Fracture-Resnet50.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "jwZ32SflMoll"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras import Sequential\n",
        "from keras import layers\n",
        "from keras.layers import Dense,Conv2D,MaxPooling2D,Flatten,BatchNormalization,Dropout,AveragePooling2D\n",
        "from keras.layers import Activation, Dropout, Flatten, Dense, GaussianNoise, GlobalMaxPooling2D\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import cv2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_eyE3rRc0EMF",
        "outputId": "fec610f0-7ebd-4561-8155-a4f1a18b0b8c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 8863 images belonging to 2 classes.\n",
            "Found 600 images belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "# Define data generators for train and test sets\n",
        "train_datagen = ImageDataGenerator(rescale=1./255)\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "# Flow images from directory in batches\n",
        "Xtrain= train_datagen.flow_from_directory(\n",
        "    directory='/Users/sukanya/Downloads/dataset_fracture/train',\n",
        "    target_size=(256, 256),\n",
        "    batch_size=100,\n",
        "    class_mode='binary'\n",
        ")\n",
        "\n",
        "Xtest = test_datagen.flow_from_directory(\n",
        "    directory='/Users/sukanya/Downloads/dataset_fracture/val',\n",
        "    target_size=(256, 256),\n",
        "    batch_size=100,\n",
        "    class_mode='binary'\n",
        ")\n",
        "\n",
        "# Optionally, if you want to convert class labels to integers\n",
        "class_indices = Xtrain.class_indices\n",
        "train_dataset = (Xtrain, class_indices)\n",
        "test_dataset = (Xtest, class_indices)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wQQii809Omh_",
        "outputId": "18614948-bf2e-4326-9862-73d5000cb5ce"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "# Define a function to normalize images\n",
        "def process(image, label):\n",
        "    image = tf.cast(image / 255., tf.float32)\n",
        "    return image, label\n",
        "\n",
        "# Convert DirectoryIterator objects to tf.data.Dataset objects\n",
        "train_dataset = tf.data.Dataset.from_generator(\n",
        "    lambda: Xtrain,\n",
        "    output_signature=(\n",
        "        tf.TensorSpec(shape=(None, 256, 256, 3), dtype=tf.float32),\n",
        "        tf.TensorSpec(shape=(None,), dtype=tf.int32)\n",
        "    )\n",
        ")\n",
        "\n",
        "test_dataset = tf.data.Dataset.from_generator(\n",
        "    lambda: Xtest,\n",
        "    output_signature=(\n",
        "        tf.TensorSpec(shape=(None, 256, 256, 3), dtype=tf.float32),\n",
        "        tf.TensorSpec(shape=(None,), dtype=tf.int32)\n",
        "    )\n",
        ")\n",
        "\n",
        "# Apply normalization using the map function\n",
        "x_train = train_dataset.map(process)\n",
        "x_test = test_dataset.map(process)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "EYo4lIl2M6aO"
      },
      "outputs": [],
      "source": [
        "input_shape = (256, 256, 3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "_VC8ejjSDS7_"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94765736/94765736 [==============================] - 8s 0us/step\n"
          ]
        }
      ],
      "source": [
        "# Creating the base model\n",
        "base_model = tf.keras.applications.ResNet50(\n",
        "    include_top=False,\n",
        "    weights='imagenet',\n",
        "    input_shape=input_shape\n",
        ")\n",
        "base_model.trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "ZS2HP8mO1uR7"
      },
      "outputs": [],
      "source": [
        "model = tf.keras.Sequential([\n",
        "    base_model,\n",
        "    layers.Flatten(),\n",
        "    layers.Dense(1, activation='sigmoid')\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ElJNhexgB1v5",
        "outputId": "ea44563f-d79a-48d3-e4c1-bd1022211018"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " resnet50 (Functional)       (None, 8, 8, 2048)        23587712  \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 131072)            0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 131073    \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 23718785 (90.48 MB)\n",
            "Trainable params: 131073 (512.00 KB)\n",
