Title of Project: Breast Cancer Classification and Detection through Advanced Pre-processing and Deep learning Techniques

Team Members:

1. Sajal Bhilatia (2100290100143, CSE-C)
2. Rachit Verma (2100290100124, CSE-B)
3. Harsh Rastogi (2200290109006, CSE-A)

Steps for Execution:

1. Clone the repository (git clone https://github.com/cse-kiet/PCSE25-55.git)
2. Set up the Python environment (Use Python 3.8+ (preferably in a virtual environment))
3. Install dependencies (pip install -r requirements.txt)
4. Download and organize the dataset (Place mammogram images under Dataset/Benign/ and Dataset/Malignant/)
5. Run image preprocessing (Execute preprocessing.ipynb or preprocess.py to: Correct orientation Remove pectoral muscle Apply CLAHE Remove noise and segment ROI)
6. Train models (Open and run train_models.ipynb to train VGG16, ResNet50, MobileNet, and Custom CNN)
7. Evaluate results (Use evaluation.ipynb to compare model performance using accuracy, precision, recall, F1-score) DATASET USED- https://www.kaggle.com/datasets/awsaf49/cbis-ddsm-breast-cancer-image-dataset

Checklist:

1. Final Project Report
2. Certificate VII Semester (Dated: December 2024).
3. Certificate VIII Semester (Dated: May 2025).
4. Synopsis
5. Final Presentation
6. Source Code
7. Database dump (.sql file)
8. If a web project, then a Docker file for deployment
9. README (This file)