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Targeted Chemotherapy with Personalized Immunotherapy

An AI Approach
Edited by Abhishek Kumar, Prasenjit Das, Pramod Singh Rathore, Sachin Ahuja, and Chetan Sharma
Copyright: 2025   |   Expected Pub Date:2025//
ISBN: 9781394270583  |  Hardcover  |  
530 pages
Price: $225 USD
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One Line Description
Targeted Chemotherapy with Personalized Immunotherapy: An AI Approach is an essential guide for healthcare teams, offering groundbreaking insights into novel immunotherapies and personalized treatments to improve cancer patient care and quality of life.

Audience
Cancer researchers, oncologists, data scientists, biotechnology and pharmaceutical professionals, and policymakers exploring AI-driven approaches for cancer treatment

Description
In the last 20 years, there have been significant leaps forward in the treatment of cancer. We now have a far better understanding of how our cells interact with one another, how cancer suppresses and hides from the immune system, and how to support the body in reacting to stop the spread of cancer. Nevertheless, there is still a great deal more to learn in this field. Researchers are working to develop methods that will help pinpoint the most effective treatment for patients. Through this research, they have discovered that, for certain patients, the best results may be reached by combining precisely targeted chemotherapy with personalized immunotherapy.
Instead of treating patients with medications that are detrimental to the body as a whole, researchers now aim to identify the molecules that play an essential part in the communication that takes place between cells. This study will help pave the way for the development of novel immunotherapies that will help the body in its fight against cancer. In order to accurately plan cancer treatment, participation from a number of different members of the healthcare team is essential. This book is a comprehensive guide for all members of this team, providing insights into groundbreaking new treatments to cure more patients and improve quality of life.

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Author / Editor Details
Abhishek Kumar, PhD is an Assistant Director and Professor in the Computer Science and Engineering Department at Chandigarh University with over 13 years of teaching experience. He has published over 170 peer-reviewed papers, authored seven books, edited over 50 volumes, and holds a patent. His research interests span artificial intelligence, renewable energy systems, image processing, and data mining.

Prasenjit Das, PhD is the Head of AI Enablement and Training for the Netsmartz Group in Chandigarh, India, with over 20 years of teaching and industry experience. He is a Senior member of the Institute of Electrical and Electronics Engineers and has filed 18 patents, four of which have been granted. His research focuses on image processing, machine learning, sentimental analysis, and computer vision. He has filed 18 Patents and holds 4 granted patents to his credit.

Pramod Singh Rathore, PhD is an Assistant Professor in the Department of Computer and Communication Engineering at Manipal University Jaipur with over 12 years of teaching experience. He has published over 85 papers in peer-reviewed national and international journals, books, and conferences. His research interests include networking, image processing, and machine learning.

Sachin Ahuja, PhD has an illustrious academic and research career, marked by numerous impactful contributions. An accomplished editor, he has contributed to numerous high-quality academic books and served as a guest editor for special issues in reputed international journals, showcasing his expertise in emerging research domains. Additionally, he has successfully led several funded projects in advanced areas, including artificial intelligence, machine learning, and data mining, driving innovation and practical solutions.

Chetan Sharma works at the PhysicsWallah Institute of Innovation, where he leads multiple schools, including Technology, Management, and Healthcare, where he showcases his 18 years of experience. He has authored one book and over 70 research publications in journals and conferences, and holds over 35 filed patents, with several granted by the Indian Patent Office. His research interests span artificial intelligence, natural language processing, IoT, cybersecurity, and green technologies.

