Chest_X_ray_NIH_dataset.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/chrismayemba/Chest_XRay_Model/blob/main/Chest_X_ray_NIH_dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r7FtuMNCl-9P",
        "outputId": "a0c03e5e-bbfc-4464-d531-df60cb1ace06"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "from google.colab import drive\n",
        "\n",
        "# Montez Google Drive\n",
        "drive.mount('/content/drive')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "QkO1bjodl_0B"
      },
      "outputs": [],
      "source": [
        "# Chemin vers le répertoire où les images sont stockées\n",
        "image_dir = '/content/drive/MyDrive/NIH Chest X-ray/sample/images'\n",
        "\n",
        "# Chemin vers le fichier CSV des labels\n",
        "label_file = '/content/drive/MyDrive/NIH Chest X-ray/sample_labels.csv'\n",
        "\n",
        "def load_images_and_labels(image_dir, label_file):\n",
        "    labels_df = pd.read_csv(label_file)\n",
        "    images = []\n",
        "    labels = []\n",
        "\n",
        "    for _, row in labels_df.iterrows():\n",
        "        img_path = os.path.join(image_dir, row['Image Index'])\n",
        "        if os.path.exists(img_path):\n",
        "            img = load_img(img_path, target_size=(128, 128), color_mode='grayscale')\n",
        "            img_array = img_to_array(img)\n",
        "            images.append(img_array)\n",
        "            labels.append(row['Finding Labels'])\n",
        "        else:\n",
        "            print(f\"Fichier non trouvé : {img_path}\")\n",
        "\n",
        "    return np.array(images), np.array(labels)\n",
        "\n",
        "images, labels = load_images_and_labels(image_dir, label_file)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "wnzbGEVYoxQb"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.2, random_state=42)\n",
        "\n",
        "# Reshape data for the CNN\n",
        "X_train = X_train.reshape(-1, 128, 128, 1)\n",
        "X_test = X_test.reshape(-1, 128, 128, 1)\n",
        "\n",
        "# Normalize data\n",
        "X_train = X_train / 255.0\n",
        "X_test = X_test / 255.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "b4-uN_gLo3RL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94d7f192-9b29-4967-f910-2f5449351b47"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "113/113 [==============================] - 130s 1s/step - loss: 0.0080 - accuracy: 0.9944 - val_loss: 0.0000e+00 - val_accuracy: 1.0000\n",
            "Epoch 2/10\n",
            "113/113 [==============================] - 125s 1s/step - loss: 5.5384e-38 - accuracy: 1.0000 - val_loss: 0.0000e+00 - val_accuracy: 1.0000\n",
            "Epoch 3/10\n",
            "113/113 [==============================] - 123s 1s/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.0000e+00 - val_accuracy: 1.0000\n",
            "Epoch 4/10\n",
            "113/113 [==============================] - 126s 1s/step - loss: 5.6053e-38 - accuracy: 1.0000 - val_loss: 0.0000e+00 - val_accuracy: 1.0000\n",
            "Epoch 5/10\n",
            "113/113 [==============================] - 125s 1s/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.0000e+00 - val_accuracy: 1.0000\n",
            "Epoch 6/10\n",
            "113/113 [==============================] - 117s 1s/step - loss: 5.4858e-34 - accuracy: 1.0000 - val_loss: 0.0000e+00 - val_accuracy: 1.0000\n",
            "Epoch 7/10\n",
            "113/113 [==============================] - 124s 1s/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.0000e+00 - val_accuracy: 1.0000\n",
            "Epoch 8/10\n",
            "113/113 [==============================] - 123s 1s/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.0000e+00 - val_accuracy: 1.0000\n",
            "Epoch 9/10\n",
            "113/113 [==============================] - 124s 1s/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.0000e+00 - val_accuracy: 1.0000\n",
            "Epoch 10/10\n",
            "113/113 [==============================] - 123s 1s/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.0000e+00 - val_accuracy: 1.0000\n"
          ]
        }
      ],
      "source": [
        "# Build the CNN model\n",
        "model = Sequential([\n",
        "    Conv2D(32, (3, 3), activation='relu', input_shape=(128, 128, 1)),\n",
        "    MaxPooling2D((2, 2)),\n",
        "    Conv2D(64, (3, 3), activation='relu'),\n",
        "    MaxPooling2D((2, 2)),\n",
        "    Conv2D(128, (3, 3), activation='relu'),\n",
        "    MaxPooling2D((2, 2)),\n",
        "    Flatten(),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dropout(0.5),\n",
        "    Dense(1, activation='sigmoid')  # Sigmoid activation for binary classification\n",
        "])\n",
        "\n",
        "# Convert labels to numerical format (assuming 'Finding Labels' contains strings like 'Yes' or 'No')\n",
        "y_train_numerical = np.where(y_train == 'Yes', 1, 0)\n",
        "y_test_numerical = np.where(y_test == 'Yes', 1, 0)\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "history = model.fit(X_train, y_train_numerical, epochs=10, batch_size=32, validation_split=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "G_n7hYjPo_hM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a487d257-f51e-44f3-829c-d5d9225f1ba4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "36/36 [==============================] - 15s 412ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
            "Loss: 0.0, Accuracy: 1.0\n",
            "36/36 [==============================] - 9s 250ms/step\n",
            "Confusion Matrix:\n",
            "[[1122]]\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      1122\n",
            "\n",
            "    accuracy                           1.00      1122\n",
            "   macro avg       1.00      1.00      1.00      1122\n",
            "weighted avg       1.00      1.00      1.00      1122\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Evaluate the model\n",
        "loss, accuracy = model.evaluate(X_test, y_test_numerical) # Use y_test_numerical instead of y_test\n",
