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A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images

  • S. Satheesh Kumar , Beena Suresh Gaikwad , Meram Munirathnam and B. Antline Nisha
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Machine Learning in Healthcare
This chapter is in the book Machine Learning in Healthcare

Abstract

Globally, cervical cancer is a cause of serious health concern, especially in less developed countries with a lack of frequent screening and specialized diagnosis. Although there has been advancement in the automated detection of cervical cancer due to the developments in deep learning over the recent past, many of the existing models suffer from poor feature representation, poor interpretability, and poor performance on class imbalance datasets. Particularly, these models tend to overlook inter-class similarity and high-resolution cellular differences and end up with low diagnostic reliability and generalizability. We introduce an innovative architecture named hierarchical cross-fusion feature extraction network (HCFEN) to address these issues. HCFEN is designed to improve contextual and morphological features in images of cervical cytology. HCFEN achieves powerful, multi-level features by combining cross-attention blocks, hierarchical fusion blocks, and a dual-branch convolutional neural network backbone. For minimizing dataset imbalance and improved minority class identification, a class-aware loss function is also utilized. Herlev dataset was used to evaluate the model proposed. For Herlev, HCFEN recorded F1-score: 98.5%, sensitivity: 98.2%, specificity: 99.0%, and accuracy: 98.7%. These facts establish that HCFEN provides an extremely accurate, robust, and interpretable model for computer-aided cervical cancer detection.

Abstract

Globally, cervical cancer is a cause of serious health concern, especially in less developed countries with a lack of frequent screening and specialized diagnosis. Although there has been advancement in the automated detection of cervical cancer due to the developments in deep learning over the recent past, many of the existing models suffer from poor feature representation, poor interpretability, and poor performance on class imbalance datasets. Particularly, these models tend to overlook inter-class similarity and high-resolution cellular differences and end up with low diagnostic reliability and generalizability. We introduce an innovative architecture named hierarchical cross-fusion feature extraction network (HCFEN) to address these issues. HCFEN is designed to improve contextual and morphological features in images of cervical cytology. HCFEN achieves powerful, multi-level features by combining cross-attention blocks, hierarchical fusion blocks, and a dual-branch convolutional neural network backbone. For minimizing dataset imbalance and improved minority class identification, a class-aware loss function is also utilized. Herlev dataset was used to evaluate the model proposed. For Herlev, HCFEN recorded F1-score: 98.5%, sensitivity: 98.2%, specificity: 99.0%, and accuracy: 98.7%. These facts establish that HCFEN provides an extremely accurate, robust, and interpretable model for computer-aided cervical cancer detection.

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
  4. DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
  5. Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
  6. A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
  7. Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
  8. Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
  9. Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
  10. EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
  11. Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
  12. Machine Learning Techniques for Healthcare 193
  13. Applications and Benefits of Machine Learning in Healthcare 215
  14. Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
  15. Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
  16. Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
  17. MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
  18. On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
  19. Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
  20. AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
  21. A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
  22. Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
  23. Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
  24. Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
  25. MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
  26. Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
  27. Index
https://doi.org/10.1515/9783111541235-019

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
  4. DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
  5. Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
  6. A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
  7. Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
  8. Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
  9. Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
  10. EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
  11. Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
  12. Machine Learning Techniques for Healthcare 193
  13. Applications and Benefits of Machine Learning in Healthcare 215
  14. Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
  15. Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
  16. Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
  17. MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
  18. On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
  19. Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
  20. AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
  21. A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
  22. Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
  23. Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
  24. Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
  25. MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
  26. Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
  27. Index
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