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
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© 2022 by IJETT Journal
Volume-70 Issue-5
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

Abstract
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).

Keywords
Lung Cancer, Deep Learning, Python, Classification, Prediction.

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