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Lung Cancer Image Segmentation using Various Image Processing Techniques

Real-world project · AICTE-aligned · AI-graded · Audit-ready certificate

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Milestones

Available mentors

Enrolled students

Core skills

About this project

Lung Cancer Image Segmentation using Various Image Processing Techniques

Milestones

2. Data collection (CT scan images, possibly from Kaggle or open medical datasets)

10 marks 30d

3. Data collection (CT scan images, possibly from Kaggle or open medical datasets)

10 marks 30d

4. Preprocessing (denoising, contrast enhancement)

10 marks 30d

5. Implementing segmentation algorithms (e.g., edge detection, watershed, thresholding)

10 marks 30d

6. Evaluation against annotated ground truth

10 marks 30d

7. Visualization and reporting

10 marks 30d

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Upcoming sessions

Session	Window	Enrolled
Lung Cancer Image Segmentation using Various Image Proces...	11 Jun 2026 to 10 Jun 2028	0

Skills you'll learn

Image Processing (thresholdingfilteringsegmentation) Machine Learning basics (if extended to ML-based segmentation) Python Programming Knowledge of medical imaging standards (DICOM) Data Annotation & Preprocessing

Tools used

Python with OpenCVscikit-imageNumPyMatplotlib MATLAB (for academic use) DICOM viewers (3D SlicerRadiAnt) Jupyter Notebook

Prerequisites

Understanding of digital image processing Basic knowledge of anatomy (lungstumors) Programming skills in Python or MATLAB

Available mentors

Priyang Kumar

Free

You'll earn — Certificate (PDF)

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