---
license: mit
language:
- en
datasets:
- mimic-cxr
- padchest
- chexpert
- vindr-cxr
- brax
- nih-chest-xrays
metrics:
- f1
- accuracy
- mape
- mae
- rmse
pipeline_tag: image-classification
library_name: keras
tags:
- medical
- radiology
- cardiology
- cardiomegaly
- chest-xray
- cardiothoracic-ratio
- segmentation
- object-detection
- radiomics
- explainable-ai
- teknofest
model-index:
- name: NeuroMed-Cardio-0.8B
  results:
  - task:
      type: image-classification
      name: Cardiomegaly Detection
    dataset:
      name: NIH ChestX-Ray14 (External Test)
      type: nih-chest-xrays
    metrics:
    - type: f1
      value: 0.853
      name: F1 Score
    - type: accuracy
      value: 0.84
      name: Accuracy
  - task:
      type: image-classification
      name: CTR Regression (Ensemble)
    dataset:
      name: NIH ChestX-Ray14 (External Test)
      type: nih-chest-xrays
    metrics:
    - type: mape
      value: 0.0502
      name: MAPE
    - type: mae
      value: 0.02
      name: MAE
    - type: rmse
      value: 0.03
      name: RMSE
---

# NeuroMed-Cardio-0.8B

A multi-component AI system for **cardiomegaly detection** from chest X-ray images. The system combines segmentation, object detection, radiomics feature extraction, and an artificial neural network to produce a single final prediction.

![unnamed](https://cdn-uploads.huggingface.co/production/uploads/684ffdf517ebbc34153de81b/3LwI3WpbE2bkmcihqHQ_w.jpeg)

> **Clinical Context:** A cardiothoracic ratio (CTR) ≥ 0.5 on a chest X-ray is the standard clinical threshold for cardiomegaly — an early indicator of heart failure.

---

## System Architecture

The pipeline runs through two parallel branches. All outputs are fused inside a final ANN for the binary cardiomegaly prediction.

```
Chest X-Ray
      │
      ▼
[Preprocessing]
Gamma Correction → Gaussian Blur → CLAHE
      │
      ├──────────────────────────────────┐
      │                                  │
      ▼                                  ▼
[Segmentation Branch]            [YOLOv11 Branch]
U-Net++ (Heart)                  Heart + Lung
DeepLabV3+ (Lungs)               Bounding Box Detection
      │                                  │
      ├── Segmentation CTR          YOLO CTR
      ├── Heart/Lung Area Ratio          │
      ├── Heart Left–Right Width         │
      └── Radiomics (55 features)        │
            │                            │
            └──────────┬─────────────────┘
                       ▼
                 Ensemble CTR
              (YOLO 95% + Segmentation 5%)
                       │
                       ▼
                [ANN — 63 Features]
                512 → 256 → 128 neurons
                       │
                       ▼
             Cardiomegaly Prediction
```

---

## Preprocessing

Raw X-ray images vary significantly across different scanner devices — contrast imbalances and sensor noise directly affect model performance. A three-stage preprocessing pipeline is applied before any model sees the image:

| Technique | What It Does |
|---|---|
| **Gamma Correction** | Reveals cardiac tissue and vascular boundary details hidden in dark regions |
| **Gaussian Blur** | Suppresses pixel-level sensor noise and smooths edges for cleaner detection |
| **CLAHE** | Locally enhances contrast to bring out fine anatomical structures |

All images are normalized to `256×256` pixels.

---

## Segmentation Models

### U-Net++ — Heart Segmentation

Predicts the heart mask at the pixel level.

- **Architecture:** U-Net++ with nested skip connections (detail-preserving)
- **Parameters:** ~8 million
- **Performance:** 89% IoU score
- **Regularization:** BatchNormalization + Dropout (prevents overfitting)
- **Output:** Binary mask isolating the heart region

### DeepLabV3+ — Lung Segmentation

Produces separate masks for the left and right lungs.

