An Efficient Model for Optimizing Hyperparameters in AlexNet for Precise Malignancy Detection in Lung and Colon Histopathology Images with CSIP-EHE
DOI:
https://doi.org/10.56294/dm2025184Keywords:
Hyperparameter Optimization, Malignancy Detection, AlexNet, Histopathology Images, Counteracting Suboptimal Image Processing (CSIP), Enhanced Histogram Equalization (EHE), Bayesian Optimization, Deep Learning, Colon Cancer PredictionAbstract
Cancer, a lethal disease stemming from genetic anomalies and biochemical irregularities, presents a major global health challenge, with lung and colon cancers being significant contributors to morbidity and mortality. Timely and precise cancer detection is crucial for optimal treatment decisions, and machine learning and deep learning techniques offer a promising solution for expediting this process. In this research, a pre-trained neural network, specifically AlexNet, was fine-tuned with modifications to four layers to adapt it to a dataset comprising histopathological images of lung and colon tissues. Additionally, a Bayesian optimization approach was employed for hyperparameter tuning in Convolutional Neural Networks (CNNs) to enhance recognition accuracy while maintaining computational efficiency. The research utilized a comprehensive dataset divided into five classes, and in cases of suboptimal results, a Counteracting Suboptimal Image Processing (CSIP) strategy was applied, focusing on improving images of underperforming classes to reduce processing time and effort.
References
1. World Fact Sheet. International Agency for Research on Cancer. Accessed: Oct. 17, 2021. [Online]. Available: https://gco.iarc.fr/today/ data/factsheets/populations/900-world-fact-sheets.pdf.
2. Cancer Symptoms and Causes. Mayo Clinic. Accessed: Oct. 18, 2021. [Online]. Available: https://www.mayoclinic.org/diseases-conditions/cancer/symptoms-causes/syc-20370588.
3. World Health Organization. Accessed: Oct. 18, 2021. [Online]. Available: https://www.who.int/news-room/ fact-sheets/detail/cancer.
4. Mayo Clinic. Accessed:Oct. 18, 2021. [Online]. Available: https://www.mayoclinic.org/ diseases-conditions/cancer/symptoms-causes/syc-20370588.
5. J. Fan, J. Lee, and Y. Lee, ``A transfer learning architecture based on a support vector machine for histopathology image classi_cation,'' Appl. Sci., vol. 11, pp. 1_16, 2021. DOI: https://doi.org/10.3390/app11146380
6. R. Selvanambi, J. Natarajan, M. Karuppiah, S. K. H. Islam, M. M. Hassan, and G. Fortino, ``Lung cancer prediction using higher-order recurrent neural network based on glowworm swarm optimization,'' Neural Comput. Appl., vol. 32, no. 9, pp. 4373_4386, 2018. DOI: https://doi.org/10.1007/s00521-018-3824-3
7. O.de Carvalho Filho, A. C. Silva, A. C. de Paiva, R. A. Nunes, and M. Gattass, ``Classi_cation of patterns of benignity and malignancy based on CT using topology-based phylogenetic diversity index and convolutional neural network,'' Pattern Recognit., vol. 81, pp. 200_212, Sep. 2018. DOI: https://doi.org/10.1016/j.patcog.2018.03.032
8. R. V. M. da Nóbrega, P. P. Rebouças Filho, M. B. Rodrigues, S. P. P. da Silva, C. M. J. M. D. Júnior, and V. H. C. de Albuquerque,``Lung nodule malignancy classi_cation in chest computed tomography images using transfer learning and convolutional neural networks,'' Neural Comput. Appl., vol. 32, no. 15, pp. 11065_11082, Aug. 2020. DOI: https://doi.org/10.1007/s00521-018-3895-1
