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