breast cancer detection

app.py

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+import gradio as gr
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.models import Model
+from tensorflow.keras.applications import ResNet50
+from tensorflow.keras.layers import Conv2D, BatchNormalization, LeakyReLU, Flatten, Dense, Reshape, Dropout, Add
+
+IMG_SIZE = 512
+
+def residual_block(X,filters):
+    # Retrieve Filters
+    F1, F2 = filters
+    # Saving the input value.we need this later to add to the output. 
+    X_shortcut = X
+    
+    # First component of main path
+    X = Conv2D(filters = F1, kernel_size = (3, 3), strides = (1,1), padding = 'same')(X)
+    X = BatchNormalization()(X)
+    X = LeakyReLU(alpha=0.1)(X)
+
+    # Second component of main path 
+    X = Conv2D(filters = F2, kernel_size = (3, 3), strides = (1,1), padding = 'same')(X)
+    X = BatchNormalization()(X)
+
+    # Final step: Add shortcut value to main path, and pass it through a RELU activation 
+    X = Add()([X, X_shortcut])
+    X = LeakyReLU(alpha=0.1)(X)
+    return X
+
+def build_model():
+    # Inputs to the model
+    base_model = ResNet50(
+        weights='imagenet',
+        input_shape=(512, 512, 3),  # Input shape of the images (height, width, channels)
+        include_top=False  # Exclude the top classification layers
+    )
+
+    # Freeze the base model's layers to prevent them from being trained
+    base_model.trainable = False
+    x = base_model.output
+    
+    # First conv block
+    x = Conv2D(32,(3, 3),kernel_initializer="he_normal",padding="same")(x)
+    x = LeakyReLU(alpha=0.1)(x)
+    
+    # Add a residual block
+    x = residual_block(x, [64, 32]) 
+    x = residual_block(x, [64, 32]) 
+    x = Flatten()(x)
+    
+    x = Dense(128, kernel_initializer='he_normal')(x)
+    x = Dropout(0.2)(x)
+    x = Dense(64, kernel_initializer='he_normal')(x)
+    x = Dropout(0.2)(x)
+    
+    x = Dense(1, activation='sigmoid')(x)
+    model = Model(inputs=base_model.input, outputs=x)
+    
+    return model
+
+
+stage1_cc = build_model()
+stage1_cc.load_weights("weights/stage1_cc_weights.weights.h5")
+
+stage1_mlo = build_model()
+stage1_mlo.load_weights("weights/stage1_mlo_weight.weights.h5")
+
+stage2_cc = build_model()
+stage2_cc.load_weights("weights/stage2_cc_weights.weights.h5")
+
+stage2_mlo = build_model()
+stage2_mlo.load_weights("weights/stage2_mlo_weight.weights.h5")
+
+
+def get_diff(prior_image, current_image):
+    print(prior_image.shape)
+    print(current_image.shape)
+
+    prior_image = np.array(prior_image, dtype=np.float32)
+    current_image = np.array(current_image, dtype=np.float32)
+
+    prior_image = prior_image.astype(np.float32)
+    current_image = current_image.astype(np.float32)
+
+    avg_width = int((prior_image.shape[0] + current_image.shape[0])/2)
+    avg_height = int((prior_image.shape[1] + current_image.shape[1])/2)
+
+    # print(avg_width, avg_width)
+
+    prior_image = np.resize(prior_image, [avg_width, avg_height, 3])
+    current_image = np.resize(current_image, [avg_width, avg_height, 3])
+
+    subtract_image =current_image - prior_image
+    subtract_image[subtract_image<0] = 0
+    return subtract_image
+
+def stage1_run(cc_diff_img, mlo_diff_img):
+    cc_diff_img = np.resize(cc_diff_img, [IMG_SIZE, IMG_SIZE, 3])
+    cc_diff_img = np.expand_dims(cc_diff_img, axis=0)
+    cc_diff_img = tf.constant(cc_diff_img, dtype=tf.float32)
+    # print(cc_diff_img.shape)
+    cc_res = stage1_cc.predict(cc_diff_img)
+    # print(cc_res)
+    # mlo_res = stage1_mlo.predict(mlo_diff_img)
+    mlo_diff_img = np.resize(mlo_diff_img, [IMG_SIZE, IMG_SIZE, 3])
+    mlo_diff_img = np.expand_dims(mlo_diff_img, axis=0)
+    mlo_diff_img = tf.constant(mlo_diff_img, dtype=tf.float32)
+
+    mlo_res = stage1_mlo.predict(mlo_diff_img)
+    # print(mlo_res)
+    
+    return (cc_res + mlo_res)/2
+
+def stage2_run(cc_diff_img, mlo_diff_img):
+    cc_diff_img = np.resize(cc_diff_img, [IMG_SIZE, IMG_SIZE, 3])
+    cc_diff_img = np.expand_dims(cc_diff_img, axis=0)
+    cc_diff_img = tf.constant(cc_diff_img, dtype=tf.float32)
+    # print(cc_diff_img.shape)
+    cc_res = stage2_cc.predict(cc_diff_img)
+    # print(cc_res)
+    # mlo_res = stage1_mlo.predict(mlo_diff_img)
+    mlo_diff_img = np.resize(mlo_diff_img, [IMG_SIZE, IMG_SIZE, 3])
+    mlo_diff_img = np.expand_dims(mlo_diff_img, axis=0)
+    mlo_diff_img = tf.constant(mlo_diff_img, dtype=tf.float32)
+
+    mlo_res = stage2_mlo.predict(mlo_diff_img)
+    # print(mlo_res)
+    
+    return (cc_res + mlo_res)/2
+
+def give_result(cc_prior_image, mlo_prior_image, cc_recent_image, mlo_recent_image):
+    cc_prior_img = np.array(cc_prior_image)
+    mlo_prior_img = np.array(mlo_prior_image)
+    cc_recent_img = np.array(cc_recent_image)
+    mlo_recent_img = np.array(mlo_recent_image)
+
+    cc_diff_img = get_diff(cc_prior_img, cc_recent_img)
+    mlo_diff_img = get_diff(mlo_prior_img, mlo_recent_img)
+
+    stage1_res = stage1_run(cc_diff_img, mlo_diff_img)
+
+    if(stage1_res<0.4):
+        return "Normal"
+    
+    stage2_res = stage2_run(cc_diff_img, mlo_diff_img)
+    if(stage2_res<0.4):
+        return "Benign"
+    else:
+        return "Suspecious"
+
+with gr.Blocks(title="Breast Cancer detection", css=".gradio-container {background:lightyellow;}") as demo:
+    gr.HTML("<h1>Breast Cancer Detection</h1>")
+    with gr.Row():
+        cc_prior_image = gr.Image(label="CC Prior image", type="pil")
+        mlo_prior_image = gr.Image(label="MLO Prior image", type="pil")
+
+    with gr.Row():
+        cc_recent_image = gr.Image(label="CC Recent image", type="pil")
+        mlo_recent_image = gr.Image(label="MLO Recent image", type="pil")
+    gr.HTML("<br/>")
+    output_label = gr.TextArea(placeholder="Result")
+    send_btn = gr.Button("Detect")
+    send_btn.click(fn=give_result, inputs=[cc_prior_image, mlo_prior_image, cc_recent_image, mlo_recent_image], outputs=[output_label])
+
+demo.launch(debug=True)

weights/stage1_cc_weights.weights.h5

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weights/stage1_mlo_weight.weights.h5

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weights/stage2_cc_weights.weights.h5

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weights/stage2_mlo_weight.weights.h5

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