Deep Learning for Lung Cancer Staging: A Performance Evaluation of Gamma Correction vs Histogram Equalization Using EfficientNetB0
Received: 9 July 2025 | Revised: 14 August 2025, 25 August 2025, 1 September 2025, and 8 September 2025 | Accepted: 9 September 2025 | Online: 8 December 2025
Corresponding author: Andani Achmad
Abstract
An accurate classification of lung cancer stages is critical in supporting the appropriate clinical decision-making and treatment strategies. This study aimed to analyze and compare the performance of two image preprocessing techniques, namely Gamma Correction and Histogram Equalization, in improving the accuracy of lung cancer stage classification based on CT scan images using the EfficientNetB0 deep learning architecture. Both methods were implemented with identical training configurations, including the use of RMSprop optimizers, last 50-layer fine-tuning, and class weighting to address data imbalances. The experimental results showed that Gamma Correction performed better with a test accuracy of 95.23% and a loss of 0.1303, compared to Histogram Equalization, which achieved an accuracy of 94.45% and a loss of 0.1523. In addition, Gamma Correction showed excellence in macro-mean F1-score metrics, especially in improving detection sensitivity in Stage Ib Adenocarcinoma and Squamous Cell Carcinoma IIIa. The training curve shows a consistent convergence trend and no indication of overfitting, with Gamma Correction demonstrating better validation stability. The results of this study confirm that the selection of preprocessing techniques has a considerable influence on the efficacy of the lung cancer stage classification model. Gamma Correction is shown to be more effective in sharpening important morphological features in CT images, while maintaining a balance between increased contrast and noise control. These findings are an important foundation for the development of accurate and reliable CAD systems for automatic lung cancer staging.
Keywords:
lung cancer, classification, EfficientNetB0, gamma correction, histogram equalizationDownloads
References
F. Bray et al., "Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries," CA: A Cancer Journal for Clinicians, vol. 74, no. 3, pp. 229–263, May 2024. DOI: https://doi.org/10.3322/caac.21834
R. Nooreldeen and H. Bach, "Current and Future Development in Lung Cancer Diagnosis," International Journal of Molecular Sciences, vol. 22, no. 16, Aug. 2021, Art. no. 8661. DOI: https://doi.org/10.3390/ijms22168661
A. Riadi, A. Achmad, and Z. Zainuddin, "Performance Analysis of Optimizers on Convolutional Neural Network for Lung Cancer Classification Using CT Scan Images," in 2025 3rd International Conference on Disruptive Technologies (ICDT), Greater Noida, India, Mar. 2025, pp. 1467–1472. DOI: https://doi.org/10.1109/ICDT63985.2025.10986330
R. Pandian, V. Vedanarayanan, D. N. S. Ravi Kumar, and R. Rajakumar, "Detection and classification of lung cancer using CNN and Google net," Measurement: Sensors, vol. 24, Dec. 2022, Art. no. 100588. DOI: https://doi.org/10.1016/j.measen.2022.100588
M. A. Khan et al., "Lungs cancer classification from CT images: An integrated design of contrast based classical features fusion and selection," Pattern Recognition Letters, vol. 129, pp. 77–85, Jan. 2020. DOI: https://doi.org/10.1016/j.patrec.2019.11.014
G. Mohandass, G. Hari Krishnan, D. Selvaraj, and C. Sridhathan, "Lung Cancer Classification using Optimized Attention-based Convolutional Neural Network with DenseNet-201 Transfer Learning Model on CT image," Biomedical Signal Processing and Control, vol. 95, Sep. 2024, Art. no. 106330. DOI: https://doi.org/10.1016/j.bspc.2024.106330
I. Naseer, S. Akram, T. Masood, A. Jaffar, M. A. Khan, and A. Mosavi, "Performance Analysis of State-of-the-Art CNN Architectures for LUNA16," Sensors, vol. 22, no. 12, Jun. 2022, Art. no. 4426. DOI: https://doi.org/10.3390/s22124426
