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Related Experiment Video

Updated: Jun 14, 2025

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

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Computer-aided diagnosis for lung cancer using waterwheel plant algorithm with deep learning.

Sana Alazwari¹, Jamal Alsamri², Mashael M Asiri³

¹Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, 21944, Taif, Saudi Arabia.

Scientific Reports

|September 4, 2024

Summary

This study introduces an AI-driven approach for early lung cancer detection using CT scans. The CADLC-WWPADL method achieves 99.05% accuracy, aiding radiologists in diagnosis.

Keywords:

Computed tomography Computer-aided diagnosis Deep learning Hyperparameter tuning Lung cancer Medical imaging

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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
Oncology

Background:

Lung cancer (LC) poses a significant global health threat.
Early diagnosis and treatment are crucial for improving patient outcomes.
Interpreting CT scans for lung tumors presents challenges for clinicians.

Purpose of the Study:

To develop an AI-based computer-aided diagnosis system for lung cancer detection.
To classify and identify the presence of lung cancer in CT scans.
To enhance the accuracy and efficiency of lung cancer diagnosis.

Main Methods:

Utilized a computer-aided diagnosis for LC using the Waterwheel Plant Algorithm with Deep Learning (CADLC-WWPADL).
Employed MobileNet for feature extraction and a Symmetrical Autoencoder (SAE) for classification.
Applied the Waterwheel Plant Algorithm (WWPA) for hyperparameter tuning.

Main Results:

The CADLC-WWPADL technique demonstrated significant detection outputs.
Achieved a maximum accuracy of 99.05% on a benchmark CT image dataset.
Outperformed other models in comparative studies.

Conclusions:

The CADLC-WWPADL approach shows high effectiveness in detecting lung cancer from CT scans.
AI and deep learning can significantly aid radiologists in early and accurate lung cancer diagnosis.
This method holds promise for improving patient survival rates through timely intervention.