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Improving Deep Learning Based Lung Nodule Classification Through Optimized Adaptive Intensity Correction.

Saba Khan¹, Muhammad Nouman Noor¹, Haya Mesfer Alshahrani²

¹Department of Artificial Intelligence and Data Science, National University of Computer and Emerging Sciences (FAST-NUCES), Islamabad 44000, Pakistan.

Bioengineering (Basel, Switzerland)

|May 4, 2026

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This summary is machine-generated.

This study introduces an optimized image preprocessing technique using Contrast-Limited Adaptive Histogram Equalization (CLAHE) to improve lung nodule detection in CT scans. The method enhances deep learning model accuracy and reduces false positives for better clinical application.

Area of Science:

Medical Imaging
Artificial Intelligence
Computer-Aided Diagnosis

Background:

Lung cancer diagnosis relies heavily on early detection via CT scans.
Inconsistent image intensity across scanners hinders automated pulmonary nodule classification accuracy.
Deep learning models face performance limitations due to image variations and false positives.

Purpose of the Study:

To develop and evaluate an optimized image preprocessing technique for lung nodule detection.
To address intensity variations in CT scans for improved deep learning model performance.
To reduce false positives in automated lung nodule classification for clinical utility.

Main Methods:

Implemented Contrast-Limited Adaptive Histogram Equalization (CLAHE) with automated parameter tuning using Covariance Matrix Adaptation Evolution Strategy (CMA-ES).

Keywords:

computed tomography imaging contrast enhancement deep learning false positive reduction intensity normalization lung nodule detection

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Applied preprocessing to the LUNA16 dataset, assessing image quality using PSNR and SSIM.

Trained deep learning models (ResNet-50, EfficientNet-B0, InceptionV3) with CutMix augmentation on preprocessed images.

Main Results:

CLAHE preprocessing significantly improved image quality (PSNR ~53 dB, SSIM 0.9) compared to standard methods.
ResNet-50 achieved up to 99.0% classification accuracy on enhanced images.
The optimized preprocessing substantially reduced false positives compared to using raw CT scans.

Conclusions:

Intelligent and optimized preprocessing effectively mitigates intensity variations in CT scans.
This approach enhances deep learning model performance for lung nodule detection.
The method advances the practical application of computer-aided diagnosis in routine clinical practice.