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Table of Contents
Foreword
Preface
1. Assessing Predictive Accuracy: Model Validation in Cancer Diagnostics
M. Sudha, Arun Elias, G. Gurumoorthy, S. Rajalakshmi and S. K. Muthusundar
1.1 Introduction
1.1.1 Conventional Cancer Diagnosis
1.1.2 Machine Learning in Cancer Diagnosis
1.1.3 Types of Cancer in Focus
1.1.3.1 Breast Cancer
1.1.3.2 Lung Cancer
1.1.3.3 Skin Cancer
1.1.4 Objectives of Study
1.1.5 Study Scope
1.1.6 Performance Metrics
1.1.7 Limitations and Future Directions
1.2 Literature Review
1.3 Methodology
1.3.1 Data Acquisition
1.3.2 Data Preprocessing
1.3.2.1 Dealing with Missing Data
1.3.2.2 Normalization and Standardization
1.3.2.3 Feature Selection and Dimensionality Reduction
1.3.3 Machine Learning Models
1.3.3.1 Support Vector Machine (SVM)
1.3.3.2 Random Forest (RF)
1.3.3.3 k-Nearest Neighbors (k-NN)
1.3.3.4 Logistic Regression (LR)
1.3.3.5 Hyperparameter Tuning
1.3.4 Performance Metrics
1.4 Analysis of Results
1.4.1 The Overall Performance of Each Model on the Breast Cancer Dataset
1.4.2 Models’ Performance for Lung Cancer Dataset
1.4.3 Model Performance on Skin Cancer Dataset
1.4.4 Analysis of Inter-Cancer Type Performance Comparison
1.5 Discussion of Results
1.6 Conclusion
References
2. Applying Transfer Learning to Accelerate Cancer Classification and Prediction
T. Ravi, Shashidhar Gurav, Nandhini, Vijayaraj and S. K. Muthusundar
2.1 Introduction
2.1.1 Background on Cancer Classification
2.1.2 Transfer Learning in Medical Imaging
2.1.3 Model Development
2.2 Literature Review
2.2.1 Application of Transfer Learning in Breast Cancer Diagnosis
2.3 Methodology
2.3.1 Introduction
2.3.2 Data Preparation
2.3.2.1 Data Source
2.3.2.2 Data Collection
2.3.2.3 Data Preprocessing
2.3.2.4 Normalization
2.3.2.5 Handling Missing Values
2.3.2.6 Feature Selection
2.3.2.7 Data Partitioning
2.3.3 Model Design
2.3.3.1 Transfer Learning Approach
2.3.4 Implementation Tools
2.4 Results
2.4.1 Data Distribution
2.4.2 Accuracy, Precision, Recall, and F1-Score
2.4.3 Confusion Matrix
2.4.4 ROC Curve Analysis
2.4.5 Comparison on Traditional Machine Learning Models
2.5 Discussion of Results
2.5.1 Model Strengths
2.5.2 Areas for Improvement
2.6 Conclusion
References
3. Artificial Intelligence in Cancer Screening: Innovations in Early Detection
Arun Elias, V. Vaithianathan, S.K. Rajesh Kanna, G.M. Raja and S.K. Muthusundar
3.1 Introduction
3.1.1 Background on Cancer Screening
3.1.2 Role of Artificial Intelligence
3.1.3 Research Methodology
3.1.4 AI in Medical Imaging
3.1.5 Challenges and Ethical Considerations
3.2 Literature Review
3.3 Methodology
3.3.1 Dataset Collection
3.3.2 Data Preprocessing
3.3.3 Architecture Model Design
3.3.4 Training and Validation
3.3.5 Metrics to Measure
3.4 Results
3.4.1 Model Performance Metrics
3.4.2 Confusion Matrix Analysis
3.4.3 Receiver Operating Characteristic Curve
3.4.4 Comparison with Existing Models
3.4.5 Error Analysis
3.5 Future Directions
3.6 Conclusion
References
4. Comprehensive Approaches to Survival Analysis and Prognostic Modeling in Cancer Research: Integrating Statistical Techniques, and Clinical Variables
B. Sriman, J. Maria Arockia Dass, R. Seetha and Ashish Kumar
4.1 Introduction
4.1.1 Objectives
4.2 Literature Review
4.3 Methodology
4.3.1 Collection and Preprocessing
4.3.2 Cox Proportional Hazards Model
4.3.3 Random Survival Forest (RSF)
4.3.4 DeepSurv: Neural Network-Based Survival Model
4.3.5 Model Evaluation and Comparison
4.4 Results
4.4.1 General Comparison of Ability
