existing designs such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), Radial Basis Function (RBF), Deep Belief Network (DBN), Feedforward Neural Network (FNN) and Artificial Neural Network (ANN).
Lung Cancer, Deep Learning, Python, Classification, Prediction.
 M. Jasim et al., A Survey of Machine Learning Approaches Applied to Gene Expression Analysis for Cancer Prediction, IEEE Access. 4 (2022) 1–1. DOI:10.1109/ACCESS.2022.3146312.
 A. Bankar, K. Padamwar, and A. Jahagirdar, Symptom Analysis Using a Machine Learning Approach for Early Stage Lung Cancer, Proc. 3rd Int. Conf. Intell. Sustain. Syst. ICISS. (2020) 246–250. DOI: 10.1109/ICISS49785.2020.9315904.
 S. Oh, J. Im, S. R. Kang, I. J. Oh, and M. S. Kim, PET-Based Deep-Learning Model for Predicting Prognosis of Patients with NonSmall Cell Lung Cancer, IEEE Access. 9 (2021) 138753–138761. DOI: 10.1109/ACCESS.2021.3115486.
 M. Bicakci, O. Ayyildiz, Z. Aydin, A. Basturk, S. Karacavus, and B. Yilmaz, Metabolic Imaging Based Sub-Classification of Lung Cancer, IEEE Access. 8 (2020) 218470–218476. DOI: 10.1109/ACCESS.2020.3040155.
 M. Si, T. J. Tarnoczi, B. M. Wiens, and K. Du, Development of Predictive Emissions Monitoring System Using Open Source Machine Learning Library-Keras: A Case Study on a Cogeneration Unit, IEEE Access. 7(10) (2019) 113463–113475. DOI: 10.1109/ACCESS.2019.2930555.
 Y. Cheng et al., Accelerating End-to-End Deep Learning Workflow with Codesign of Data Preprocessing and Scheduling, IEEE Trans. Parallel Distrib. Syst. 32(7) (2020) 1–1. DOI: 10.1109/TPDS.2020.3047966.
 B. Fu, P. Liu, J. Lin, L. Deng, K. Hu, and H. Zheng, Predicting Invasive Disease-Free Survival for Early Stage Breast Cancer Patients Using Follow-Up Clinical Data, IEEE Trans. Biomed. Eng. 66(7) (2019) 2053–2064. DOI: 10.1109/TBME.2018.2882867.
 M. Nazar, M. M. Alam, E. Yafi, and M. M. Su’ud, A Systematic Review of Human–Computer Interaction and Explainable Artificial Intelligence in Healthcare with Artificial Intelligence Techniques, IEEE Access. 9 (2021) 153316–153348. DOI: 10.1109/ACCESS.2021.3127881.
 J. Theis, W. L. Galanter, A. D. Boyd, and H. Darabi, Improving the In-Hospital Mortality Prediction of Diabetes ICU Patients Using a Process Mining/Deep Learning Architecture, IEEE J. Biomed. Heal. Informatics. 26(1) (2022) 388–399. DOI: 10.1109/JBHI.2021.3092969.
 L. Li, F. Yang, J. Li, B. Xu, and D. Wang, Research Progress on Benign and Malignant Lung Nodule Classification Based on Deep Learning, Cross-Strait Radio Sci. Wirel. Technol. Conf. CSRSWTC - Proc. (2020) 23–25. DOI: 10.1109/CSRSWTC50769.2020.9372585.
 A. Shrestha and A. Mahmood, Review of Deep Learning Algorithms and Architectures, IEEE Access. 7 (2019) 53040–53065. DOI: 10.1109/ACCESS.2019.2912200.
 D. Sun, M. Wang, and A. Li, A Multimodal Deep Neural Network for Human Breast Cancer Prognosis Prediction by Integrating Multi-Dimensional Data, IEEE/ACM Trans. Comput. Biol. Bioinforma. 16(3) (2019) 841–850. DOI: 10.1109/TCBB.2018.2806438.
 K. Liao, Y. Zhao, J. Gu, Y. Zhang, and Y. Zhong, Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification, IEEE Access. 9 (2021) 27182–27188. DOI: 10.1109/ACCESS.2021.3053427.
 M. Saha and C. Chakraborty, Her2Net: A Deep Framework for Semantic Segmentation and Classification of Cell Membranes and Nuclei in Breast Cancer Evaluation, IEEE Trans. Image Process. 27(5) (2018) 2189–2200. DOI: 10.1109/TIP.2018.2795742.
