CNN_Classifier_Subset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detection and Classification of Thorax Diseases on NIH Chest Xray Dataset ( CNNs ) - Subset\n",
    "\n",
    "*This subset is only for demo `Contains: 1000 samples only`*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.preprocessing import image\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tqdm import tqdm\n",
    "from keras.layers import BatchNormalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMG_DIR = './Datasets/images-small/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (1000, 16)\n"
     ]
    },
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Image</th>\n",
       "      <th>Atelectasis</th>\n",
       "      <th>Cardiomegaly</th>\n",
       "      <th>Consolidation</th>\n",
       "      <th>Edema</th>\n",
       "      <th>Effusion</th>\n",
       "      <th>Emphysema</th>\n",
       "      <th>Fibrosis</th>\n",
       "      <th>Hernia</th>\n",
       "      <th>Infiltration</th>\n",
       "      <th>Mass</th>\n",
       "      <th>Nodule</th>\n",
       "      <th>PatientId</th>\n",
       "      <th>Pleural_Thickening</th>\n",
       "      <th>Pneumonia</th>\n",
       "      <th>Pneumothorax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "              Image  Atelectasis  Cardiomegaly  Consolidation  Edema  \\\n",
       "0  00008270_015.png            0             0              0      0   \n",
       "1  00029855_001.png            1             0              0      0   \n",
       "2  00001297_000.png            0             0              0      0   \n",
       "\n",
       "   Effusion  Emphysema  Fibrosis  Hernia  Infiltration  Mass  Nodule  \\\n",
       "0         0          0         0       0             0     0       0   \n",
       "1         1          0         0       0             1     0       0   \n",
       "2         0          0         0       0             0     0       0   \n",
       "\n",
       "   PatientId  Pleural_Thickening  Pneumonia  Pneumothorax  \n",
       "0       8270                   0          0             0  \n",
       "1      29855                   0          0             0  \n",
       "2       1297                   1          0             0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_small = pd.read_csv('Datasets/train-subset.csv')\n",
    "print(f'Shape: {df_small.shape}')\n",
    "df_small.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Image Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:06<00:00, 156.58it/s]\n"
     ]
    }
   ],
   "source": [
    "SIZE = 256\n",
    "X_dataset = []  \n",
    "for i in tqdm(range(df_small.shape[0])):\n",
    "    img = image.load_img(IMG_DIR + df_small['Image'][i], target_size=(SIZE,SIZE,3))\n",
    "    img = image.img_to_array(img)\n",
    "    img = img/255.\n",
    "    X_dataset.append(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert Images, Labels to numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image Shape :(1000, 256, 256, 3)\n",
      "Label Shape :(1000, 14)\n"
     ]
    }
   ],
   "source": [
    "X_dataset = np.array(X_dataset)\n",
    "# print(df['Image'][999])\n",
    "\n",
    "y_dataset = np.array(df_small.drop(['Image', 'PatientId'], axis=1))\n",
    "\n",
    "print(f'Image Shape :{X_dataset.shape}')\n",
    "print(f'Label Shape :{y_dataset.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train-Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Shape :((700, 256, 256, 3), (700, 14))\n",
      "Test Shape :((300, 256, 256, 3), (300, 14))\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_dataset, y_dataset, random_state=20, test_size=0.3)\n",
    "\n",
    "print(f'Train Shape :{X_train.shape, y_train.shape}')\n",
    "print(f'Test Shape :{X_test.shape, y_test.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 252, 252, 16)      1216      \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 252, 252, 16)      64        \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 126, 126, 16)      0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 126, 126, 16)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 122, 122, 32)      12832     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 61, 61, 32)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 61, 61, 32)        128       \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 61, 61, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 57, 57, 64)        51264     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 28, 28, 64)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 28, 28, 64)        256       \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 28, 28, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 24, 24, 64)        102464    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 12, 12, 64)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 12, 12, 64)        256       \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 12, 12, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 9216)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               1179776   \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 14)                910       \n",