            "Non-trainable params: 23587712 (89.98 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "r2RfYW9a0ih2"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"accuracy\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HHBFNFHCtzLu",
        "outputId": "a01d3dc5-a48b-4efe-e406-607c695333ce"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "89/89 [==============================] - 212s 2s/step - loss: 1.0764 - accuracy: 0.5937 - val_loss: 0.6295 - val_accuracy: 0.6633\n",
            "Epoch 2/10\n",
            "89/89 [==============================] - 210s 2s/step - loss: 0.6403 - accuracy: 0.6420 - val_loss: 0.6249 - val_accuracy: 0.6817\n",
            "Epoch 3/10\n",
            "89/89 [==============================] - 211s 2s/step - loss: 0.5936 - accuracy: 0.6764 - val_loss: 0.6355 - val_accuracy: 0.5533\n",
            "Epoch 4/10\n",
            "89/89 [==============================] - 212s 2s/step - loss: 0.5437 - accuracy: 0.7176 - val_loss: 0.6211 - val_accuracy: 0.5950\n",
            "Epoch 5/10\n",
            "89/89 [==============================] - 214s 2s/step - loss: 0.4876 - accuracy: 0.7637 - val_loss: 0.5831 - val_accuracy: 0.6933\n",
            "Epoch 6/10\n",
            "89/89 [==============================] - 214s 2s/step - loss: 0.5251 - accuracy: 0.7245 - val_loss: 0.6224 - val_accuracy: 0.6033\n",
            "Epoch 7/10\n",
            "89/89 [==============================] - 214s 2s/step - loss: 0.4490 - accuracy: 0.7838 - val_loss: 0.6790 - val_accuracy: 0.6267\n",
            "Epoch 8/10\n",
            "89/89 [==============================] - 212s 2s/step - loss: 0.4702 - accuracy: 0.7571 - val_loss: 0.5866 - val_accuracy: 0.6900\n",
            "Epoch 9/10\n",
            "89/89 [==============================] - 212s 2s/step - loss: 0.4158 - accuracy: 0.7983 - val_loss: 0.6374 - val_accuracy: 0.6517\n",
            "Epoch 10/10\n",
            "89/89 [==============================] - 212s 2s/step - loss: 0.3904 - accuracy: 0.8248 - val_loss: 0.6226 - val_accuracy: 0.6533\n"
          ]
        }
      ],
      "source": [
        "batch_size = min(100, len(Xtrain))\n",
        "history = model.fit(Xtrain, epochs=10, batch_size=batch_size, validation_data=Xtest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [],
      "source": [
        "y_test = Xtest.classes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "6/6 [==============================] - 15s 2s/step\n",
            "Accuracy: 0.49333333333333335\n",
            "Precision: 0.36324786324786323\n",
            "Recall: 0.3541666666666667\n",
            "F1-score: 0.35864978902953587\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "\n",
        "# Assuming you have model predictions and true labels\n",
        "predictions = model.predict(Xtest)\n",
        "predicted_labels = (predictions < 0.5).astype(int)\n",
        "true_labels = y_test\n",
        "\n",
        "# Calculate metrics\n",
        "accuracy = accuracy_score(true_labels, predicted_labels)\n",
        "precision = precision_score(true_labels, predicted_labels)\n",
        "recall = recall_score(true_labels, predicted_labels)\n",
        "f1 = f1_score(true_labels, predicted_labels)\n",
        "\n",
        "# Print metrics\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"Precision:\", precision)\n",
        "print(\"Recall:\", recall)\n",
        "print(\"F1-score:\", f1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot training and validation accuracy\n",
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Plot training and validation loss\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "6/6 [==============================] - 14s 2s/step\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "\n",
        "# Get model predictions for the test dataset\n",
        "y_pred = model.predict(Xtest)\n",
        "y_pred_classes = np.argmax(y_pred, axis=1)\n",
        "\n",
        "# Compute confusion matrix\n",
        "conf_matrix = confusion_matrix(y_test, y_pred_classes)\n",
        "\n",
        "# Plot confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', \n",
        "            xticklabels=['Fracture', 'Non-Fracture'], \n",
        "            yticklabels=['Fracture', 'Non-Fracture'])\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.ylabel('True Label')\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}