DecisionLevelFusionFinal.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wR_GSEH18Q9O",
        "outputId": "adfbd408-0526-42aa-f9ae-d4b54917409e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0jmgXNE48U2b"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.layers import Input, Conv2D, Dense, Activation, Flatten, BatchNormalization, MaxPooling2D, concatenate, Dropout, Lambda, RepeatVector, GlobalAveragePooling2D\n",
        "from tensorflow.keras.applications import ResNet50\n",
        "from tensorflow.keras.models import Model, Sequential\n",
        "import numpy as np\n",
        "import os\n",
        "import tensorflow as tf\n",
        "import cv2\n",
        "import imutils\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils import shuffle\n",
        "from keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.layers.experimental import preprocessing\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix, roc_auc_score, roc_curve, f1_score, precision_recall_fscore_support, confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "from collections import Counter\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "from sklearn.preprocessing import label_binarize\n",
        "from itertools import cycle\n",
        "from scipy.interpolate import make_interp_spline\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9dle-AgQ8a0n",
        "outputId": "e496c84a-b281-48b2-cd9e-2cdb34046c6c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!unzip /content/drive/MyDrive/DATASET.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DuWL7dRDpr4B"
      },
      "outputs": [],
      "source": [
        "images96 = []\n",
        "vectors = []\n",
        "labels = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FLqfeno_pwep"
      },
      "outputs": [],
      "source": [
        "images64 = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2I9RdTvOpxBh"
      },
      "outputs": [],
      "source": [
        "images229 = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xNdcztjX8dMx"
      },
      "outputs": [],
      "source": [
        "code={\"EOSINOPHIL\":0,\"LYMPHOCYTE\":1,\"MONOCYTE\":2,\"NEUTROPHIL\":3}\n",
        "\n",
        "\n",
        "def findedges(image):\n",
        "    gray = cv2.GaussianBlur(image, (1, 1), 0)\n",
        "    edged = cv2.Canny(gray, 100, 400)\n",
        "    edged = cv2.dilate(edged, None, iterations=1)\n",
        "    edged = cv2.erode(edged, None, iterations=1)\n",
        "    return edged\n",
        "\n",
        "def getimageconturs(edged):\n",
        "    contours = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
        "    contours = imutils.grab_contours(contours)\n",
        "    contours = sorted(contours, key=lambda x: cv2.contourArea(x))\n",
        "    return contours\n",
        "\n",
        "def getboxes(contours,orig):\n",
        "    boxes = []\n",
        "    centers = []\n",
        "    for contour in contours:\n",
        "        box = cv2.minAreaRect(contour)\n",
        "        box = cv2.cv.BoxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box)\n",
        "        box = np.array(box, dtype=\"int\")\n",
        "        (tl, tr, br, bl) = box\n",
        "        if (np.linalg.norm(tl - bl)) > 0 and (np.linalg.norm(tl - tr)) > 0:\n",
        "            boxes.append(box)\n",
        "    return boxes\n",
        "\n",
        "def getcode(n):\n",
        "    if type(n)==str:\n",
        "        for x,y in code.items():\n",
        "            if n==x:\n",
        "                return y\n",
        "    else:\n",
        "        for x,y in code.items():\n",
        "            if n==y:\n",
        "                return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bwOUYiYw8j4X"
      },
      "outputs": [],
      "source": [
        "def load_and_preprocess_data(images_path, path):\n",
        "\n",
        "    for label, folder_path in enumerate(path):\n",
        "        vec_files = os.listdir(folder_path)\n",
        "        for vec_file in vec_files:\n",
        "            filename = vec_file.split(\".\")[0]\n",
        "            class_name = getcode(label)\n",
        "            image_path = f\"{images_path}/{class_name}/{filename}.jpeg\"\n",
        "            vector_path = f\"{folder_path}{vec_file}\"\n",
        "\n",
        "            image = cv2.imread(image_path)\n",
        "            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "            # add padding to the image to better detect cell at the edge\n",
