Deep Learning for Early Cancer Detection: A Comprehensive Analysis of Imaging Data

Authors

  • Chhaya Gosavi Comp Dept. Cummins College of Engineering for Women, Pune, India
  • Anjali Naik Comp Dept. Cummins College of Engineering for Women, Pune, India
  • Madhuri Tasgaonkar Comp Dept. Cummins College of Engineering for Women, Pune, India

Keywords:

Early Cancer Detection, Lung Cancer Screening, Otsu Thresholding, Cuckoo Search Algorithm, Convolutional Neural Network (CNN), Genetic Algorithms

Abstract

This examination paper digs into the basic domain of early malignant growth location, zeroing in on cellular breakdown in the lungs, a noticeable reason for mortality. Current screening strategies frequently battle with precise division of assorted disease cell morphologies, prompting less than ideal dependability. Accordingly, we present a powerful screening method consolidating Otsu thresholding division, cuckoo search calculation enhancement, and nearby twofold examples including extraction. The following Convolutional Brain Organization (CNN) classifier actually recognizes threatening and non-harmful lung injuries, accomplishing an amazing 96.97% exactness. Our use of genetic algorithms and Particle Swarm Optimization to compile the results demonstrates a significant improvement in accuracy. This exploration contributes a promising system to the scene of early malignant growth identification, pushing for further developed screening accuracy and, thus, improved patient results.

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References

B. A. Skourt, A. El Hassani, and A. Majda, ‘‘Lung CT image segmentation using deep neural networks,’’ Procedia Comput. Sci., vol. 127, pp. 109–113, Jan. 2018. DOI: https://doi.org/10.1016/j.procs.2018.01.104

F. Silva, T. Pereira, J. Morgado, J. Frade, J. Mendes, C. Freitas, E. Negrão, B. Lima, M. Silva, A. Madureira, I. Ramos, V. Hespanhol, J. Costa, A. Cunha, H. Oliveira, “EGFR Assessment in Lung Cancer CT Images: Analysis of Local and Holistic Regions of Interest Using Deep Unsupervised Transfer Learning,” 2021. [Online]. Available: https://doi.org/10.1109/ACCESS.2021.3070701

S. Pang, Y. Zhang, M. Ding, X. Wang, X. Xie, “A Deep Model for Lung Cancer Type Identification by Densely Connected Convolutional Networks and Adaptive Boosting,” 2020. [online]. Available: https://doi.org/10.1109/ACCESS.2019.2962862

P. M. Shakeel, M. A. Burhanuddin, and M. I. Desa, “Lung cancer detection from CT image using improved profuse clustering and deep learning instantaneously trained neural networks,”2019. Google scholar. DOI: https://doi.org/10.1016/j.measurement.2019.05.027

Iwendi C, Bashir AK, Peshkar A, Sujatha R, Chatterjee JM, Pasupuleti S, et al. COVID-19 patient health prediction using boosted random forest algorithm. Front Public Health. (2020) 8:357. doi: 10.3389/fpubh.2020. 00357

Aparna P, Polurie V, Vijay K. An efficient medical image watermarking technique in e-healthcare application using hybridization of compression and cryptography algorithm. J Intell Syst. (2017) 27:115–33. doi: 10.1515/jisys-2017-0266

Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Fenyö D, et al. Classification and Mutation Prediction From non–Small Cell Lung Cancer Histopathology Images Using Deep Learning. Nat Med (2018) 24:1559–67. doi: 10.1038/s41591-018-0177-5

Revathi A, Kaladevi R, Ramana K, Jhaveri RH, Rudra Kumar M, Sankara Prasanna Kumar M. Early Detection of Cognitive Decline Using Machine Learning Algorithm and Cognitive Ability Test. Secur Communicat Networks (2022) 2021:1–13. doi: 10.1155/2022/4190023

Song Q, Zhao L, Luo X, Dou X. Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images. J Healthcare Eng (2017) 2017:1–7. doi: 10.1155/2017/8314740

Y. Ren, M. Y. Tsai, L. Chen, J. Wang, S. Li et al., “A manifold learning regularization approach to enhance 3D CT image-based lung nodule classification,” International Journal Computer Assisted Radiology and Surgery, vol. 15, no. 2, pp. 287–295, 2020.

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Published

07.01.2024

How to Cite

Gosavi, C. . ., Naik, A. ., & Tasgaonkar, M. . (2024). Deep Learning for Early Cancer Detection: A Comprehensive Analysis of Imaging Data. International Journal of Intelligent Systems and Applications in Engineering, 12(10s), 619–623. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4426

Issue

Vol. 12 No. 10s (2024)

Section

Research Article

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