        "print(f'Loss: {loss}, Accuracy: {accuracy}')\n",
        "\n",
        "# Predictions\n",
        "y_pred = (model.predict(X_test) > 0.5).astype(\"int32\")\n",
        "\n",
        "print('Confusion Matrix:')\n",
        "print(confusion_matrix(y_test_numerical, y_pred)) # Use y_test_numerical for confusion matrix\n",
        "print('Classification Report:')\n",
        "print(classification_report(y_test_numerical, y_pred)) # Use y_test_numerical for classification report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "VoZhDQ3WqfXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6fb8ddfc-89c4-41c6-f4e2-c36da2cad468"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
            "Installing collected packages: xxhash, requests, pyarrow, dill, multiprocess, datasets\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.31.0\n",
            "    Uninstalling requests-2.31.0:\n",
            "      Successfully uninstalled requests-2.31.0\n",
            "  Attempting uninstall: pyarrow\n",
            "    Found existing installation: pyarrow 14.0.2\n",
            "    Uninstalling pyarrow-14.0.2:\n",
            "      Successfully uninstalled pyarrow-14.0.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 16.1.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-2.20.0 dill-0.3.8 multiprocess-0.70.16 pyarrow-16.1.0 requests-2.32.3 xxhash-3.4.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pyarrow",
                  "requests"
                ]
              },
              "id": "c2d7beb73801476591b1a3c6a245bf20"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip install transformers datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "QLnb4xZVqh8Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "b7222e9b-54e3-45ae-ed71-9f3baef9d1b7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'load_img' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-f2c6f448db55>\u001b[0m in \u001b[0;36m<cell line: 35>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     33\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m \u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_images_and_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-2-f2c6f448db55>\u001b[0m in \u001b[0;36mload_images_and_labels\u001b[0;34m(image_dir, label_file)\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0mimg_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Image Index'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m             \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_img\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m128\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor_mode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'grayscale'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m             \u001b[0mimg_array\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg_to_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m             \u001b[0mimages\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_array\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'load_img' is not defined"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "from transformers import TrainingArguments, Trainer, AutoModelForSequenceClassification, AutoTokenizer\n",
        "from datasets import load_dataset, Dataset\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Mont Google Drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Chemin vers le répertoire où les images sont stockées\n",
        "image_dir = '/content/drive/MyDrive/NIH Chest X-ray/sample/images'\n",
        "\n",
        "# Chemin vers le fichier CSV des labels\n",
        "label_file = '/content/drive/MyDrive/NIH Chest X-ray/sample_labels.csv'\n",
        "\n",
        "def load_images_and_labels(image_dir, label_file):\n",
        "    labels_df = pd.read_csv(label_file)\n",
        "    images = []\n",
        "    labels = []\n",
        "\n",
        "    for _, row in labels_df.iterrows():\n",
        "        img_path = os.path.join(image_dir, row['Image Index'])\n",
        "        if os.path.exists(img_path):\n",
        "            img = load_img(img_path, target_size=(128, 128), color_mode='grayscale')\n",
        "            img_array = img_to_array(img)\n",
        "            images.append(img_array)\n",
        "            labels.append(row['Finding Labels'])\n",
        "        else:\n",
        "            print(f\"Fichier non trouvé : {img_path}\")\n",
        "\n",
        "    return np.array(images), np.array(labels)\n",
        "\n",
        "images, labels = load_images_and_labels(image_dir, label_file)\n",
        "X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.2, random_state=42)\n",
        "\n",
        "# Convertir les données en dataset Hugging Face\n",
        "train_data = Dataset.from_dict({\"image\": X_train, \"label\": y_train})\n",
        "test_data = Dataset.from_dict({\"image\": X_test, \"label\": y_test})\n",
        "\n",
        "# Charger le modèle et le tokenizer\n",
        "model_name = \"togethercomputer/dragonfly_med\"\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "# Préparer les données pour l'entraînement\n",
        "def preprocess_function(examples):\n",
        "    return tokenizer(examples[\"image\"], padding=\"max_length\", truncation=True)\n",
        "\n",
        "train_dataset = train_data.map(preprocess_function, batched=True)\n",
        "test_dataset = test_data.map(preprocess_function, batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YVh3woNSqnM5"
      },
      "outputs": [],
      "source": [
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./results\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=16,\n",
        "    num_train_epochs=3,\n",
        "    weight_decay=0.01,\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=test_dataset,\n",
        ")\n",
        "\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8-JuUjdhqtkS"
      },
      "outputs": [],
      "source": [
        "results = trainer.evaluate()\n",
        "print(results)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNmwKhzNZ9lRC8d4NYBYAIc",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}