        "            image = cv2.copyMakeBorder(image,10,10,10,10,cv2.BORDER_CONSTANT,value=[198, 203, 208])\n",
        "\n",
        "            #thresholding the image to get the target cell\n",
        "            image1 = cv2.inRange(image,(80, 80, 180),(180, 170, 245))\n",
        "\n",
        "            # openning errosion then dilation\n",
        "            kernel = np.ones((3, 3), np.uint8)\n",
        "            kernel1 = np.ones((5, 5), np.uint8)\n",
        "            img_erosion = cv2.erode(image1, kernel, iterations=2)\n",
        "            image1 = cv2.dilate(img_erosion, kernel1, iterations=5)\n",
        "\n",
        "            #detecting the blood cell\n",
        "            edgedImage = findedges(image1)\n",
        "            edgedContours = getimageconturs(edgedImage)\n",
        "            edgedBoxes =  getboxes(edgedContours, image.copy())\n",
        "\n",
        "            if len(edgedBoxes)==0:\n",
        "                continue\n",
        "            # get the large box and get its cordinate\n",
        "            last = edgedBoxes[-1]\n",
        "            max_x = int(max(last[:,0]))\n",
        "            min_x = int( min(last[:,0]))\n",
        "            max_y = int(max(last[:,1]))\n",
        "            min_y = int(min(last[:,1]))\n",
        "\n",
        "            # draw the contour and fill it\n",
        "            mask = np.zeros_like(image)\n",
        "            cv2.drawContours(mask, edgedContours, len(edgedContours)-1, (255,255,255), -1)\n",
        "\n",
        "            # any pixel but the pixels inside the contour is zero\n",
        "            image[mask==0] = 0\n",
        "\n",
        "            # extract th blood cell\n",
        "            image = image[min_y:max_y, min_x:max_x]\n",
        "\n",
        "            if (np.size(image)==0):\n",
        "                continue\n",
        "            # resize th image\n",
        "\n",
        "            image64 = cv2.resize(image, (64,64))\n",
        "            image64 = image64 / 255.0\n",
        "\n",
        "            images64.append(image64)\n",
        "\n",
        "            image96 = cv2.resize(image, (96,96))\n",
        "            image96 = image96 / 255.0\n",
        "\n",
        "            images96.append(image96)\n",
        "\n",
        "            image229 = cv2.resize(image, (229,229))\n",
        "            image229 = image229 / 255.0\n",
        "\n",
        "            images229.append(image229)\n",
        "\n",
        "            vector = np.loadtxt(vector_path, delimiter=',')\n",
        "\n",
        "\n",
        "            vectors.append(vector)\n",
        "            labels.append(label)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HGnbF1F08svg"
      },
      "outputs": [],
      "source": [
        "test_paths = [\n",
        "    '/content/DATASET/TEST/VECTORS/EOSINOPHIL/',\n",
        "    '/content/DATASET/TEST/VECTORS/LYMPHOCYTE/',\n",
        "    '/content/DATASET/TEST/VECTORS/MONOCYTE/',\n",
        "    '/content/DATASET/TEST/VECTORS/NEUTROPHIL/'\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1gVvjj-d8vn6"
      },
      "outputs": [],
      "source": [
        "load_and_preprocess_data(\"/content/DATASET/TEST/IMAGES\", test_paths)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zb_9300x8zXQ"
      },
      "outputs": [],
      "source": [
        "images64 = np.array(images64, dtype = 'float32')\n",
        "images96 = np.array(images96, dtype = 'float32')\n",
        "images229 = np.array(images229, dtype = 'float32')\n",
        "vectors = np.array(vectors, dtype = 'float32')\n",
        "labels = np.array(labels, dtype = 'int32')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1oCpQPNO809f",
        "outputId": "31779119-2ed6-4c87-b27a-a2520b850598"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2426, 64, 64, 3) (2426, 96, 96, 3) (2426, 229, 229, 3) (2426, 10) (2426,)\n"
          ]
        }
      ],
      "source": [
        "print(images64.shape, images96.shape, images229.shape, vectors.shape, labels.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "owSF_3ppQjFX"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ua0FOQ-T9KaN"
      },
      "outputs": [],
      "source": [
        "resnet = tf.keras.models.load_model('/content/drive/MyDrive/Pretrained Models with knowledge/ResNetPretrainedModelWithKnowledge.h5')\n",
        "densenet = tf.keras.models.load_model('/content/drive/MyDrive/Pretrained Models with knowledge/DenseNetPretrainedModelWithKnowledge.h5')\n",