M. Q. Shatnawi, Q. Abuein, and R. Al-Quraan, "Deep learning-based approach to diagnose lung cancer using CT-scan images," Intelligence-Based Medicine, vol. 11, 2025, Art. no. 100188. DOI: https://doi.org/10.1016/j.ibmed.2024.100188
W. Rahman, M. G. G. Faruque, K. Roksana, A. H. M. S. Sadi, M. M. Rahman, and M. M. Azad, "Multiclass blood cancer classification using deep CNN with optimized features," Array, vol. 18, Jul. 2023, Art. no. 100292. DOI: https://doi.org/10.1016/j.array.2023.100292
A. Loddo, F. Pili, and C. Di Ruberto, "Deep Learning for COVID-19 Diagnosis from CT Images," Applied Sciences, vol. 11, no. 17, Sep. 2021, Art. no. 8227. DOI: https://doi.org/10.3390/app11178227
X. Li et al., "Multi-resolution convolutional networks for chest X-ray radiograph based lung nodule detection," Artificial Intelligence in Medicine, vol. 103, Mar. 2020, Art. no. 101744. DOI: https://doi.org/10.1016/j.artmed.2019.101744
D. Xiang, H. Wang, D. He, and C. Zhai, "Research on Histogram Equalization Algorithm Based on Optimized Adaptive Quadruple Segmentation and Cropping of Underwater Image (AQSCHE)," IEEE Access, vol. 11, pp. 69356–69365, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3290201
H. T. Gayap and M. A. Akhloufi, "Deep Machine Learning for Medical Diagnosis, Application to Lung Cancer Detection: A Review," BioMedInformatics, vol. 4, no. 1, pp. 236–284, Jan. 2024. DOI: https://doi.org/10.3390/biomedinformatics4010015
P. Reddi, G. Srinivas, P. V. G. D. Prasad Reddy, and D. S. Krihsna, "A Multi-Head Self-Attention Mechanism for Improved Brain Tumor Classification using Deep Learning Approaches," Engineering, Technology & Applied Science Research, vol. 14, no. 5, pp. 17324–17329, Oct. 2024. DOI: https://doi.org/10.48084/etasr.8484
R. Javed, T. Abbas, A. H. Khan, A. Daud, A. Bukhari, and R. Alharbey, "Deep learning for lungs cancer detection: a review," Artificial Intelligence Review, vol. 57, no. 8, Jul. 2024, Art. no. 197. DOI: https://doi.org/10.1007/s10462-024-10807-1
M. N. Nguyen, "A scoping review of deep learning approaches for lung cancer detection using chest radiographs and computed tomography scans," Biomedical Engineering Advances, vol. 9, Jun. 2025, Art. no. 100138. DOI: https://doi.org/10.1016/j.bea.2024.100138
D. Moitra and R. Kr. Mandal, "Classification of non-small cell lung cancer using one-dimensional convolutional neural network," Expert Systems with Applications, vol. 159, Nov. 2020, Art. no. 113564. DOI: https://doi.org/10.1016/j.eswa.2020.113564
K. Kansal, T. B. Chandra, and A. Singh, "ResNet-50 vs. EfficientNet-B0: Multi-Centric Classification of Various Lung Abnormalities Using Deep Learning," Procedia Computer Science, vol. 235, pp. 70–80, 2024. DOI: https://doi.org/10.1016/j.procs.2024.04.007
R. Raza et al., "Lung-EffNet: Lung cancer classification using EfficientNet from CT-scan images," Engineering Applications of Artificial Intelligence, vol. 126, Nov. 2023, Art. no. 106902. DOI: https://doi.org/10.1016/j.engappai.2023.106902
A. Bhandary et al., "Deep-learning framework to detect lung abnormality – A study with chest X-Ray and lung CT scan images," Pattern Recognition Letters, vol. 129, pp. 271–278, Jan. 2020. DOI: https://doi.org/10.1016/j.patrec.2019.11.013
M. Wu and Q. Zhong, "Image enhancement algorithm combining histogram equalization and bilateral filtering," Systems and Soft Computing, vol. 6, Dec. 2024, Art. no. 200169. DOI: https://doi.org/10.1016/j.sasc.2024.200169
N. Severoglu, Y. Demir, N. H. Kaplan, and S. Kucuk, "ACGC: Adaptive chrominance gamma correction for low-light image enhancement," Journal of Visual Communication and Image Representation, vol. 107, Mar. 2025, Art. no. 104402. DOI: https://doi.org/10.1016/j.jvcir.2025.104402
"Chest CT Scan Image Lung." Kaggle, [Online]. Available: https://www.kaggle.com/datasets/diayruldip/carinocroma.
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Copyright (c) 2025 Annahl Riadi, Andani Achmad, Zahir Zainuddin, Harry Akza Putrawan
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