| --- |
| license: mit |
| tags: |
| - medical-imaging |
| - pcos-detection |
| - explainable-ai |
| - grad-cam |
| - ultrasound |
| - image Classification |
| - pcos |
| language: en |
| metrics: |
| - accuracy |
| library_name: tensorflow |
| --- |
| |
| # PCOS Detection with Explainable AI |
|
|
| A deep learning model for **Polycystic Ovary Syndrome (PCOS)** detection from ultrasound images with **Grad-CAM** visualization for clinical interpretability. |
|
|
| ## Model Overview |
|
|
| - **Architecture**: Dual-path CNN with multi-head attention |
| - **Input**: 224×224 RGB ultrasound images |
| - **Output**: Binary classification (PCOS-positive / Healthy) |
| - **Accuracy**: ~95%+ on test set |
| - **XAI**: Grad-CAM heatmaps for interpretability |
|
|
| ## 🚀 Quick Start |
|
|
| ```bash |
| pip install tensorflow opencv-python matplotlib numpy requests huggingface-hub |
| ``` |
|
|
| ### Complete Working Example |
|
|
| ```python |
| # ============================================================ |
| # 🔍 PCOS Prediction + Grad-CAM (HF VERSION) |
| # ============================================================ |
| |
| import numpy as np |
| import cv2 |
| import tensorflow as tf |
| import matplotlib.pyplot as plt |
| from tensorflow.keras import Model, Input |
| from tensorflow.keras.layers import ( |
| Conv2D, MaxPooling2D, Flatten, Dense, |
| Lambda, Reshape, Concatenate, |
| MultiHeadAttention, GlobalAveragePooling1D |
| ) |
| import requests |
| from huggingface_hub import hf_hub_download |
| |
| # ============================================================ |
| # Config |
| # ============================================================ |
| IMG_SIZE = (224, 224) |
| HF_MODEL_REPO = "Dehsahk-AI/Pcos-Detect" |
| MODEL_FILENAME = "best_pcos_model.h5" |
| IMAGE_URL = "https://example.com/ultrasound.jpg" # Your image URL |
| CLASS_NAMES = ["infected", "noninfected"] |
| |
| # ============================================================ |
| # Download model from HF |
| # ============================================================ |
| MODEL_PATH = hf_hub_download(repo_id=HF_MODEL_REPO, filename=MODEL_FILENAME) |
| print(f" Model downloaded to: {MODEL_PATH}") |
| |
| # ============================================================ |
| # Custom Lambda Functions |
| # ============================================================ |
| def split_image(image): |
| upper = image[:, :IMG_SIZE[0]//2, :, :] |
| lower = image[:, IMG_SIZE[0]//2:, :, :] |
| return upper, lower |
| |
| def flip_lower(lower_half): |
| return tf.image.flip_left_right(lower_half) |
| |
| # ============================================================ |
| # Rebuild Model Architecture |
| # ============================================================ |
| input_layer = Input(shape=(224,224,3)) |
| |
| upper_half, lower_half = Lambda(split_image)(input_layer) |
| lower_half = Lambda(flip_lower)(lower_half) |
| |
| # Upper CNN |
| u = Conv2D(32, 3, activation="relu", padding="same")(upper_half) |
| u = MaxPooling2D(2)(u) |
| u = Conv2D(64, 3, activation="relu", padding="same")(u) |
| u = MaxPooling2D(2)(u) |
| u = Conv2D(128, 3, activation="relu", padding="same", name="upper_last_conv")(u) |
| u = MaxPooling2D(2)(u) |
| u = Flatten()(u) |
| |
| # Lower CNN |
| l = Conv2D(32, 3, activation="relu", padding="same")(lower_half) |
| l = MaxPooling2D(2)(l) |
| l = Conv2D(64, 3, activation="relu", padding="same")(l) |
| l = MaxPooling2D(2)(l) |
| l = Conv2D(128, 3, activation="relu", padding="same", name="lower_last_conv")(l) |
| l = MaxPooling2D(2)(l) |
| l = Flatten()(l) |
| |
| u_dense = Dense(512, activation="relu")(u) |
| l_dense = Dense(512, activation="relu")(l) |
| |
| u_r = Reshape((1,512))(u_dense) |
| l_r = Reshape((1,512))(l_dense) |
| |
| concat = Concatenate(axis=1)([u_r, l_r]) |
| |
| att = MultiHeadAttention(num_heads=4, key_dim=64)(concat, concat) |
| att = GlobalAveragePooling1D()(att) |
| |
| fc = Dense(256, activation="relu")(att) |
| fc = Dense(128, activation="relu")(fc) |
| |
| # Logits for Grad-CAM |
| logits = Dense(2, name="logits")(fc) |
| output = tf.keras.layers.Activation('softmax', name='softmax')(logits) |
| |
| model = Model(input_layer, output) |
| model.load_weights(MODEL_PATH) |
| print(" Weights loaded successfully") |
| |
| # ============================================================ |
| # Load & Preprocess Image |
| # ============================================================ |
| response = requests.get(IMAGE_URL) |
| img_array_raw = np.asarray(bytearray(response.content), dtype=np.uint8) |
| img = cv2.imdecode(img_array_raw, cv2.IMREAD_COLOR) |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) |
| img = cv2.resize(img, IMG_SIZE) |