        "mobilenet = tf.keras.models.load_model('/content/drive/MyDrive/Pretrained Models with knowledge/MobileNetV2PretrainedModelWithKnowledge.h5')\n",
        "vggnet = tf.keras.models.load_model('/content/drive/MyDrive/Pretrained Models with knowledge/VGG16PretrainedModelWithKnowledge.h5')\n",
        "inceptionv3 = tf.keras.models.load_model('/content/drive/MyDrive/Pretrained Models with knowledge/InceptionV3PretrainedModelWithKnowledge.h5')\n",
        "phase1model =  tf.keras.models.load_model('/content/drive/MyDrive/Pretrained Models with knowledge/final_model_v64.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0hZO5Mo2-MG-",
        "outputId": "89a39532-9df6-4e37-8043-cf63ede968b2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "76/76 [==============================] - 6s 33ms/step\n",
            "76/76 [==============================] - 8s 48ms/step\n",
            "76/76 [==============================] - 3s 17ms/step\n",
            "76/76 [==============================] - 5s 46ms/step\n",
            "76/76 [==============================] - 12s 99ms/step\n",
            "76/76 [==============================] - 1s 5ms/step\n"
          ]
        }
      ],
      "source": [
        "preds_resnet_model = resnet.predict([images96, vectors])\n",
        "preds_densenet_model = densenet.predict([images96, vectors])\n",
        "preds_mobilenet_model = mobilenet.predict([images96, vectors])\n",
        "preds_vggnet_model = vggnet.predict([images96, vectors])\n",
        "preds_inceptionv3_model = inceptionv3.predict([images229, vectors])\n",
        "preds_phase1_model = phase1model.predict([images64, vectors])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ChX8YyQCClul"
      },
      "outputs": [],
      "source": [
        "class_preds_model1 = np.argmax(preds_resnet_model, axis=1)\n",
        "class_preds_model2 = np.argmax(preds_densenet_model, axis=1)\n",
        "class_preds_model3 = np.argmax(preds_mobilenet_model, axis=1)\n",
        "class_preds_model4 = np.argmax(preds_vggnet_model, axis=1)\n",
        "class_preds_model5 = np.argmax(preds_inceptionv3_model, axis=1)\n",
        "class_preds_model6 = np.argmax(preds_phase1_model, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nZ629z_w-wL9"
      },
      "outputs": [],
      "source": [
        "# Define weights for each model (replace with your own weights)\n",
        "weights = np.array([0.15, 0.10, 0.10, 0.15, 0.25, 0.25])\n",
        "\n",
        "# Calculate weighted average of class probabilities\n",
        "weighted_avg_probs = (weights[0] * preds_resnet_model +\n",
        "                      weights[1] * preds_densenet_model +\n",
        "                      weights[2] * preds_mobilenet_model +\n",
        "                      weights[3] * preds_vggnet_model +\n",
        "                      weights[4] * preds_inceptionv3_model +\n",
        "                      weights[5] * preds_phase1_model)\n",
        "\n",
        "# Get class predictions from weighted average probabilities\n",
        "weighted_avg_preds = np.argmax(weighted_avg_probs, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WY76Zl8y_grn",
        "outputId": "6f0c9476-f7a6-4149-da56-5c32a4142136"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Weighted Average Accuracy: 0.9929925803792251\n"
          ]
        }
      ],
      "source": [
        "# Calculate accuracy for weighted average predictions\n",
        "weighted_avg_accuracy = accuracy_score(labels, weighted_avg_preds)\n",
        "\n",
        "# print(\"Max Voting Accuracy:\", max_voting_accuracy)\n",
        "print(\"Weighted Average Accuracy:\", weighted_avg_accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(weighted_avg_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jo3OO7TUhazd",
        "outputId": "c304fb3c-920f-4bdd-a859-84e5fa9c1cc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 ... 3 3 3]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(labels, weighted_avg_preds)\n",
        "\n",
        "# Define class labels\n",
        "classes = ['Eosinophil', 'Lymphocyte', 'Monocyte', 'Neutrophil']\n",
        "\n",
        "# Plot confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=classes, yticklabels=classes)\n",
        "plt.xlabel('Predicted labels')\n",
        "plt.ylabel('True labels')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "WhTD0G9lvUSw",
        "outputId": "dcaa58c7-7a89-4af2-ef11-67f92d60f41b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8I59bCxsz8Vd"
      },
      "outputs": [],
      "source": [
        "\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}