---
license: apache-2.0
datasets:
- kanaadlimaye/Alzheimers-Classification-Dataset
language:
- en
metrics:
- accuracy
base_model:
- microsoft/resnet-152
pipeline_tag: image-classification
tags:
- medical-imaging
- alzheimers
- mri
- computer-vision
- pytorch
- transfer-learning

---

# Alzheimer Classification using ResNet-152

This model performs **multi-class classification of Alzheimer's disease stages** from MRI brain scans.  
It uses **ResNet152 pretrained on ImageNet** as a feature extractor with a custom fully-connected classification head.  

> The following repo contains the TensorFlow-Keras model (.h5) version. The PyTorch version (.pt) is yet to be released.

> **The eval metrics on both the models is the same.**
---

## Source Code (GitHub Repository)
https://github.com/kanaad-lims/Alzheimer-Classification-using-PyTorch

---

## Model Description

This model classifies MRI scans into **four stages of Alzheimer's disease**:

| Class | Description        |
|-------|--------------------|
| MD    | Mild Demented      |
| MoD   | Moderate Demented  |
| ND    | Non Demented       |
| VMD   | Very Mild Demented |

The architecture uses **transfer learning** with a pretrained ResNet152 backbone followed by custom dense layers for classification.

---

## Model Architecture
```
Input Image (3 × H × W tensor)
        ↓
ResNet152 Backbone (ImageNet pretrained)
        ↓
Global Average Pooling
        ↓
  2048 feature vector
        ↓
  Batch Normalization
        ↓
      Dense(512)
        ↓
      Dense(256)
        ↓
     Dropout(0.5)
        ↓
      Dense(128)
        ↓
     Dropout(0.3)
        ↓
       Dense(64)
        ↓
    Dense(4) (class logits)
```

Softmax is implicitly applied during training through **CrossEntropyLoss**.

---

## Training Details

### Training Hardware
- Kaggle Notebooks
- NVIDIA Tesla T4 GPUs

### Hyperparameters

| Parameter      | Value                          |
|----------------|--------------------------------|
| Batch Size     | 32                             |
| Optimizer      | Adam                           |
| Loss Function  | CrossEntropyLoss               |
| Backbone       | ResNet152 (ImageNet pretrained)|

The ResNet backbone weights were **frozen**, and only the custom classifier head was trained.

---

## Dataset

**Alzheimer's Classification Dataset**  
https://www.kaggle.com/datasets/kanaadlimaye/alzheimers-classification-dataset

The dataset consists of **MRI brain images** categorized into four stages of Alzheimer's disease.

Images were preprocessed using:
- Resizing to **224 × 224**
- Horizontal and vertical flips
- Rotation augmentation (±15°)
- Normalization

---

## Evaluation Results

| Metric              | Value  |
|---------------------|--------|
| Training Accuracy   | ~97%   |
| Validation Accuracy | ~94%   |
| Test Accuracy       | ~93%   |
| Test Loss           | ~0.22  |

### Classification Report
```
              precision    recall    f1-score    support

          MD     0.92       0.94       0.93        86
         MoD     1.00       0.80       0.89         5
          ND     0.94       0.96       0.95       319
         VMD     0.94       0.91       0.92       230

    accuracy                           0.94
   macro avg     0.95       0.90       0.92
weighted avg     0.94       0.94       0.94
```

---

## Intended Use

The model is intended for:
- Academic research
- Machine learning experimentation
- Medical imaging classification studies

> It is **not intended for clinical diagnosis**.

---

## Limitations

- The dataset contains **class imbalance**, particularly for the Moderate Dementia class.
- The model was trained on a relatively small dataset.
- MRI scans from different scanners or hospitals may reduce performance.

---

## Ethical Considerations

This model is intended strictly for **research and educational purposes**.  
Medical decisions should **never rely solely on automated predictions**.

---

## Citation

If you use this work in research, please cite the associated dataset and project.
```bibtex
@misc{alzheimers_resnet152_classifier,
  title  = {Alzheimer Stage Classification using ResNet152},
  author = {Kanaad Limaye},
  year   = {2026}
}
```