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Related Experiment Video

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

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Multi-Process Remora Enhanced Hyperparameters of Convolutional Neural Network for Lung Cancer Prediction.

Jothi Prabha Appadurai¹, Suganeshwari G², Balasubramanian Prabhu Kavin³

¹Computer Science and Engineering Department, Kakatiya Institute of Technology and Science, Warangal 506015, Telangana, India.

|March 29, 2023

Summary

This summary is machine-generated.

This study introduces a novel Multi-Process Remora Optimized Hyperparameters of Convolutional Neural Network (MPROH-CNN) for accurate lung cancer prediction from CT images. The developed method achieved a high accuracy of 0.98 in predicting lung cancer.

Keywords:

CT image feature extraction and pre-processing lung cancer prediction performance matrices remora optimization algorithm

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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

Lung cancer prediction is critical for reducing mortality rates.
Existing methods often suffer from reduced accuracy during prediction.
The need for advanced techniques in medical image analysis is evident.

Purpose of the Study:

To develop an optimized deep learning model for accurate lung cancer prediction using CT images.
To enhance the classification efficiency of lung cancer detection.
To introduce the Multi-Process Remora Optimized Hyperparameters of Convolutional Neural Network (MPROH-CNN) for this purpose.

Main Methods:

Utilized open-source CT image databases for lung cancer detection.
Applied pre-processing techniques (filtering, contrast enhancement) to remove noise.
Employed feature extraction methods including histogram, texture, and wavelet analysis.
Developed a hybrid classifier combining Convolutional Neural Network (CNN) with Remora Optimization Algorithm (ROA) for multi-process optimization (structure and hyperparameter).

Main Results:

The MPROH-CNN model demonstrated superior performance in lung cancer prediction.
Achieved a high accuracy level of 0.98 in the prediction task.
Outperformed traditional methods like CNN, CNN-Particle Swarm Optimization (PSO), and CNN-Firefly Algorithm (FA).

Conclusions:

The MPROH-CNN approach offers a robust and accurate solution for lung cancer prediction from CT scans.
The integration of ROA with CNN significantly improves prediction accuracy.
This method holds potential for improving early detection and patient outcomes in lung cancer diagnosis.