4.4.2 Results of Cox Proportional Hazards (CPH) Model
4.4.3 RSF Results
4.4.4 Results on DeepSurv
4.4.5 Model Comparison and Discussion
4.4.6 Impact on Personalized Medicine
4.5 Conclusion
References
5. Exploring Cancer Therapeutics: A Collection of Case Studies
L. Selvam, Annie Silviya S. H., Singaravelan M. and Ira Aditi
5.1 Introduction
5.1.1 Conventional Cancer Therapies: Limitations and Challenges
5.1.2 The New Era of Targeted Therapies
5.1.3 Immunotherapy
5.1.4 Case Study: Targeted Therapy in HER2-Positive Breast Cancer
5.1.5 Case Study: Immunotherapy in Advanced Melanoma
5.2 Literature Review
5.3 Methodology
5.3.1 Research Design
5.3.2 Patient Selection
5.3.2.1 Case Study: HER2-Positive Breast Cancer (Trastuzumab)
5.3.2.2 Advanced Melanoma Case Study (Pembrolizumab)
5.3.3 Treatment Protocols
5.3.3.1 Trastuzumab Protocol for HER2-Positive Breast Cancer
5.3.4 Data Collection
5.3.4.1 Clinical and Imaging Data
5.3.4.2 Immune and Genetic Markers
5.3.5 Statistical Analysis
5.4 Results
5.4.1 Tumor Response
5.4.2 Survival Analysis
5.4.3 Recurrence Rate and Disease Control
5.4.4 Immune-Related Adverse Events and Safety Profile
5.5 Conclusion
References
6. Predicting Cancer Outcomes Using Transfer Learning: Harnessing Pre-Trained Models and Cross-Domain Knowledge for Enhanced Prognosis and Personalized Treatment Strategies
R. Ramachandran, V. Vaissnave, Vijayaraj and S. K. Muthusundar
6.1 Introduction
6.1.1 Background
6.1.2 Objectives
6.2 Literature Review
6.3 Methodology
6.3.1 Data Collection
6.3.2 Preprocessing the Data
6.3.3 Modeling
6.3.4 Model Assessment
6.3.5 Implementation of the Integrated Model
6.4 Results
6.4.1 Model Performance Metrics
6.4.2 Baseline Model Comparisons
6.4.3 Feature Importance Analysis
6.4.4 Clinical Validation Results
6.5 Conclusion
References
7. Predicting Cancer Outcomes with RNNs: A Time Series Approach
M. Mahalakshmi, Annie Silviya S. H., Kumud Sachdeva and Rajan Sachdeva
7.1 Introduction
7.1.1 Background
7.1.2 Significance of Ensemble Learning
7.1.3 Objectives
7.1.4 Significance of the Study
7.2 Literature Review
7.3 Methodology
7.3.1 Objective
7.3.2 Data Collection
7.3.2.1 Dataset
7.3.3 Preprocessing
7.3.3.1 Data Drawing
7.3.3.2 Normalization Numerical Features
7.3.3.3 Point Selection
7.3.4 Feature Selection
7.3.5 Ensemble Learning Techniques
7.3.6 Model Evaluation Metrics
7.3.7 Cross-Validation
7.4 Results
7.4.1 Model Performance
7.5 Results
7.5.1 Cross-Validation Results
7.5.2 Model Comparison
7.6 Conclusion
References
8. AI in Cancer Screening and Early Detection
Priya Batta and Soumen Sardar
8.1 Introduction
8.2 Literature Review
8.3 Methodology
8.4 Results
8.5 Conclusion and Future Scope
References
9. Challenges and Limitations of AI in Oncology
Priya Batta
9.1 Introduction
9.2 Literature Review
9.3 Methodology
9.4 Results
9.5 Conclusion and Future Scope
References
10. Predictive Models for Cancer-Related Lymphedema: Enhancing Telerehabilitation and Physiotherapy Management
Madhusmita Jena, Charu Chhabra, Huma Parveen, Sahar Zaidi, Noor Fatima and Habiba Sundus
10.1 Introduction
10.1.1 Prevalence of Lymphedema
10.1.2 Diagnostic Technique for Lymphedema
10.1.3 Commonly Used Scales for Diagnosis of Lymphedema
10.2 Lymphedema’s Impact on Cancer Survivors
10.3 Current Challenges in Lymphedema Management
10.4 Role of AI in Lymphedema Management
10.4.1 Customizing Physiotherapy Regimens Based on AI Predictions
10.4.2 Integrating Telerehabilitation for Effective Lymphedema Management
10.5 Conclusion
References
