Bone_Age_VGG16.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM19yn2a+eMSlcAI69BRcGs",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Armin-Abdollahi/Bone-Age-Estimation/blob/main/Bone_Age_VGG16.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TL4lrilPWtaY"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input, Flatten, Dense, Dropout\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.applications import VGG16"
      ],
      "metadata": {
        "id": "HQd_9YzWW1or"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the labels\n",
        "labels_path = '/content/drive/MyDrive/Digital_Hand_Atlas_Dataset/Total_Images/Bordered_XRay_Images/Label.csv'\n",
        "labels = pd.read_csv(labels_path)"
      ],
      "metadata": {
        "id": "PZKgbYwdW1lR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels['Case ID'] = labels['Case ID'].apply(lambda x: str(x)+'.jpg')\n",
        "\n",
        "labels.head()"
      ],
      "metadata": {
        "id": "gMyiOPwYW1iP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Ensure the 'Case ID' column is of type string\n",
        "labels['Case ID'] = labels['Case ID'].astype(str)"
      ],
      "metadata": {
        "id": "IpKuZ9O9W1fm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the image data generator\n",
        "datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)"
      ],
      "metadata": {
        "id": "6tuyR0ZOW1c9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the train and validation generators\n",
        "train_generator = datagen.flow_from_dataframe(\n",
        "    dataframe=labels,\n",
        "    directory='/content/drive/MyDrive/Digital_Hand_Atlas_Dataset/Total_Images/Bordered_XRay_Images',\n",
        "    x_col='Case ID',\n",
        "    y_col='Bone Age',\n",
        "    target_size=(256, 256),\n",
        "    color_mode='grayscale',\n",
        "    class_mode='raw',\n",
        "    batch_size=32,\n",
        "    subset='training'\n",
        ")\n",
        "\n",
        "validation_generator = datagen.flow_from_dataframe(\n",
        "    dataframe=labels,\n",
        "    directory='/content/drive/MyDrive/Digital_Hand_Atlas_Dataset/Total_Images/Bordered_XRay_Images',\n",
        "    x_col='Case ID',\n",
        "    y_col='Bone Age',\n",
        "    target_size=(256, 256),\n",
        "    color_mode='grayscale',\n",
        "    class_mode='raw',\n",
        "    batch_size=32,\n",
        "    subset='validation'\n",
        ")"
      ],
      "metadata": {
        "id": "HvpCqTAdW1aQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the VGG16 model, pre-trained on ImageNet data\n",
        "vgg16_base = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))"
      ],
      "metadata": {
        "id": "XzQg0oAMW1Xn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Freeze the layers of VGG16\n",
        "for layer in vgg16_base.layers:\n",
        "    layer.trainable = False"
      ],
      "metadata": {
        "id": "NFxVNtIIW1VA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create new input layer\n",
        "input_layer = Input(shape=(224, 224, 3))"
      ],
      "metadata": {
        "id": "QAIPcI65W1SW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the generated model\n",
        "output_vgg16_conv = vgg16_base(input_layer)"
      ],
      "metadata": {
        "id": "zugiZbUKW1Pp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Add the fully-connected layers\n",
        "x = Flatten(name='flatten')(output_vgg16_conv)\n",
        "x = Dense(512, activation='relu', name='fc1')(x)\n",
        "x = Dropout(0.5, name='dropout')(x)\n",
        "x = Dense(1, activation='linear', name='predictions')(x)  # Linear activation for regression"
      ],
      "metadata": {
        "id": "LVQdwjM7W1Mt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create your own model\n",
        "my_model = Model(inputs=input_layer, outputs=x)"
      ],
      "metadata": {
        "id": "ACshHjMtW1J9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the model\n",
        "model.compile(optimizer=Adam(), loss='mean_squared_error', metrics=['mae', 'accuracy'])"
      ],
      "metadata": {
        "id": "lnZYOCWyW1Hr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "history = model.fit(\n",
        "    train_generator,\n",
        "    steps_per_epoch=train_generator.samples // 32,\n",
        "    validation_data=validation_generator,\n",
        "    validation_steps=validation_generator.samples // 32,\n",
        "    epochs=10\n",
        ")"
      ],
      "metadata": {
        "id": "zwD5bb7TXLDU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot training & validation MAE values\n",
        "plt.figure(figsize=(18, 4))\n",
        "\n",
        "plt.subplot(1, 3, 1)\n",
        "plt.plot(history.history['mae'])\n",
        "plt.plot(history.history['val_mae'])\n",
        "plt.title('Model MAE')\n",
        "plt.ylabel('MAE')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "\n",
        "# Plot training & validation loss values\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "\n",
        "# Plot training & validation accuracy values\n",
        "plt.subplot(1, 3, 3)\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "9ue3VnQxXN_Z"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}