Introduction to Cervical Cancer PPT.pptx

This is the PPT about Cervical Cancer.

1. By: Khowaja Ashfaque 科瓦吉 Topic: A Deep Learning Approach to Multimodal Domain Adaptation for Cervical Cancer Diagnosis

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3. Tsinghua University of China 自强不息 厚德载物 Opening Report Central South University What is the Cervix? Cervix is a muscular, tunnel-like organ. It's the lower part of uterus, and it connects uterus and vagina. 2024/2/28 3 Research Background

4. Tsinghua University of China 自强不息 厚德载物 Opening Report Central South University Cervical Cancer (CC): Cervical cancer is a type of cancer that occurs in the cells of the cervix . Fourth most common cause of cancer death among women worldwide . Second most common cancer among women , especially in developing countries . With more than 700 mortality daily and estimated by 2030 to be 400,000 annually, of which 90% will occur in developing countries . 2024/2/28 4 Research Background

5. Tsinghua University of China 自强不息 厚德载物 Opening Report Central South University Causes of Cervical Cancer: More than 95% of Cervical Cancer cases are associated with the human papillomavirus (HPV), which is sexually transmitted. Risk Factors Many sexual partners Early sexual activity Smoking A weakened immune system. 2024/2/28 5 Research Background

6. Tsinghua University of China 自强不息 厚德载物 Opening Report Central South University Problem Statement: Cervical cancer is among the leading causes of death around the globe. Deep learning models can help automate the segmentation and classification process. Integrating clinical survey data such as patient age, medical history, and symptoms with imaging data can provide valuable information for diagnosis and treatment planning . 2024/2/28 6 Research Background

7. Tsinghua University of China 自强不息 厚德载物 Opening Report Central South University Segmentation: Segmentation is a crucial step in the analysis of medical images as it enables the localization and extraction of the relevant regions. 2024/2/28 7 Current Research Status Classification: Classification techniques use features extracted from Region of Interest (ROI) to distinguish the colposcopy images into normal and abnormal regions. Image Acquisition Image Processing Image Segmentation Feature Extraction Image Classification

8. Tsinghua University of China 自强不息 厚德载物 Opening Report Central South University Table.1: Different Papers methodologies and results achieved 2024/2/28 8 Current Research Status Paper Method Result T. Zhang et al. Pre-trained densely connected convolutional networks 73.08% Jiménez Gaona , Y et al. Model based on the U-net network plus SVM Segmentation = 80% accuracy Classification = 58% accuracy S. Dash et al. Improved Inception-ResNet-V2 81.24% accuracy Asyhar et al. LSTM algorithm 66% accuracy Yan et al. RNN-2-DT Recall rate and mean average precision are increased by 7.2% and 4.3%, respectively Elakkiya et al. Faster Small-Object Detection Neural Networks (FSOD-GAN) 99% accuracy

9. Tsinghua University of China 自强不息 厚德载物 Opening Report Central South University A deep learning model that effectively segments and classifies cervical cancer using colposcopy imaging dataset. A personalized risk prediction model that incorporates both imaging and clinical data to provide a more individualized approach to cervical cancer diagnosis and treatment. Improvement in the accuracy and efficiency of cervical cancer diagnosis and treatment. 2024/2/28 9 Research Goals

10. Tsinghua University of China 自强不息 厚德载物 Opening Report Central South University 2024/2/28 10 Research Methodology Dataset: We collected dataset from WHO International Agency for Research on Cancer (IARC). There are 913 Images of 200 Patients. They also provided Metadata of all the images and cases.

11. Tsinghua University of China 自强不息 厚德载物 Opening Report Central South University 2024/2/28 11 Research Methodology Dataset: Table.2: Example of c linical survey dataset Patient ID Age HPV First Time Intercourse Smoking Abnormal Vaginal Bleeding Pelvic Pain Dyspareunia Unusual Vaginal Discharge Palpable Lymph Nodes Pelvic Abnormalities Routine Electrolyte Analysis Blood Lipid Tests Calcitonin Cardiac Troponin Urine Analysis Complete Blood Cell Count (CBC) ECG 1 45 Yes 18 Yes No Yes Yes No No Yes Normal Normal 5.2 0.03 Normal Normal Normal 2 32 No 20 No Yes No No Yes Yes No Abnormal Normal 4.9 0.01 Abnormal Normal Abnormal 3 52 Yes 17 Yes Yes Yes Yes Yes No Yes Normal Abnormal 6.1 0.02 Normal Abnormal Normal 4 38 Yes 19 No No No No No No Yes Normal Normal 5.5 0.03 Abnormal Normal Abnormal 5 27 No 21 Yes No Yes Yes No Yes No Abnormal Abnormal 5.3 0.01 Normal Abnormal Normal

12. Tsinghua University of China 自强不息 厚德载物 Opening Report Central South University 2024/2/28 12 Research Methodology Proposed Research Methods and Technical Routes : Data pre-processing Development of the deep learning model Evaluation and optimization of the model Comparison with existing methods Validation of the model Personalized risk prediction model

13. Tsinghua University of China 自强不息 厚德载物 Opening Report Central South University 2024/2/28 13 Expected Outcomes Effective segmentation and classification. Integrating imaging and clinical data diagnosis and treatment. Enhancing the accuracy and efficiency. Evaluating the efficacy of clinical data integration in deep learning for cervical cancer segmentation and classification.

14. 2024/2/28 14 Fall Spring Fall Spring Fall Spring Fall Spring 1 st Year 2 nd Year 3 rd Year 4 th Year Course Work Literature Review Experiment Design Advanced Research Study Experiment Design Experiment and Data Analysis Writing Academic Papers Writing Academic Papers Thesis Writing Thesis Defense

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