11. Role of AI in the Prediction of Leukemia and AI-Driven Predictive Models for Rehabilitation Outcomes in Acute Lymphoblastic Leukemia
Huma Parveen, Charu Chhabra, Sahar Zaidi, Noor Fatima, Madhusmita Jena and Amaan Ali Khan
11.1 Acute Lymphoblastic Leukemia
11.2 Importance of Early Prediction and Rehabilitation in ALL
11.3 Role of AI in Healthcare
11.4 AI in Leukemia Prediction
11.5 AI-Driven Predictive Rehabilitation Outcomes in ALL
11.6 Data Privacy and Security in Healthcare Models
11.7 Framework for Protecting Data Privacy
11.7.1 Acts and Policies
11.7.2 National Policies
11.7.3 AI Models-Based Privacy Protection
11.8 Ethical Concerns in AI Healthcare
References
12. Data Privacy and Ethical Challenges in AI-Driven Cancer Care
Firdaus Jawed, Rabia Aziz, Sumbul Ansari, Shahnawaz Anwar and Sohrab Ahmad Khan
12.1 Introduction to Data Privacy and Ethics in AI-Driven Cancer Care
12.2 Types of Sensitive Data in AI-Driven Cancer Care
12.3 Ethical Frameworks and Guidelines for Data Privacy
12.4 Data Security and Protection Techniques
12.5 Bias, Fairness, and Algorithmic Transparency in AI-Driven Cancer Care
12.6 Regulatory and Compliance Challenges
12.7 Emerging Technologies and Innovations in Privacy
12.8 Future Directions in Ethical AI for Cancer Care
12.9 Conclusions
References
13. Cancer Rehabilitation in the Era of Targeted Chemotherapy and Personalized Immunotherapy
Rabia Aziz, Firdaus Jawed, Sumbul Ansari, Shahnawaz Anwar and Sohrab Ahmad Khan
13.1 Evolving Landscape of Cancer Treatment
13.2 Importance of Cancer Rehabilitation
13.3 Integrating Rehabilitation Into AI-Powered Cancer Rehabilitation
13.3.1 The Role of Data in Rehabilitation
13.3.2 Machine Learning and Predictive Analytics
13.3.3 Real-Time Monitoring and Feedback
13.3.4 Outcomes Measurement and Continuous Improvement
13.3.5 The Rationale for Integration
13.3.6 Utilizing Biomarkers in Rehabilitation
13.3.7 Multidisciplinary Collaboration
13.3.8 Early Intervention Strategies
13.3.9 Leveraging Technology for Monitoring and Feedback
13.4 Leveraging Data Analytics and AI for Adaptive Rehabilitation
13.4.1 The Role of Data Analytics in Rehabilitation
13.4.2 AI-Driven Personalization of Rehabilitation Programs
13.4.3 Integration of Wearable Technology and Telehealth
13.4.4 Virtual Reality (VR) and Augmented Reality (AR) Applications
13.5 Tailoring Rehabilitation Strategies for Targeted Therapies
13.5.1 Understanding Targeted Therapies and Their Implications
13.5.2 Personalized Assessment and Planning
13.5.3 Integrating Evidence-Based Interventions
13.5.3.1 Physical Therapy
13.5.3.2 Occupational Therapy
13.5.3.3 Psychosocial Support
13.5.3.4 Nutritional Counseling
13.5.4 Utilizing Technology for Enhanced Rehabilitation
13.6 Future Directions and Emerging Trends
13.7 Summary
References
14. Role of AI in Cancer Screening and Its Detection
Muskan, Shweta Sharma, Parul Sharma, Manoj Malik and Jaspreet Kaur
14.1 Introduction
14.2 Cancer Mechanisms and Various Pathologies
14.3 Conventional Methods of Cancer Screening
14.3.1 Mammography
14.3.2 Ultrasound
14.3.3 Magnetic Resonance Imaging
14.3.4 Liquid Biopsies
14.3.5 Pap Smear (Papanicolaou Test)
14.3.6 Barium X-Ray (Barium Swallow or Enema)
14.3.7 Photoacoustic Tomography (PAT)
14.3.8 SPECT (Single-Photon Emission Computed Tomography) and PET (Positron Emission Tomography)
14.4 Overview of AI (Artificial Intelligence) in Cancer Detection
14.5 AI Applications in Cancer Screening Using Deep Learningand Machine Learning
14.5.1 AI Models for Breast Cancer
14.5.2 AI Models for Lung Cancer
14.5.3 AI Models for Skin Cancer
14.5.4 AI Models for Gastric Cancers
