Deep Learning-Based Cervical Cancer Detection Using Image Processing Techniques

Author(s)	Prof.Pratima Shinde, Prof.Bhagat Shraddha, Prof. Sorate Shilpa
Country	India
Abstract	Abstract Cervical cancer remains a leading cause of cancer-related mortality among women worldwide, especially in developing regions with limited access to early screening and diagnostic tools. Traditional methods like Pap smears and biopsies are often invasive, resource-intensive, and require specialized equipment and trained personnel. This research presents a deep learning-based image processing framework using Convolutional Neural Networks (CNN), including advanced architectures like VGG19 and MobileNet, to enable accurate, non-invasive, and cost-effective detection of cervical cancer. The proposed system automates the analysis of cervical cell images, reducing diagnostic delays and supporting clinical decision-making. Experimental results using publicly available datasets demonstrate high accuracy, precision, and recall, with MobileNet offering a balance between performance and computational efficiency.
Keywords	Keywords: Cervical Cancer Detection, Deep Learning, Convolutional Neural Networks (CNN), VGG19, MobileNet, Image Processing, Medical Imaging, Non-invasive Diagnosis, Automated Screening, Computer-Aided Diagnosis (CAD), Healthcare AI.
Field	Engineering
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-05-21
DOI	https://doi.org/10.71097/IJSAT.v16.i2.5383
Short DOI	https://doi.org/g9mq8h

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