Advance ANN algorithm and research challenges for breast cancer detection and classification with evaluation Ni LabVIEW
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Date
2025-07-15
Authors
Haider, Natasha
Jabeen Mazhar, Iqra
Haider, Shehpara
Ghazanfar, Jawaria
Muhammad Zohaib, Malik
Haider, Zarrar
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published Version
Abstract
Machine learning and digital image processing are hot emerging technologies which have garnered a lot attention for the past few decades because of promising results in multi fields like electronics, medical, communication and vision. Deep learning which is subclass, has been used for the classification for the natural images has gained a lot of research interest among biomedical researchers for better estimation of medical diagnosis. In this research paper, we developed and implemented a deep learning algorithms with image processing for detection of breast cancer in the early stage using by utilization of the algorithms in X-ray mammograms images during diagnosis. The information parameters include background tissues, abnormality, severity of classes, and etc. The algorithm utilized neural network for the model training and implementation performed in LabVIEW NI vision toolkit. The proposed algorithm performed better results and promising with excessive training of the images. Finally, we proposed novel research challenges and open problems in this area which requires further research and investigation by researchers.
Description
Keywords
Classification , Deep learning , Digital image processing , LabVIEW , Machine learning , Neural network , Artificial Neural Network , Algorithms design
Citation
Haider, N., Mazhar, I. J., Haider, S., Ghazanfar, J., Zohaib, M. M. and Haider, Z. (2025) 'Advance ANN algorithm and research challenges for breast cancer detection and classification with evaluation Ni LabVIEW', 2025 6th International Conference on Artificial Intelligence, Robotics and Control (AIRC), Savannah, GA, USA, 7-9 April 2025, pp. 342-348. https://doi.org/10.1109/AIRC64931.2025.11077485
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