14.5.5 AI Models for Prostate Cancers
14.6 Challenges in AI Adoption for Cancer Screening
14.7 Proposed Strategies for AI Implementation for Cancer Detection
14.8 Conclusion
14.9 Future Directions
References
15. Automated 3D U-Net Framework for Brain Tumor Segmentation and Classification with Insights Into AI-Driven Cancer Research Applications
S. Usharani, P. Manju Bala, T. Ananth Kumar and G. Glorindal Selvam
15.1 Introduction
15.2 Literature Review
15.2.1 Brain Tumor MRI Image Segmentation
15.2.1.1 Methods for Manual Segmentation
15.2.1.2 Methods for Partly-Automated Segmentation
15.2.1.3 Methods for Absolutely Automated Segmentation
15.2.2 Brain Tumor MRI Classification
15.3 Materials and Methods
15.3.1 Materials
15.3.2 Methods
15.3.2.1 System Model
15.3.2.2 Multi Scale Feature Extraction Network
15.3.2.3 Incremental Feature Improvement
15.3.2.4 Loss Function
15.4 Experimental Setup
15.4.1 Experimental Analysis
15.5 Conclusion
References
16. Early Prediction of Bone Cancer: Integrating Deep Learning Models
R. Dhinesh, T. Ananth kumar, P. Kanimozhi and Sunday Adeola AJAGBE
16.1 Introduction
16.2 Related Works
16.3 Proposed Methodology
16.4 Results and Discussion
16.5 Conclusion
References
17. Machine Learning Techniques for Predicting Epileptic Seizures: A Data-Driven Analysis Using EEG Signals
Preeti Narooka, Ankit Vishnoi and Jatin Verma
17.1 Introduction
17.1.1 Background
17.1.2 Objective
17.2 Literature Survey
17.2.1 Study 1: Feature Extraction Techniques in EEG-Based Seizure Detection
17.2.2 Study 2: Application of Deep Learning in Neurological Disorders
17.2.3 Study 3: Comparative Analysis of ML Algorithms
17.2.4 Study 4: Transfer Learning in EEG Analysis
17.2.5 Study 5: Real-Time Seizure Prediction Systems
17.2.6 Study 6: Explainable Artificial Intelligence in Seizure Detection
17.2.7 Study 7: Challenges in EEG-Based Seizure Detection
17.2.8 Study 8: Multimodal Learning Approaches
17.3 Methodology
17.3.1 Dataset
17.3.2 Preprocessing
17.3.3 Feature Extraction
17.3.4 Model Architecture
17.4 Results and Discussion
17.4.1 Model Performance
17.4.2 Discussion
17.4.3 Implications for Healthcare Applications
17.5 Conclusion
References
18. Transfer Learning in Cancer Research
Mamta and Nitin
18.1 Definition and Overview of Transfer Learning
18.1.1 Transfer Learning Typically Involves the Following Components
18.1.2 Importance of Transfer Learning in Cancer Research
18.1.3 Challenges in Traditional Cancer Research Approaches
18.2 How Transfer Learning Works
18.2.1 Types of Transfer Learning
18.2.2 Transductive Transfer Learning
18.3 Applications of Transfer Learning in Cancer Research
18.4 Challenges in Transfer Learning for Cancer
18.4.1 Data Scarcity and Domain Adaptation
18.4.2 Model Interpretability
18.5 Future Directions: Personalized Medicine and Drug Discovery
18.5.1 Personalized Medicine: Tailoring Treatment to the Individual
18.6 Drug Discovery: Accelerating the Path to New Therapies
18.7 Challenges and Ethical Considerations
18.8 Conclusion
References
19. Machine Learning Approaches for Early Detection of Cervical Cancer: A Comparative Study of Classification Models
Inam Ul Haq, Janvi Malhotra, Vanshika Rawat, Jyoti Kumari and Gagandeep Kaur
19.1 Introduction
19.2 Literature Review of Some Research Papers
19.3 Methodology
19.4 Results
19.5 Conclusion and Future Scope
References
20. Interactive Data Management for Cancer Care: Leveraging Electronic Health Records and Proteomic Data
M. Rohini, S. Oswalt Manoj, J. P. Ananth and D. Surendran
20.1 Introduction
