| --- |
| license: mit |
| library_name: keras |
| pipeline_tag: image-classification |
| tags: [cnn, image-classification, tensorflow, keras, educational, from-scratch] |
| --- |
| |
| # cnn-model: small CNN image-classification demo |
|
|
| Classroom prototype. NOT a medical device and NOT a validated pneumonia detector. |
|
|
| ## Model |
|
|
| This is a custom TensorFlow Sequential CNN with three `Conv2D` blocks, global-average pooling, a dense layer, dropout, and a three-class softmax output. It was trained from scratch with no pretrained weights. |
|
|
| Classes: |
|
|
| - `INVALID` |
| - `NORMAL` |
| - `PNEUMONIA` |
|
|
| Recorded configuration: |
|
|
| - Input: RGB image resized to `224 × 224`, producing a `(224, 224, 3)` model input |
| - Application preprocessing: convert the image to RGB, resize it to `(224, 224)`, convert it to a NumPy array with `dtype=np.float32`, and add a batch dimension with `np.expand_dims(..., axis=0)` |
| - Rescaling: `tf.keras.layers.Rescaling(1.0 / 255.0)` inside the model; do not rescale the application input separately |
| - Optimizer: `tf.keras.optimizers.Adam(learning_rate=0.001)` |
| - Loss: `tf.keras.losses.SparseCategoricalCrossentropy()` |
| - Training metric: `accuracy` |
|
|
| ## Training data |
|
|
| Dataset name: **not recorded**. Dataset source: **not recorded**. The images are not included. |
|
|
| The experiment used 12 original images: 10 `INVALID`, 1 `NORMAL`, and 1 `PNEUMONIA`. Offline augmentation expanded these into 612 images, which were then split 80/20 with seed `123` into 490 training images and 122 validation images. |
|
|
| Training used `EarlyStopping` monitoring `val_loss` with patience 5 and `restore_best_weights=True`, plus `ReduceLROnPlateau` monitoring `val_loss`. |
|
|
| ## Result |
|
|
| **From saved notebook output, not re-run:** validation accuracy **1.0000** and validation loss **0.00044693** on 122 validation images. |
|
|
| ## How to read this result |
|
|
| Augmentation was done before the split, so augmented copies of the same original image appear in both the training and validation sets. The same validation set drove `EarlyStopping` and `ReduceLROnPlateau`. There is no held-out test set; precision, recall, F1, and a confusion matrix were not computed. The number does not show performance on new X-rays. |
|
|
| ## How to load it |
|
|
| The expected input is one or more RGB images resized to `224 × 224`, represented as `float32` values in the original `0–255` range. The model contains its own `1/255` rescaling layer. |
|
|
| ```python |
| import tensorflow as tf |
| from huggingface_hub import hf_hub_download |
| |
| model_path = hf_hub_download( |
| repo_id="faiz4320/cnn-model", |
| filename="model.keras" |
| ) |
| |
| model = tf.keras.models.load_model( |
| model_path |
| ) |
| ``` |
|
|
| For inference, convert the source image to RGB, resize it to `(224, 224)`, convert it to a NumPy `float32` array, and add a batch dimension. Do not apply another `1/255` normalization because rescaling is part of the saved model. |
|
|
| ## Intended use and limitations |
|
|
| - Educational demonstration only; not for medical use |
| - Tiny independent sample: 1 original `NORMAL` image and 1 original `PNEUMONIA` image |
| - Leakage caused by augmenting before splitting |
| - No held-out test set |
| - Artificial class balance created through augmentation |
| - Dataset source not recorded |
|
|
| ## Code |
|
|
| https://github.com/faiz-sheikh/Chest-Xray-CNN |
|
|
| ## Credits |
|
|
| - TensorFlow/Keras |
| - Streamlit |
|
|
