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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): | |
| res = stage1_res.numpy() | |
| return f"Normal, you have {1-res} chance of being suspecious." | |
| stage2_res = stage2_run(cc_diff_img, mlo_diff_img) | |
| if(stage2_res<0.4): | |
| return "Benign" | |
| else: | |
| return "Suspecious, You need to go for biopsy immediately." | |
| 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) |