- **Backbone:** ResNet101
- **Key Component:** ASPP (Atrous Spatial Pyramid Pooling) — captures multi-scale contextual features simultaneously
- **Mechanism:** Atrous (dilated) convolution captures wide receptive fields without losing fine boundary detail
- **Output:** Separate masks for left lung and right lung

---

## YOLOv11 Branch — Object Detection

Runs in parallel with segmentation and produces an independent CTR estimate.

- **Model:** YOLOv11-large
- **Detected Structures:** Heart, Left Lung, Right Lung
- **Keypoint Detection:** Lower corners of lungs and heart — used to measure horizontal extents
- **Data Augmentation:** Mosaic, Mixup, Horizontal/Vertical Flip
- **Training:** 50 epochs, batch size 64
- **CTR Calculation:** Heart width and lung width are derived from detected bounding box coordinates

---

## CTR Calculation

### Segmentation-Based CTR

Canny edge detection is applied to the heart and lung masks. Horizontal extents are measured from the detected boundaries:

```
CTR = Heart Width / Lung Width
```

### Ensemble CTR

The two branches are combined with a weighted average:

```
Ensemble CTR = (YOLO CTR × 0.95) + (Segmentation CTR × 0.05)
```

YOLO contributes high overall accuracy; the segmentation branch adds fine anatomical detail as a complementary correction signal.

**Ensemble performance improvement:**

| Model | MAPE | SMAPE | MAE | RMSE |
|---|---|---|---|---|
| YOLO only | 5.58% | 5.32% | 0.02 | 0.04 |
| Segmentation only | 6.03% | 5.64% | 0.03 | 0.05 |
| **Ensemble** | **5.02%** | **4.87%** | **0.02** | **0.03** |

---

## Feature Extraction

The 63 features fed into the ANN come from four sources:

### 1. Ensemble CTR
The weighted CTR value computed above. The primary clinical measurement.

### 2. Heart/Lung Area Ratio
Computed as: `Heart Pixel Area / Lung Pixel Area`

Complements CTR by providing an area-based measurement rather than a width-based one. Particularly useful in cases of chest deformity or lung disease where width measurements alone can be misleading.

### 3. Heart Left–Right Width
Measures how much the heart extends to the left and right of the cardiac midline.

- The esophageal axis is estimated by predicting its upper and lower endpoints
- The angle between these points is computed via `arctan2` and extended into a full axis line
- The segmented heart is split along this line
- Left-side and right-side widths are measured separately

This determines the **direction of cardiac enlargement**, providing clinically meaningful spatial context beyond a single ratio.

### 4. Radiomics — 55 Features
Extracted from the heart segmentation mask using PyRadiomics.

- 186 candidate features are initially extracted covering intensity distribution, tissue homogeneity, shape, and texture
- 55 clinically significant and statistically contributive features are selected for the final model
- These features allow the model to reason about the **statistical character of the image**, not just its visual appearance

---

## CNN — Parallel Image Classifier

A custom **Residual Attention CNN** runs alongside the feature extraction pipeline and provides an independent classification signal.

- **Input:** 256×256 grayscale chest X-ray
- **Parameters:** ~7.5 million
- **Loss Function:** Binary Crossentropy
- **Optimizer:** Adam, 100 epochs

| Component | Purpose |
|---|---|
| Residual (skip) blocks | Prevents information loss in deeper layers |
| Attention mechanism | Focuses the model on the cardiac and pulmonary region |
| Dropout + Batch Normalization | Prevents overfitting |
| Latent Supervision | Trains on both intermediate and final outputs for balanced learning |

- **External Test Performance:** 80.8% accuracy, 81.8% F1 score

---

## ANN — Final Fusion Model

All features (63-dimensional vector) are fused in a single fully-connected ANN that produces the final prediction.

