CoolFace
Apppublic

aicolab25/COVID-19_Radiography_Database

sourceHugging Faceupdated 16d agoView on Hugging Face
0likes
App README

The dataset for this project is downloaded from Kaggle - 'COVID-19 Radiography Database'. Available at: https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database.

This is an experimental multi-class classification model, based on U-NET architecture, to help medical practioners in diagnosis of a lung disease, namely,

  1. 1.Viral Pneumonia
  2. 2.COVID
  3. 3.Lung Opacity (that indicates a disease in the lung without specifying which one)
  4. 4.Normal

Notwithstanding the hardware restrictions - the model produces acceptable values for Precision, Recall and F1-score, although not accurate enough to be used in a medical setting yet.

<font color='Maroon'> Steps for installaton</font>

<font color='Teal'> [A] Setting up a VSCode workspace on Mac</font>

Required Python version - 3.12.12

  1. 1.Create a virtual environment with Python 3.12.12
  2. 2.VSCode prompts installing libraries from 'requirements.txt' - install all dependencies.
  3. 3.!! IMP !! Do not change the Tensorflow version.

Tensorflow 2.18.1 is compatible with Python 3.12.12.

Tensorflow-metal is essential for GPU acceleration on Apple Silicon chips (M1,M2,M3,M4).

  1. 1.Optionally, install "macmon" with Homebrew to monitor GPU and CPU utilization
  2. 2.Go to ../pneumonia_detection/notebooks/

a) Configure 'config.json' with the local path to your directory

b) Execute pneumonia-detection.ipynb notebook

  1. 1.Dataset will be downloaded from Kaggle into folder ../pneumonia_detection/data/

Alternatively, export 'pneumonia-detection.ipynb' into a python script and run this script in a Terminal.

<font color='Teal'> [B] Setting up VSCode workspace on Windows 11 system with NVIDIA hardware (GPU) acceleration</font>

Setting up AI environment on Windows is highly dependent on the hardware. The given steps are compatible with below hardware specifications. OS: Windows 11 GPU: NVIDIA GeForce RTX 5090 Driver: NVIDIA Studio Driver

Required Python version - 3.10.8

  1. 1.Create a virtual environment with Python 3.10.8
  1. 1.The dependencies for GPU acceleration:

a) CUDA version 11.5 (for Windows 10 - 11.0) b) Tensorflow version 2.10.0 is compatible with the specifies python version c) Optionally: numpy<1.24, protobuf==3.19.6, tensorflow-datasets==4.6.0

  1. 1.Go to ../pneumonia_detection/notebooks/

a) Configure 'config.json' with the local path to your directory

b) Execute pneumonia-detection.ipynb notebook

  1. 1.Dataset will be downloaded from Kaggle into folder ../pneumonia_detection/data/

Alternatively, export 'pneumonia-detection.ipynb' into a python script and run this script in PowerShell. Monitor CPU and GPU utilization on Task Manager.

<font color='Maroon'> Project directory structure</font>

<font color='Teal'> |-- data </font>

This is where the dataset from Kaggle will be downloaded.

<font color='Teal'> |-- models</font>

Contains all logs and model history - populated during training and retrieved for evaluating performance metrics.

<font color='Teal'> |-- notebooks</font>

Main Jupyter notebooks -

  • —pneumonia-detection.ipynb,
  • —performance_metrics.ipynb

Downsampling was performed in below notebooks to achieve practical trade-off between model performance and resource allocation -

  • —pneumonia-detection_downscaled.ipynb
  • —performancemetricsdownscaled.ipynb

Jupyter notebooks demonstrating -

i. Describing the problem statement ii. Exploratory data analysis iii. Splitting the dataset iv. Model training v. Evaluating with Test dataset vi. Performance metrics

<font color='Teal'> |-- scripts</font>

Custom python packages tailored for handling dataset 'COVID-19 Radiography Database'.