/DIME

Disease Identification Made Easy

Primary LanguageJupyter Notebook

DIME

Disease Identification Made Easy! Breast Cancer Identification using CNNs.

By Mithesh Ramachandran, Sagarika Raje, Kopal Sharma and Ujwal Shah.

Directory Metadata:

📦 DIME
├─ Codes - (All .py files)
│  â”œâ”€ NN.py (Model Building)
│  â”œâ”€ Preprocessing.py (The preproccessing pipeline)
│  â”œâ”€ Visualization_3d.py (The 3D visualization pipeline)
│  â”œâ”€ generate arrays.py
│  â””─ generate masks.py
├─ DIME APP.mp4 (The app video)
├─ Dime.png (Our product logo)
├─ Images (All images)
│  â”œâ”€ DIME APP 2.png (App)
│  â”œâ”€ DIME APP 3.png (App)
│  â”œâ”€ DIME APP.png (App)
│  â”œâ”€ generate mask.png (generate masks.py output image)
│  â”œâ”€ img1a.png (PCR)
│  â”œâ”€ img1b.png (Non PCR)
│  â”œâ”€ plot3d.png (The 3-D plot)
│  â””─ samplehist.png (Sample Hist - different from sample CT file)
├─ Models
│  └─ final_model.h5 (Only in Google Drive) (Our final trained model)
├─ Notebooks
│  â”œâ”€ DIME.ipynb (The notebook used in this project)
├─ README.md (This file)
├─ Samples
│  â”œâ”€ 1-01 (1) (2).dcm (Sample dicom image)
├─ app.py (The app main file)
├─ requirements.txt (App requirements)
└─ requirements (Notebook requirements)
   â””─ requirements.txt (Notebook requirements)