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Jaya Ant lion optimization-driven Deep recurrent neural network for cancer classification using gene expression data.

Ramachandro Majji1, G Nalinipriya2, Ch Vidyadhari3

  • 1Department of Computer Science and Engineering, GMR Institute of Technology, GMR Nagar, Razam, Andhra Pradesh, 532127, India. rama00565@gmail.com.

Medical & Biological Engineering & Computing
|April 14, 2021
PubMed
Summary
This summary is machine-generated.

Early cancer detection is crucial for survival. This study introduces a JayaAnt Lion Optimization-based Deep Recurrent Neural Network (JayaALO-based DeepRNN) for accurate cancer classification, achieving 95.97% accuracy.

Keywords:
Ant lion optimizationCancer classificationDeep recurrent neural networkJaya algorithmNon-negative matrix factorization

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Area of Science:

  • Computational biology and bioinformatics
  • Artificial intelligence in healthcare
  • Machine learning for medical diagnosis

Background:

  • Cancer is a leading cause of death worldwide, with early detection significantly improving patient survival rates.
  • Accurate classification of tumors as malignant or benign is essential for timely and effective treatment.
  • Existing diagnostic methods can be limited, necessitating the development of automated and precise prediction systems.

Purpose of the Study:

  • To develop a novel JayaAnt Lion Optimization-based Deep Recurrent Neural Network (JayaALO-based DeepRNN) for automated cancer classification.
  • To enhance the accuracy and reliability of early cancer detection through an advanced deep learning approach.
  • To provide a robust strategy for distinguishing between malignant and benign tumors.

Main Methods:

  • The JayaALO-based DeepRNN model incorporates data normalization, log transformation for data transformation, and non-negative matrix factorization for feature dimension reduction.
  • Deep Recurrent Neural Network (DeepRNN) is trained using the JayaALO algorithm, a hybrid of the Ant Lion Optimization (ALO) and Jaya algorithm.
  • The classification process utilizes the reduced dimension features extracted from the transformed data.

Main Results:

  • The proposed JayaALO-based DeepRNN model achieved a maximal accuracy of 95.97%.
  • Maximal sensitivity and specificity were recorded at 95.95% and 96.96%, respectively, demonstrating high diagnostic performance.
  • The method effectively classifies cancer using reduced dimension features, yielding satisfactory and reliable outcomes.

Conclusions:

  • The JayaALO-based DeepRNN presents a highly effective strategy for early cancer classification, crucial for preventing organ damage.
  • The hybrid optimization approach significantly improves the performance of DeepRNN in cancer diagnosis.
  • This automated system offers a promising tool for improving cancer detection rates and patient prognoses.