 S. Yun et al., Deep Learning-Based Ground Vibration Monitoring: Reinforcement Learning and RNN-CNN Approach, IEEE Geosci. Remote Sens. Lett. 19 (2022) 1–5. DOI: 10.1109/LGRS.2021.3067974.
 I. Ullah and Q. H. Mahmoud, Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks, IEEE Access. 9 (2021) 103906–103926. DOI: 10.1109/ACCESS.2021.3094024.
 Z. Gan, W. Sun, K. Liao, and X. Yang, Probabilistic Modeling for Image Registration Using Radial Basis Functions: Application to Cardiac Motion Estimation, IEEE Trans. Neural Networks Learn. Syst. PP (2022) 1–15. DOI: 10.1109/tnnls.2022.3141119.
 C. Y. Low, J. Park, and A. B. J. Teoh, Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern Classification, IEEE Trans. Cybern. 50(12) (2020) 5021–5034. DOI: 10.1109/TCYB.2019.2908387.
 Y. Chen, Y. Wang, F. Hu, L. Feng, T. Zhou, and C. Zheng, Ldnnet: Towards Robust Classification of Lung Nodule and CancerUsing Lung Dense Neural Network, IEEE Access. 9 (2021) 50301–50320. DOI: 10.1109/ACCESS.2021.3068896.
 M. Li et al., The Deep Learning Compiler: A Comprehensive Survey, IEEE Trans. Parallel Distrib. Syst. 32(3) (2021) 708–727. DOI: 10.1109/TPDS.2020.3030548.
 Rodriguez-Puigvert, R. Martinez-Cantin, and J. Civera, Bayesian Deep Neural Networks for Supervised Learning of Single-View Depth, IEEE Robot. Autom. Lett. (2022). DOI: 10.1109/LRA.2022.3142915.
 T. H. Vu, T. Van Nguyen, and S. Kim, Cooperative NOMA-Enabled SWIPT IoT Networks with Imperfect SIC: Performance Analysis and Deep Learning Evaluation, IEEE Internet Things J. (2021). DOI: 10.1109/JIOT.2021.3091208.
 I. Hammad, R. Simpson, H. D. Tsague, and S. Hall, Using Deep Learning to Automate the Detection of Flaws in Nuclear Fuel Channel UT Scans, IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 69(1) (2022) 323–329. DOI: 10.1109/TUFFC.2021.3112078.
 S. Wang et al., An Ensemble-Based Densely-Connected Deep Learning System for Assessment of Skeletal Maturity, IEEE Trans. Syst. Man, Cybern. Syst. 52(1) (2022) 426–437. DOI: 10.1109/TSMC.2020.2997852.
 T. Chaudhuri, M. Wu, Y. Zhang, P. Liu, and X. Li, An Attention-Based Deep Sequential GRU Model for Sensor Drift Compensation, IEEE Sens. J. 21(6) (2021) 7908–7917. DOI: 10.1109/JSEN.2020.3044388.
 M. A. Labarbera et al., New Radiomic Markers of Pulmonary Vein Morphology Associated with Post-Ablation Recurrence of Atrial Fibrillation, IEEE J. Transl. Eng. Heal. Med. 10(7) (2022). DOI: 10.1109/JTEHM.2021.3134160.
 O. Ozdemir, R. L. Russell, and A. A. Berlin, A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans, IEEE Trans. Med. Imaging. 39(5) (2019) 1419–1429. DOI: 10.1109/TMI.2019.2947595.
 H. Xiao, W. Pei, W. Deng, L. Kong, H. Sun, and C. Tang, A Comparative Study of Deep Neural Network and Meta-Model Techniques in Behavior Learning of Microgrids, IEEE Access. 8 (2020) 30104–30118. DOI: 10.1109/ACCESS.2020.2972569.
 H. Shakir, T. Khan, H. Rasheed, and Y. Deng, Radiomics Based Bayesian Inversion Method for Prediction of Cancer and Pathological Stage, IEEE J. Transl. Eng. Heal. Med. 9(5) (2021) 1–8. DOI: 10.1109/JTEHM.2021.3108390.
 Q. Mu and J. Wang, CNAPE: A Machine Learning Method for Copy Number Alteration Prediction from Gene Expression, IEEE/ACM Trans. Comput. Biol. Bioinforma. 18(1) (2021) 306–311. DOI:10.1109/TCBB.2019.2944827.