# syed-md-abdud-dayan/pneumonia-detection architecture

> Architecture diagram of the GitHub repository syed-md-abdud-dayan/pneumonia-detection, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-25). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/syed-md-abdud-dayan/pneumonia-detection
- Repository: https://github.com/syed-md-abdud-dayan/pneumonia-detection

## Overview

This repository documents a notebook-based chest X-ray classification workflow: load and inspect image data, preprocess and augment it, train a VGG16-based binary classifier, then evaluate and report results on held-out test data. The README describes the workflow and model, but no notebook source was available to verify internal calls or implementation details; the edges below therefore reflect only documented data flow and operations.

Main entry points: [`Pneumonia Detection.ipynb`](https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb) (Pneumonia workflow), [`Pneumonia_Detection_Images`](https://github.com/syed-md-abdud-dayan/pneumonia-detection/tree/main/Pneumonia_Detection_Images) (Saved visualizations)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_data["Data analysis"]
  node_inspect["Dataset inspection"]
  node_eda["Class and image EDA"]
end

subgraph group_prep["Image preparation"]
  node_preprocess["Image preprocessing"]
  node_augment["Training augmentation"]
  node_weights["Class weighting"]
end

subgraph group_model["Model training"]
  node_vgg["VGG16 classifier"]
  node_training["Model training"]
  node_callbacks["Training callbacks"]
  node_checkpoint["Best model"]
end

subgraph group_eval["Evaluation"]
  node_test["Independent test set"]
  node_metrics["Evaluation metrics"]
  node_reports["Evaluation reports"]
  node_visuals["Saved visualizations"]
end

node_user(("Notebook user"))
node_drive[("Google Drive data")]
node_notebook["Pneumonia workflow"]

node_user -->|"runs"| node_notebook
node_notebook -->|"loads data"| node_drive
node_notebook -->|"inspects"| node_inspect
node_notebook -->|"analyzes"| node_eda
node_drive -->|"provides images"| node_preprocess
node_preprocess -->|"prepares training"| node_augment
node_preprocess -->|"provides inputs"| node_training
node_notebook -->|"calculates"| node_weights
node_weights -->|"weights classes"| node_training
node_notebook -->|"builds"| node_vgg
node_vgg -->|"provides model"| node_training
node_training -->|"uses"| node_callbacks
node_training -->|"saves best"| node_checkpoint
node_checkpoint -->|"evaluated on"| node_test
node_test -->|"supplies predictions"| node_metrics
node_metrics -->|"summarized as"| node_reports
node_notebook -->|"produces plots"| node_visuals

click node_notebook "https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb"
click node_inspect "https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb"
click node_eda "https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb"
click node_preprocess "https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb"
click node_augment "https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb"
click node_weights "https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb"
click node_vgg "https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb"
click node_training "https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb"
click node_callbacks "https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb"
click node_checkpoint "https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb"
click node_test "https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb"
click node_metrics "https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb"
click node_reports "https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb"
click node_visuals "https://github.com/syed-md-abdud-dayan/pneumonia-detection/tree/main/Pneumonia_Detection_Images"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_inspect,node_eda,node_user toneBlue
class node_preprocess,node_augment,node_weights,node_drive toneAmber
class node_vgg,node_training,node_callbacks,node_checkpoint toneMint
class node_test,node_metrics,node_reports,node_visuals toneRose
class node_notebook toneTeal
```

## Components

### Data analysis

- [Dataset inspection](https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb): `Pneumonia Detection.ipynb`
- [Class and image EDA](https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb): `Pneumonia Detection.ipynb`

### Image preparation

- [Image preprocessing](https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb): `Pneumonia Detection.ipynb`
- [Training augmentation](https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb): `Pneumonia Detection.ipynb`
- [Class weighting](https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb): `Pneumonia Detection.ipynb`

### Model training

- [VGG16 classifier](https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb): `Pneumonia Detection.ipynb`
- [Model training](https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb): `Pneumonia Detection.ipynb`
- [Training callbacks](https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb): `Pneumonia Detection.ipynb`
- [Best model](https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb): `Pneumonia Detection.ipynb`

### Evaluation

- [Independent test set](https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb): `Pneumonia Detection.ipynb`
- [Evaluation metrics](https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb): `Pneumonia Detection.ipynb`
- [Evaluation reports](https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb): `Pneumonia Detection.ipynb`
- [Saved visualizations](https://github.com/syed-md-abdud-dayan/pneumonia-detection/tree/main/Pneumonia_Detection_Images): `Pneumonia_Detection_Images`

### Other components

- **Notebook user**
- **Google Drive data**
- [Pneumonia workflow](https://github.com/syed-md-abdud-dayan/pneumonia-detection/blob/main/Pneumonia%20Detection.ipynb): `Pneumonia Detection.ipynb`

## Connections

- Notebook user → Pneumonia workflow: runs
- Pneumonia workflow → Google Drive data: loads data
- Pneumonia workflow → Dataset inspection: inspects
- Pneumonia workflow → Class and image EDA: analyzes
- Google Drive data → Image preprocessing: provides images
- Image preprocessing → Training augmentation: prepares training
- Image preprocessing → Model training: provides inputs
- Pneumonia workflow → Class weighting: calculates
- Class weighting → Model training: weights classes
- Pneumonia workflow → VGG16 classifier: builds
- VGG16 classifier → Model training: provides model
- Model training → Training callbacks: uses
- Model training → Best model: saves best
- Best model → Independent test set: evaluated on
- Independent test set → Evaluation metrics: supplies predictions
- Evaluation metrics → Evaluation reports: summarized as
- Pneumonia workflow → Saved visualizations: produces plots