Empowering Histopathological Breast Cancer Diagnosis through Convolutional Neural Networks

Authors

  • Thyagaraj T. Department of Electronics and Communication, BMS Institute of Technology and Management, Visvesvaraya Technological University, Belagavi, India
  • Keshava Prasanna College of Horticulture Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Shivamogga, India
  • Hariprasad S. A. Faculty of Engineering and Technology JAIN deemed to be university, Bengaluru, India

Keywords:

convolutional neural networks, breast cancer

Abstract

Recent advancements in Convolutional Neural Networks (CNNs) have significantly supported the field of breast cancer discovery using medical imaging. This paper explains a custom CNN framework for breast cancer detection employing histopathological images. We examine improved CNN performances in the classification of breast cancer highlighting the effectiveness of convolutional neural networks. The paper also discusses the impact of specific data augmentation like picture in picture on CNN performance and the disadvantage of training standard CNN models by curated datasets. With the highest accuracy documented as 90.50% for standard histopathological data, the performance of the model when validated on data sourced from oncology hospital is presented. The hyperparameters like learning rate of the model that present the optimal performance is compared and presented.  A comparative analysis of the CNN model performance for different arhitectures is presented and the best validation accuracy of the model obtained is 91.31%. A summary of key findings and future research directions, emphasizing the need of custom CNN models in breast cancer detection is provided.

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Published

24.03.2024

How to Cite

T., T. ., Prasanna, K. ., & S. A., H. . (2024). Empowering Histopathological Breast Cancer Diagnosis through Convolutional Neural Networks. International Journal of Intelligent Systems and Applications in Engineering, 12(18s), 90–98. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/4954

Issue

Vol. 12 No. 18s (2024)

Section

Research Article

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