International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Breast Cancer Detection through Histological Imaging: A Machine Learning Approach

Abstract

Breast cancer is one of the most common types of cancer globally, and early detection is crucial for increasing the chances of survival. Mammography and biopsy are conventional diagnostic methods that are accurate but labor-intensive and prone to human error. Recent machine learning (ML)-based advancements have enabled automated systems for cancer classification that may improve their efficiency. In this study, using histological images of size 700 × 460, a total of 1,148 images, the performance of K-Nearest Neighbor (KNN) classification algorithm for breast cancer classification was evaluated. We split the data, where 70% is trained and 30% is tested. To enhance classification accuracy, various data preprocessing methods and feature selection techniques are implemented. The Results show that KNN is offering another fine Performance with Accuracy, Precision, Recall, and F1 score of 100% as the perfect prediction. Choose one optimal k value such that it provides best classification between (benign and malignant cases), which make the KNN one of the most accurate algorithms for breast cancer classification. The study signifies a paradigm shift in medical image analysis, indicating the efficacy of ML-based approaches over traditional approaches.

Country : Iraq

1 Aisha W. Saadoun2 Doaa A. Mishaal3 Abdullah N. Nadhim4 Ramadhan Abdul Wahab R.5 Matti S. Matti6 Zeena T. Hamdon7 Rusul R. Ghaleeb8 Nada H. Saadallah9 Marwa M. Mohamedsheet Al-Hatab

  1. Technical Engineering College /Northern Technical University, Mosul, Iraq
  2. Technical Engineering College /Northern Technical University, Mosul, Iraq
  3. Technical Engineering College /Northern Technical University, Mosul, Iraq
  4. Technical Engineering College /Northern Technical University, Mosul, Iraq
  5. Sahel Ninevah University College, Iraq
  6. Technical Engineering College /Northern Technical University, Mosul, Iraq
  7. Technical Engineering College /Northern Technical University, Mosul, Iraq
  8. Technical Engineering College /Northern Technical University, Mosul, Iraq
  9. Technical Engineering College /Northern Technical University, Mosul, Iraq

