Deep Learning and Machine Learning Frameworks for Cervical Cancer Screening

Deep Learning and Machine Learning Frameworks for Cervical Cancer Screening presents a comprehensive exploration of computational intelligence techniques applied to digital pathology, automated cytology, and cervical cancer diagnostic systems. As clinical oncology increasingly adopts computer-assisted diagnostic tools, designing resilient algorithm pipelines to evaluate Papanicolaou smears, colposcopy imagery, and histopathological tissue samples has become a critical engineering discipline. This monograph details the mathematical principles, model architectures, and data processing strategies required to build reliable machine learning models for early detection and risk stratification.

The text examines convolutional neural network architectures, transfer learning strategies, feature extraction routines, and hybrid classification algorithms optimized for complex cellular structures. It addresses fundamental technical challenges including class imbalance in clinical datasets, slide artifact filtering, multi-scale image segmentation, and model interpretability. Designed for biomedical engineers, data scientists, and medical imaging software developers, this volume provides rigorous insights into establishing automated screening systems that enhance diagnostic accuracy and operational efficiency in modern healthcare settings.

2. Short Description3. Keywords4. BISAC Subjects

COMPUTERS / Data Science / Data Analytics

5. Thema Subjects

MKD Medical equipment & techniques / Biomedical engineering

UYQ Artificial intelligence & machine learning

MMF Radiology