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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Developing an Efficient Cancer Detection and Prediction Tool Using Convolution Neural Network Integrated with Neural

Roshan Gangurde¹, Vishal Jagota², Mohammad Shahbaz Khan³

¹School of Computer Science, MIT World Peace University, Pune, India.

Biomed Research International

|February 10, 2023

Summary

This study introduces a novel Convolution Neural Network (CNN) and neural pattern recognition (NPR) model for accurate cancer-type prediction. Achieving 94% accuracy, this computational approach aids in early cancer diagnosis and treatment planning.

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Area of Science:

Computational biology
Bioinformatics
Medical diagnostics

Background:

Current cancer diagnosis methods are becoming outdated due to rising cancer incidence.
Accurate cancer-type prediction is crucial for effective treatment strategies.
Gene expression data holds significant potential for improving cancer diagnosis.

Purpose of the Study:

To develop and evaluate a novel computational model for accurate cancer-type prediction.
To integrate Convolutional Neural Networks (CNN) with Neural Pattern Recognition (NPR) for enhanced diagnostic capabilities.
To leverage high-dimensional gene expression data for predicting cancer type based on tissue of origin.

Main Methods:

A novel architecture combining 1-D Convolutional Neural Networks (CNN) with Neural Pattern Recognition (NPR) was developed.
The model was trained and tested on gene expression profiles from a 5000-person sample.
The model accounts for the tissue of origin in its cancer type prediction.

Main Results:

The proposed CNN-NPR model achieved a prediction accuracy of 94%.
The model demonstrated efficiency by requiring fewer parameters for training.
The integration of CNN and NPR proved effective for cancer type prediction using gene expression data.

Conclusions:

The developed CNN-NPR model shows significant promise for long-term cancer diagnosis and detection.
This computational approach offers an effective and efficient methodology for cancer-type prediction.
The model's high accuracy highlights the potential of integrating advanced computational techniques with genomic data in oncology.