9. Masood, B. Sheng, P. Li, X. Hou, X. Wei, J. Qin, and D. Feng, ``Computer-assisted decision support system in pulmonary cancer detection and stage classi_cation on CT images,'' J. Biomed. Inf., vol. 79, pp. 117_128, Mar. 2018. DOI: https://doi.org/10.1016/j.jbi.2018.01.005
10. V. Nardone, P. Tini, P. Pastina, C. Botta, and A. Reginelli, ``Radiomics predicts survival of patients with advanced non-small cell lung cancer undergoing PD-1 blockade using Nivolumab,'' Oncol. Lett., vol. 9, no. 2,pp. 1559_1566, 2020. DOI: https://doi.org/10.3892/ol.2019.11220
11. Z. Zhou, S. Li, G. Qin, M. Folkert, S. Jiang, and J.Wang, ``Multi-objective- based radiomic feature selection for lesion malignancy classi_cation,'' IEEE J. Biomed. Health Informat., vol. 24, no. 1, pp. 194_204, Jan. 2020. DOI: https://doi.org/10.1109/JBHI.2019.2902298
12. Z. Liu et al., ``Radiomics of multiparametric MRI for pretreatment pre- diction of pathologic complete response to neoadjuvant chemotherapy in breast cancer: A multicenter study,'' Clin. Cancer Res., vol. 25, no. 12, pp. 3538_3547, Jun. 2019. DOI: https://doi.org/10.1158/1078-0432.CCR-18-3190
13. Y. Xie et al., ``Knowledge-based collaborative deep learning for benign- malignant lung nodule classi_cation on chest CT,'' IEEE Trans. Med. imag., vol. 38, no. 4, pp. 991_1004, Apr. 2019. DOI: https://doi.org/10.1109/TMI.2018.2876510
14. Y. Boers and P. K. Mandal, ``Optimal particle-_lter-based detector,'' IEEE Signal Process. Lett., vol. 26, no. 3, pp. 435_439, Mar. 2019. DOI: https://doi.org/10.1109/LSP.2019.2895279
15. S. Hawkins et al., ``Predicting malignant nodules from screening CT scans,'' J. Thoracic Oncol., vol. 11, no. 12, pp. 2120_2128, Dec. 2016. DOI: https://doi.org/10.1016/j.jtho.2016.07.002
16. L. Tiancheng, B. Miodrag, and D. M. Petar, ``Resampling methods for particle _ltering: Classi_cation, implementation, and strategies,'' Signal Process. Mag., vol. 32, no. 3, pp. 70_86, May 2015.
17. K. Liu and G. Kang, ``Multiview convolutional neural networks for lung nodule classi_cation,'' Int. J. Imag. Syst. Technol., vol. 27, no. 1, pp. 12_22, Mar. 2017. DOI: https://doi.org/10.1002/ima.22206
18. L. Tiancheng, B. Miodrag, and D. M. Petar, ``Resampling methods for particle _ltering: Classi_cation, implementation, and strategies,'' Signal Process. Mag., vol. 32, no. 3, pp. 70_86, May 2015. DOI: https://doi.org/10.1109/MSP.2014.2330626
19. D. R. Aberle et al., ``The national lung screening trial: Overview and study design,'' Radiology, vol. 258, no. 1, pp. 243_253, Jan. 2011. DOI: https://doi.org/10.1148/radiol.10091808
20. T. Khan, P. Ramuhalli, and S. C. Dass, ``Particle-_lter-based multisensory fusion for solving low-frequency electromagnetic NDE inverse problems,'' IEEE Trans. Instrum. Meas., vol. 60, no. 6, pp. 2142_2153, Jun. 2011. DOI: https://doi.org/10.1109/TIM.2011.2117170
21. S. D. Chen and A. R. Ramli, ``Preserving brightness in histogram equalization-based contrast enhancement techniques,'' Digit Signal Pro- cess, vol. 14, no. 5, pp. 413_428, 2004. DOI: https://doi.org/10.1016/j.dsp.2004.04.001
Downloads
Published
Issue
Section
License
Copyright (c) 2025 D Mythrayee, Dinesh Kumar Anguraj, M Gokilavani, V. K. Gnanavel (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