- **Architecture:** 512 → 256 → 128 neurons (fully connected)
- **Input:** CNN output + Ensemble CTR + Heart/Lung Area Ratio + Heart Left-Right Width + 55 Radiomics features
- **Regularization:** BatchNormalization + Dropout
- **Output:** Cardiomegaly present / not present (binary)
- **External Test Performance:** 84.0% accuracy, 85.3% F1 score

---

## Synthetic Data Augmentation with GANs

To address low diversity in cardiomegaly cases, four GAN architectures were trained and evaluated using FID scores (lower = better):

| Model | FID Score |
|---|---|
| CGAN | 62 |
| DCGAN | 72 |
| IAGAN | 40 |
| **StyleGAN2-ADA** | **25** ✓ |

StyleGAN2-ADA's **Adaptive Data Augmentation (ADA)** mechanism enables stable training even with limited data, achieving the lowest FID score and the most realistic synthetic X-ray images.

---

## Explainable AI (XAI)

The system not only produces a prediction — it explains why.

- **Grad-CAM:** Generates heatmaps showing which image regions the CNN focused on. Confirms the model attends to the cardiac and pulmonary area rather than irrelevant regions.
- **SHAP Analysis:** Quantifies each feature's individual contribution to the final prediction. CNN output is the highest contributor, followed by YOLO CTR and area ratio.

---

## Training Data

A total of **80,572 images** from five datasets were assembled into a balanced training set (40,286 cardiomegaly / 40,286 non-cardiomegaly).

| Dataset | Institution | Images | Split |
|---|---|---|---|
| MIMIC-CXR | MIT, USA | 51,693 | Training |
| PadChest | Hospital San Juan, Spain | 12,600 | Training |
| CheXpert | Stanford University, USA | 6,046 | Training |
| VinDr-CXR | VinBigData, Vietnam | 4,598 | Training |
| BRAX | Hospital Albert Einstein, Brazil | 2,572 | Hard Training |
| NIH ChestX-Ray14 | NIH Clinical Center, USA | 3,063 | **External Test** |

---

## Performance Summary

| Model / Component | Metric | Value |
|---|---|---|
| U-Net++ (Heart Segmentation) | IoU | 89% |
| YOLOv11 CTR | MAPE / SMAPE | 5.58% / 5.32% |
| Segmentation CTR | MAPE / SMAPE | 6.03% / 5.64% |
| **Ensemble CTR** | **MAPE / SMAPE** | **5.02% / 4.87%** |
| CNN Classifier | F1 / Accuracy | 81.8% / 80.8% |
| **ANN (Final)** | **F1 / Accuracy** | **85.3% / 84.0%** |

---

## Installation & Validation

> This is the official technical documentation prepared by the Ethosoft team for Teknofest validators.

### Python Version

**Python 3.12** must be installed on the validation device. This is required for full library compatibility.

### NVIDIA GPU Setup (Optional)

If the validation device has an NVIDIA GPU and GPU acceleration is desired, follow the steps below. This step can be skipped for CPU-only runs.

> ⚠️ On **Windows**, GPU acceleration will **not work for TensorFlow**. It can be used for PyTorch only.  
> ⚠️ Non-NVIDIA GPUs are **not supported**.

1. Visit the CUDA Toolkit download page for your device type and follow the installation steps.
2. After installation, verify by running the following in your terminal:

```bash
nvidia-smi
```

### Library Installation

#### Windows

1. Activate your target Python environment (skip if installing globally).
2. Run:

```bash
pip install -r winrequirements.txt
```

#### Linux

1. Activate your target Python environment (skip if installing globally).
2. Run:

```bash
pip install -r requirements.txt
```

### PyRadiomics Installation

The cardiomegaly task uses **PyRadiomics**, which cannot be installed directly via pip and requires a manual build. Git must be installed on your system.

1. Open a terminal in the documentation folder.
2. Run:

```bash
git clone https://github.com/AIM-Harvard/pyradiomics.git
pip install -e pyradiomics/[dev,docs,test]
```

### Running Cardiomegaly / CTR Validation

From the documentation folder, with your Python environment active, run:

```bash
python kardiyomegaliteknefes.py --base_path [IMAGE_PATH] --output_json [OUTPUT_JSON_PATH]
```

Replace the bracketed values with your own paths:

| Argument | Description |
|---|---|
| `--base_path` | Path to the folder containing the chest X-ray images |
| `--output_json` | Path where the output JSON file will be saved |