Lung_Cancer_Detection_Presentation.pptx
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Embark on an insightful journey into the realms of medical research with our detailed explanation of the groundbreaking paper on lung cancer detection. This video meticulously breaks down the methodologies and findings of authors in their influential work, providing a clearer understanding of the complex technology used to fight the world's leading cause of cancer-related deaths.
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- 1. Lung Cancer Detection and Classification using CT Scan Image Processing Presented by: Keerthana Parsa
- 2. Background • Lung cancer is the leading cause of cancer deaths • High mortality rates as it's often diagnosed late when harder to treat • CT screening allows early detection - improves survival rates • But CT interpretation requires expertise; prone to human error • Authors aimed to develop automated detection system to address limitations
- 3. Objective • The authors' goal is to develop a computer-aided diagnosis system using CT scans that can automatically detect and classify tumors without human input.
- 4. Authors’ Proposed Approach • Apply image processing and machine learning to detect and classify tumors from CT scans • Goal: Automatically identify lung tumors and determine if benign or malignant
- 5. Dataset • Authors collected 168 CT scans from The Cancer Imaging Archive • Contains multiple confirmed cases of lung tumors • Both benign and malignant cases • Variety of patients
- 6. Technical Pipeline • Image Preprocessing: • Enhance images to improve quality • Includes noise reduction, contrast adjustments • Segmentation: • Isolate tumor region from rest of lung tissue • Uses thresholding and edge detection techniques • Feature Extraction: • Quantify shape attributes like area, circularity • Captures tumor morphology • Classification with Supervised ML: • Support Vector Machine (SVM) • Train on sample features, predict test cases
- 8. Preprocessing Results • Applied Median Filtering [3x3] to reduce noise • Resized images to uniform 256 x 256 pixels • Increased contrast to reveal tumor boundaries better
- 10. Tumor Segmentation Outcomes • Otsu’s thresholding to generate binary mask • Detected edges and cleared borders • Isolated contiguous tumor region from lung tissue
- 11. Feature Extraction • Computed geometric attributes: • Area, perimeter, eccentricity • Compactness, circularity • Describe tumor shape and morphology
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- 14. Reported Results • Achieved 85% accuracy for tumor detection and classification tasks • Compared performance to recent state-of-the-art techniques • 3% and 5% improvement over previous works • Radiologists qualitatively validated predictions • Confirmed solid detection and categorization performance
- 15. Conclusion & Future Work • Proposed approach shows strong potential • Reasonably high accuracy obtained • Scope for further optimizations and enhancements