IRJIET, Volume 9, Issue 3, March 2025 pp. 97-103

doi.org/10.47001/IRJIET/2025.903012

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References

  1. D. L. Weaver, "Breast cancer early detection: Imaging techniques and risk models," Radiology, vol. 255, no. 2, pp. 1-9, 2015.
  2. A.B. Smith, J. W. Miller, and S. H. Brooks, "Early detection of breast cancer: Current technologies and future directions," Journal of Cancer, vol. 10, no. 3, pp. 195-203, 2016.
  3. P. A. Olsson, "Mammography and breast cancer screening," Journal of the National Cancer Institute, vol. 107, no. 8, pp. 1202-1215, 2015.
  4. E. M. G. Rayan, "Ultrasound in breast cancer: Diagnostic role and clinical applications," Clinical Imaging, vol. 33, pp. 99-107, 2018.
  5. K. Y. Wei, J. Z. Huo, and L. T. Liang, "Challenges in breast cancer diagnosis using traditional techniques," Medical Imaging, vol. 35, no. 1, pp. 29-36, 2019.
  6. M. J. Santos, A. K. Gupta, and R. F. Thompson, "Challenges in breast cancer detection: A review of clinical and diagnostic techniques," Journal of Health Technology, vol. 14, no. 1, pp. 53-60, 2020.
  7. Z. P. Ahmed, R. S. Yaseen, and H. G. Bahar, "Application of deep learning for breast cancer diagnosis: A review," Journal of Medical Imaging, vol. 44, no. 2, pp. 121-130, 2021.
  8. R. Patel, T. S. Singh, and S. T. Yadav, "Breast cancer detection using machine learning techniques," Computers in Biology and Medicine, vol. 50, pp. 62-71, 2020.
  9. T. K. Le, "K-Nearest Neighbors algorithm in pattern recognition," Journal of Machine Learning, vol. 15, pp. 92-100, 2017.
  10. M. T. Rehman, P. K. Kumar, and S. S. Gupta, "Application of K-Nearest Neighbors for classification of breast cancer histology images," Bioinformatics Journal, vol. 22, pp. 45-56, 2019.
  11. J. D. Boughorbel, M. Ben Othman, and M. L. Gana, "Breast cancer diagnosis: Machine learning techniques for histological image analysis," Medical Image Analysis, vol. 45, pp. 223-235, 2018.
  12. A.S. Jones, "Feature selection techniques for improving machine learning models in breast cancer classification," IEEE Access, vol. 7, pp. 56327-56335, 2020.
  13. S. A. Dube, P. G. Abella, and P. V. Kumar, "Optimizing KNN for medical image classification: A study on feature selection methods," IEEE Transactions on Medical Imaging, vol. 28, no. 1, pp. 65-75, 2022.
  14. H. L. Tan, R. M. Lacy, and W. T. Li, "Large-scale breast cancer datasets for image classification," Journal of Computational Biology, vol. 40, no. 2, pp. 112-118, 2021.
  15. T. L. Williams, "Stratified sampling in medical image datasets," Journal of Health Informatics, vol. 10, no. 3, pp. 201-210, 2018.
  16. M. J. Smith, J. S. Lee, and M. A. Thompson, "Enhancement of mammogram images using contrast adjustment," Journal of Imaging Science, vol. 58, no. 2, pp. 212-219, 2019.
  17. A.G. Foster, P. S. Martin, and F. L. Davis, "Normalization techniques in medical imaging," IEEE Transactions on Biomedical Engineering, vol. 60, no. 5, pp. 1308-1317, 2020.
  18. P. P. Wang, "Standardization of medical image datasets for classification tasks," International Journal of Imaging, vol. 35, pp. 61-70, 2018.
  19. J. S. Kapoor, R. V. Patel, and M. S. Ali, "Noise reduction techniques for medical image processing," Medical Imaging and Technology, vol. 24, pp. 102-110, 2021.
  20. P. T. Zhang, J. L. Lee, and J. F. Yung, "Feature extraction techniques for breast cancer detection in histological images," IEEE Access, vol. 7, pp. 125543-125550, 2020.
  21. H. P. Duan, W. M. Liu, and M. A. Ahmed, "Texture features extraction using GLCM for breast cancer classification," International Journal of Computer Vision, vol. 21, pp. 231-241, 2019.
  22. K. Y. Zhang, M. M. Jones, and R. T. James, "Color feature extraction for medical image classification using RGB and HSV spaces," Biomedical Signal Processing and Control, vol. 53, pp. 81-92, 2021.
  23. S. F. Brown, "Geometric methods in shape analysis of medical images," Journal of Computational Geometry, vol. 50, pp. 115-128, 2020.
  24. R. C. Lee, J. P. Zhao, and M. A. Maher, "Compactness measures for detecting malignant tumors in medical imaging," Medical Image Computing, vol. 63, pp. 72-81, 2020.
  25. W. M. Sutherland, K. G. Jordan, and T. L. Johnson, "Solidity and circularity metrics for tumor detection," Journal of Applied Computer Science, vol. 45, pp. 112-123, 2021.
  26. R. A. Stevens, D. M. Smith, and P. T. Lee, "Edge detection techniques in medical imaging: A comparative study," Journal of Biomedical Engineering, vol. 40, no. 2, pp. 65-73, 2019.
  27. L. H. Zhu, J. Q. Yang, and Z. L. Wu, "Optimizing Canny edge detection for medical image segmentation," Journal of Image Processing, vol. 58, pp. 34-44, 2020.
  28. C. F. Taylor, R. D. Chan, and G. M. Foster, "Histogram of Oriented Gradients: A new method for medical image feature extraction," Medical Imaging Research, vol. 16, pp. 79-90, 2020.
  29. S. R. Gupta, "Recursive Feature Elimination for image classification," Journal of Machine Learning Research, vol. 23, pp. 11-19, 2021.
  30. T. D. Choi, "Mutual information techniques for feature selection in cancer detection," Journal of Artificial Intelligence in Medicine, vol. 45, pp. 34-42, 2020.
  31. L. S. Chen, "A study of KNN-based image classification," International Journal of Computer Vision, vol. 57, no. 4, pp. 121-132, 2019.
  32. H. T. Liao, "Grid search method for optimizing KNN classifier," IEEE Transactions on Machine Learning, vol. 60, pp. 321-334, 2020.
  33. D. J. Schneider, "Precision and recall metrics in medical image classification," Journal of Biomedical Imaging, vol. 41, pp. 201-210, 2020.
  34. S. S. Allen, "F1-score analysis in machine learning classification," Journal of Computational Statistics, vol. 62, pp. 54-63, 2021.
  35. A.T. Patel, "Performance metrics for image classification: An in-depth analysis," IEEE Transactions on Medical Imaging, vol. 67, pp. 182-191, 2022.
  36. J. D. Haines, "Cross-validation for machine learning in medical image classification," Journal of Machine Learning Applications, vol. 51, pp. 123-130, 2018.
  37. M. Mohammad, W. R. Fathel, H. D. Ali, and M. M. Al-Hatab, "Enhanced non-invasive blood glucose monitoring system employing wearable optical technology," Fusion: Practice and Applications (FPA), vol. 19, no. 1, Jan. 2025. DOI: 10.54216/FPA.190101.
  38. M. M. M. Al-Hatab, A. S. I. Al-Obaidi, and M. A. Al-Hashim, "Exploring CIE Lab color characteristics for skin lesion images detection: A novel image analysis methodology incorporating color-based segmentation and luminosity analysis," Fusion: Practice and Applications, vol. 15, no. 1, pp. 88–97, 2024.
  39. W. R. Fathel, M. M. Al-Hatab, and M. A. Qasim, "Classification of ECG signals based on k-nearest neighbors (k-NN) algorithm," Eurasian J. Eng. Technol., vol. 16, Mar. 2023, ISSN: 2795-7640.
  40. M. A. Malla, O. H. Al-Beaka, D. M. Hameed, M. M. M. Al-Hatab, R. O. Al-Nima, M. S. Jarjees, and K. A. K. Al-Maqsood, "Adopting machine learning to automatically identify a suitable surgery type for refractive error patients," Jurnal Kejuruteraan, vol. 36, no. 4, pp. 1749–1757, 2024.

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