| img = img.astype(np.float32) / 255.0 |
| img_array = np.expand_dims(img, axis=0) |
| |
| # ============================================================ |
| # Prediction |
| # ============================================================ |
| pred = model.predict(img_array, verbose=0)[0] |
| pred_class = np.argmax(pred) |
| confidence = pred[pred_class] |
| |
| print(f"\n Prediction: {CLASS_NAMES[pred_class]}") |
| print(f" Confidence: {confidence:.2%}") |
| |
| # ============================================================ |
| # Grad-CAM |
| # ============================================================ |
| def gradcam(img_array, model, layer_name, pred_index): |
| logits_layer = model.get_layer('logits') |
| grad_model = Model( |
| model.input, |
| [model.get_layer(layer_name).output, logits_layer.output] |
| ) |
| |
| with tf.GradientTape() as tape: |
| conv_out, logits = grad_model(img_array) |
| loss = logits[:, pred_index] |
| |
| grads = tape.gradient(loss, conv_out) |
| pooled = tf.reduce_mean(grads, axis=(0,1,2)) |
| conv_out = conv_out[0] |
| |
| heatmap = conv_out @ pooled[..., tf.newaxis] |
| heatmap = tf.squeeze(heatmap) |
| heatmap = tf.maximum(heatmap, 0) |
| |
| if tf.reduce_max(heatmap) > 0: |
| heatmap /= tf.reduce_max(heatmap) |
| |
| return heatmap.numpy() |
| |
| upper = gradcam(img_array, model, "upper_last_conv", pred_class) |
| lower = gradcam(img_array, model, "lower_last_conv", pred_class) |
| |
| h = IMG_SIZE[0] // 2 |
| upper = cv2.resize(upper, (IMG_SIZE[1], h)) |
| lower = cv2.resize(lower, (IMG_SIZE[1], h)) |
| lower = cv2.flip(lower, 1) |
| |
| heatmap = np.vstack([upper, lower]) |
| |
| heatmap_color = cv2.applyColorMap(np.uint8(255*heatmap), cv2.COLORMAP_JET) |
| heatmap_color = cv2.cvtColor(heatmap_color, cv2.COLOR_BGR2RGB) / 255.0 |
| |
| overlay = 0.5 * heatmap_color + 0.5 * img |
| |
| # ============================================================ |
| # Visualization |
| # ============================================================ |
| plt.figure(figsize=(15,5)) |
| |
| plt.subplot(1,3,1) |
| plt.imshow(img) |
| plt.title("Original") |
| plt.axis("off") |
| |
| plt.subplot(1,3,2) |
| plt.imshow(heatmap, cmap="jet") |
| plt.title("Grad-CAM") |
| plt.axis("off") |
| |
| plt.subplot(1,3,3) |
| plt.imshow(overlay) |
| plt.title(f"{CLASS_NAMES[pred_class]} ({confidence:.2%})") |
| plt.axis("off") |
| |
| plt.tight_layout() |
| plt.show() |
| ``` |
|
|
| ### Load from Local File |
|
|
| ```python |
| # Replace URL loading with: |
| img = cv2.imread('path/to/ultrasound.jpg') |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) |
| img = cv2.resize(img, IMG_SIZE) |
| img = img.astype(np.float32) / 255.0 |
| img_array = np.expand_dims(img, axis=0) |
| ``` |
|
|
| ## Understanding Grad-CAM Output |
|
|
| - **Red/Hot regions**: High importance for prediction (follicles, cysts) |
| - **Blue/Cool regions**: Low influence on decision |
| - **Dual visualization**: Separate heatmaps for upper and lower ovarian regions |
|
|
| ## Model Architecture |
|
|
| ``` |
| Input (224×224×3) |
| ├── Split horizontally (upper/lower) |
| ├── Upper Path: Conv32 → Conv64 → Conv128 → Dense512 |
| ├── Lower Path: Conv32 → Conv64 → Conv128 → Dense512 |
| ├── Multi-Head Attention (4 heads, dim=64) |
| └── Classification: Dense256 → Dense128 → Dense2 |
| ``` |
|
|
| **Key Features:** |
| - Dual-path CNN for separate ovarian region analysis |
| - Lower region flipped for symmetry normalization |
| - Multi-head attention for feature fusion |
| - Logits-based Grad-CAM (fixes saturated softmax gradients) |
|
|
| ## Dataset |
|
|
| - **Total**: 11,784 ultrasound images |
| - **PCOS-positive**: 6,784 images (57.5%) |
| - **Healthy**: 5,000 images (42.5%) |
| - **Source**: 3 clinics (2018-2022), expert-annotated |
| - **Dataset**: [PCOS XAI Ultrasound](https://www.kaggle.com/datasets/ibadeus/pcos-xai-ultrasound-dataset) |
|
|
| ## Important Notes |
|
|
| **Clinical Use:** |
| - Research purposes only - NOT FDA approved |
| - Not a diagnostic tool - requires professional validation |
| - Must be validated on local datasets before clinical deployment |
|
|
| **Technical:** |
| - Fixed 224×224 input size required |
| - RGB images only |
| - Model performance may vary across different ultrasound machines |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{pcos_xai_2024, |
| title={PCOS Detection with Explainable AI}, |
| author={Dehsahk-AI}, |
| year={2025}, |
| url={https://huggingface.co/Dehsahk-AI/Pcos-Detect} |
| } |
| ``` |
|
|
| ## License |
|
|
| MIT License - See LICENSE file for details. |
|
|
|
|
| --- |
|
|
| **Model Version**: 1.0 | **Last Updated**: December 2025 |
| license: MIT |
| --- |