20.1.1 Need of Electronic Health Record Maintenance
20.1.2 Message Passing Protocol for Cancer EHR Updates
20.1.3 Reliable Messaging for Critical Data
20.1.4 Microservice-Oriented Cancer Data Staging and Deployment
20.2 HER Data Processing
20.2.1 Staging Service
20.2.1.1 Autoscaling Based on Criticality of EHR System
20.2.2 Internal Working of the Staging Service
20.2.2.1 Validate and Fetch Dashboard Details
20.2.2.2 Execute Stored Procedure
20.2.2.3 High-Availability Deployment Phase
20.3 Conclusion
References
21. Artificial Intelligence–Driven Personalized Cancer Treatment
Gurwinder Singh, Sarthak Sharma and Aastha Anand
21.1 Introduction: The Dawn of Artificial Intelligence–Powered Cancer Screening
21.2 Role of AI in Cancer Screening
21.3 Role of AI in Early Detection
21.4 Case Studies and Real-World Implementation
21.5 Benefits and Opportunities
21.6 Conclusion
21.7 Future Scope
References
22. Revolutionizing Breast Cancer Detection: Emerging Trends and Future Technologies
Gurmeet Kaur Saini, Inderdeep Kaur and Kanwaldeep Kaur
22.1 Overview
22.2 Risk Assessment Types
22.3 Risk Elements
22.4 Risk Factors for Hormones and Reproduction
22.5 Additional Risk Factors
22.6 Risk of Breast Cancer Over Time
22.6.1 Risk Assessment by Family History
22.7 Models for Risk Estimate
22.7.1 The Gail Model
22.7.2 Claus–Mammary Carcinoma Risk Assessment Model
22.7.3 The BRCAPRO Model
22.7.4 Tools for Risk Calculation
22.8 Clinical Breast Imaging Techniques
22.8.1 Mammography
22.8.2 Ultrasonic
22.8.3 Magnetic Resonance Imaging
22.9 Measurement Systems and Techniques for Microwave Breast Imaging
22.9.1 Tomography Using Microwaves
22.9.2 Microwave Imaging Using Radar Technology
22.9.3 Breast Cancer Detection Using Biosensors
22.9.4 Use of Thermography to Find Breast Cancer
22.10 Discussion
22.11 Present Developments and Prospects for Breast Cancer Screening Methods
22.12 Conclusion
References
23. Future of Neurological Research: Leveraging Artificial Intelligence for Precision and Discovery
Hemlata and Utsav Krishan Murari
23.1 Introduction
23.2 AI in Neuroimaging: A Revolution in Neurological Research
23.3 Computational Neuroscience and Modeling: Transforming Understanding of Neural Mechanisms through AI
23.4 AI and BCIs: Transforming Accessibility and Real-Time Neural Interaction
23.5 Ethics in the Integration of AI Into Neurological Research
23.6 Conclusion
References
24. Cervical Cancer Detection Using Machine Learning
Saranya. A., S. Ravi, Harsha Latha. P., T. Kalaichelvi and A. Anbarasi
24.1 Introduction
24.1.1 Overview of Medical Image Analysis
24.2 ML Techniques for Cervical Cancer Diagnosis
24.2.1 ML Algorithms
24.2.2 Methodology of ML Classification of Images
24.2.3 Cervical Cancer Image Dataset
24.3 Related Work
24.3.1 Cervical Cancer Detection Using ML
24.4 Findings
24.5 Performance Metrics in ML
24.6 Conclusion
References
25. Deep Learning Techniques–Based Medical Image Segmentation in Cervical Cancer
Saranya. A., S. Ravi, Harsha Latha. P. and T. Kalaichelvi
25.1 Introduction
25.2 Motivation of Computer-Aided Diagnosis
25.3 History of DL in Medical Imaging
25.4 Deep Learning Application of Cervical Cancer
25.5 Cervical Cancer Detection Based on DL Techniques for Medical Image Segmentation
25.5.1 Deep Learning in Image Segmentation
25.5.2 Deep Learning in Classification Task
25.6 Frameworks Used in Detecting Cervical Cancer
25.6.1 Comparison Between DL Segmentation and Classification
25.7 Performance Metrics
25.8 Conclusion
References
Index

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