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An Advanced Lung Carcinoma Prediction and Risk Screening Model Using Transfer Learning.

Isha Bhatia¹, Aarti¹, Syed Immamul Ansarullah²

¹Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144001, India.

Diagnostics (Basel, Switzerland)

|July 13, 2024

Summary

This summary is machine-generated.

This study introduces an advanced lung carcinoma prediction model using transfer learning for early lung cancer detection. The model achieves high accuracy in identifying and assessing risk, improving upon current prediction methods.

Keywords:

CT image deep learning (DL)lung carcinoma machine learning (ML)

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Lung cancer (lung carcinoma) poses a significant mortality risk, emphasizing the need for early diagnosis.
Current lung cancer prediction models struggle with accuracy, noise, and low contrast in medical images.
Effective early detection and risk assessment are crucial for improving patient outcomes.

Purpose of the Study:

To propose an advanced lung carcinoma prediction and risk screening model utilizing transfer learning.
To address limitations of existing models, including low accuracy, noise, and low contrast in computed tomography (CT) images.
To enhance early lung cancer diagnosis and risk stratification.

Main Methods:

Preprocessing of lung CT images: noise removal, contrast stretching, convex hull lung region extraction, and edge enhancement.
Image segmentation using the modified Bates distribution coati optimization (B-RGS) algorithm for feature extraction.
Classification of lung cancer as normal or abnormal using a PResNet classifier, followed by risk screening (low/high risk).

Main Results:

The proposed model achieved high performance metrics comparable to state-of-the-art methods.
Achieved accuracy of 98.21%, precision of 98.71%, and recall of 97.46%.
Demonstrated effectiveness in early lung carcinoma prediction and risk assessment.

Conclusions:

The developed transfer learning model offers an efficient and effective solution for early lung cancer detection.
The methodology shows significant promise in improving the accuracy and reliability of lung carcinoma risk screening.
This approach validates the potential of advanced AI techniques in enhancing oncological diagnostics.