Add initial Keras model for stroke classification

README.md

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+
+# Stroke Classification ResNet Model
+
+This is a Keras model for classifying MRI images into:
+- Hemorrhagic Stroke
+- Ischemic Stroke
+- No Stroke
+
+The model is a fine-tuned ResNet50.
+
+## How to use this model (example in Python)
+
+```python
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+import os # Added for os.path.join
+
+# Load the model
+# Ensure the model file 'stroke_classification_model.h5' is in the same directory
+# or provide the full path.
+model = tf.keras.models.load_model('stroke_classification_model.h5')
+
+# Define your class names (must match how your model was trained)
+CLASS_NAMES = ['hemorrhagic_stroke', 'ischemic_stroke', 'no_stroke'] # Automatically populated from your Colab session
+
+def preprocess_image_for_prediction(image_path, target_size=(224, 224), pixel_threshold=40):
+    img = Image.open(image_path).convert("L")
+    original_width, original_height = img.size
+    data = np.array(img)
+    rows_with_content = np.any(data > pixel_threshold, axis=1)
+    cols_with_content = np.any(data > pixel_threshold, axis=0)
+    try:
+        min_row = np.where(rows_with_content)[0][0]
+        max_row = np.where(rows_with_content)[0][-1]
+        min_col = np.where(cols_with_content)[0][0]
+        max_col = np.where(cols_with_content)[0][-1]
+    except IndexError:
+        cropped_img = img
+    else:
+        buffer = 5
+        min_row = max(0, min_row - buffer)
+        max_row = min(original_height - 1, max_row + buffer)
+        min_col = max(0, min_col - buffer)
+        max_col = min(original_width - 1, max_col + buffer)
+        cropped_img = img.crop((min_col, min_row, max_col + 1, max_row + 1))
+    processed_img = cropped_img.resize(target_size, Image.LANCZOS)
+    if processed_img.mode == 'L':
+        processed_img = processed_img.convert('RGB')
+    img_array = tf.keras.utils.img_to_array(processed_img)
+    img_array = tf.expand_dims(img_array, 0)
+    return img_array
+
+# Example usage:
+# image_path = "path/to/your/new_mri_image.jpg"
+# preprocessed_img = preprocess_image_for_prediction(image_path)
+# if preprocessed_img is not None:
+#     predictions = model.predict(preprocessed_img)
+#     predicted_class_index = np.argmax(predictions[0])
+#     predicted_class_name = CLASS_NAMES[predicted_class_index]
+#     confidence = np.max(predictions[0]) * 100
+#     print(f"Predicted: {predicted_class_name} with {confidence:.2f}% confidence")
+```

stroke_classification_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f68f6e0e5521317725c5385e66d1858d3ff2d6e147494ef30282232334356cf4
+size 286389792