Segmentation and Classification of Pap Smear Images for Cervical Cancer Detection Using Deep Learning
arXiv:2508.17728v2 Announce Type: replace-cross Abstract: Cervical cancer remains a significant global health concern and a leading cause of cancer-related deaths among women. Early detection through Pap smear tests is essential to reduce mortality rates; however, the manual examination is time consuming and prone to human error. This study proposes a deep learning framework that integrates U-Net for segmentation and a classification model to enhance diagnostic performance. The Herlev Pap Smear Dataset, a publicly available cervical cell dataset, was utilized for training and evaluation. The i
The increasing maturity of deep learning techniques, especially in computer vision, now allows for practical applications in medical image analysis, coinciding with a global push for more efficient healthcare diagnostics.
This development can significantly improve the accuracy and efficiency of cervical cancer screening, reducing human error and enabling earlier detection, which is critical for patient outcomes and healthcare system loads.
Pap smear analysis can transition from a predominantly manual, labor-intensive process to an AI-assisted workflow, potentially making screening more accessible and standardized globally.
- · AI healthcare diagnostic companies
- · Patients in underserved areas
- · Medical imaging hardware manufacturers
- · Public health organizations
- · Traditional cytology labs relying solely on manual review
- · Companies offering outdated diagnostic tools
Reduced mortality rates from cervical cancer due to earlier and more accurate diagnosis.
Increased adoption of AI in other medical image analysis fields, accelerating the digital transformation of pathology.
Potential for new diagnostic models that integrate various 'omics' data with imaging for highly personalized disease prediction and prevention strategies.
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