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Identifying Cancer Disease Using Softmax-Feed Forward Recurrent Neural Classification

P. Saranya*, P. Asha

Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India

* Corresponding Author: P. Saranya. Email:

Intelligent Automation & Soft Computing 2023, 36(1), 1137-1149.


In today’s growing modern world environment, as human food activities are changing, it is affecting human health, thus leading to diseases like cancer. Cancer is a complex disease with many subtypes that affect human health without premature treatment and cause death. So the analysis of early diagnosis and prognosis of cancer studies can improve clinical management by analyzing various features of observation, which has become necessary to classify the type in cancer research. The research needs importance to organize the risk of the cancer patients based on data analysis to predict the result of premature treatment. This paper introduces a Maximal Region-Based Candidate Feature Selection (MRCFS) for early risk diagnosing using Soft-Max Feed Forward Neural Classification (SMF2NC) to solve the above problem. The predictive model is based on a different relational feature learning model, which is possessed to candidate selection to reduce the dimensionality. The redundant features are processed marginal weight rates for observing similar features’ variants and the absolute value. Softmax neural hidden layers are trained using the Sigmoid Activation Function (SAF) to create the logical condition for feed-forward layers. Further, the maximal features are introduced to invite a deep neural network constructed on the Feed Forward Recurrent Neural Network (FFRNN). The classifier produces higher classification accuracy than the previous methods and observes the cancer detection, which is recommended for early diagnosis.


Cite This Article

P. Saranya and P. Asha, "Identifying cancer disease using softmax-feed forward recurrent neural classification," Intelligent Automation & Soft Computing, vol. 36, no.1, pp. 1137–1149, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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