      "=================================================================\n",
      "Total params: 1,357,422\n",
      "Trainable params: 1,357,070\n",
      "Non-trainable params: 352\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(filters=16, kernel_size=(5, 5), activation=\"relu\", input_shape=(SIZE,SIZE,3)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Conv2D(filters=32, kernel_size=(5, 5), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Conv2D(filters=64, kernel_size=(5, 5), activation=\"relu\"))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Conv2D(filters=64, kernel_size=(5, 5), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(14, activation='sigmoid'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "11/11 [==============================] - 25s 2s/step - loss: 0.7880 - accuracy: 0.0687 - val_loss: 0.5913 - val_accuracy: 0.0233\n",
      "Epoch 2/15\n",
      "11/11 [==============================] - 27s 2s/step - loss: 0.4623 - accuracy: 0.0924 - val_loss: 0.5143 - val_accuracy: 0.0567\n",
      "Epoch 3/15\n",
      "11/11 [==============================] - 27s 2s/step - loss: 0.3803 - accuracy: 0.1326 - val_loss: 0.3623 - val_accuracy: 0.3800\n",
      "Epoch 4/15\n",
      "11/11 [==============================] - 27s 2s/step - loss: 0.3457 - accuracy: 0.1215 - val_loss: 0.2602 - val_accuracy: 0.1133\n",
      "Epoch 5/15\n",
      "11/11 [==============================] - 27s 2s/step - loss: 0.3146 - accuracy: 0.1333 - val_loss: 0.2913 - val_accuracy: 0.5100\n",
      "Epoch 6/15\n",
      "11/11 [==============================] - 27s 2s/step - loss: 0.2909 - accuracy: 0.1487 - val_loss: 0.2731 - val_accuracy: 0.5333\n",
      "Epoch 7/15\n",
      "11/11 [==============================] - 27s 2s/step - loss: 0.2675 - accuracy: 0.1398 - val_loss: 0.2359 - val_accuracy: 0.5067\n",
      "Epoch 8/15\n",
      "11/11 [==============================] - 27s 2s/step - loss: 0.2505 - accuracy: 0.1286 - val_loss: 0.1936 - val_accuracy: 0.4833\n",
      "Epoch 9/15\n",
      "11/11 [==============================] - 27s 2s/step - loss: 0.2547 - accuracy: 0.1689 - val_loss: 0.2074 - val_accuracy: 0.5100\n",
      "Epoch 10/15\n",
      "11/11 [==============================] - 27s 2s/step - loss: 0.2445 - accuracy: 0.1890 - val_loss: 0.2064 - val_accuracy: 0.1000\n",
      "Epoch 11/15\n",
      "11/11 [==============================] - 27s 2s/step - loss: 0.2617 - accuracy: 0.1434 - val_loss: 0.1888 - val_accuracy: 0.1500\n",
      "Epoch 12/15\n",
      "11/11 [==============================] - 27s 2s/step - loss: 0.2495 - accuracy: 0.1520 - val_loss: 0.1878 - val_accuracy: 0.1000\n",
      "Epoch 13/15\n",
      "11/11 [==============================] - 27s 2s/step - loss: 0.2337 - accuracy: 0.1781 - val_loss: 0.1865 - val_accuracy: 0.1033\n",
      "Epoch 14/15\n",
      "11/11 [==============================] - 27s 2s/step - loss: 0.2344 - accuracy: 0.1763 - val_loss: 0.1926 - val_accuracy: 0.0967\n",
      "Epoch 15/15\n",
      "11/11 [==============================] - 26s 2s/step - loss: 0.2376 - accuracy: 0.1566 - val_loss: 0.2216 - val_accuracy: 0.1167\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs=15, validation_data=(X_test, y_test), batch_size=64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation (Loss & Accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x----------- Loss  ----------- x\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x----------- Accuracy ----------- x\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f80500abee0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('x----------- Loss  ----------- x')\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "epochs = range(1, len(loss) + 1)\n",
    "plt.plot(epochs, loss, 'y', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "print('x----------- Accuracy ----------- x')\n",
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "plt.plot(epochs, acc, 'y', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'r', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference | Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mass (0.329)\n",
      "Emphysema (0.321)\n",
      "Nodule (0.248)\n",
      "Cardiomegaly (0.24)\n",
      "PatientId (0.233)\n",
      "Pneumothorax (0.212)\n",
      "Hernia (0.178)\n",
      "Consolidation (0.168)\n",
      "Fibrosis (0.164)\n",
      "Effusion (0.164)\n",
      "10/10 [==============================] - 2s 152ms/step - loss: 0.2216 - accuracy: 0.1167\n",
      "Accuracy =  11.666666716337204 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cardiomegaly : Enlarged condition of Heart\n",
    "img = image.load_img('Datasets/00000001_000.png', target_size=(SIZE,SIZE,3))\n",
    "\n",
    "img = image.img_to_array(img)\n",
    "img = img/255.\n",
    "plt.imshow(img)\n",
    "img = np.expand_dims(img, axis=0)\n",
    "\n",
    "classes = np.array(df_small.columns[2:]) #Get array of all classes\n",
    "proba = model.predict(img)  #Get probabilities for each class\n",
    "sorted_categories = np.argsort(proba[0])[:-11:-1]  #Get class names for top 10 categories\n",
    "\n",
    "#Print classes and corresponding probabilities\n",
    "for i in range(10):\n",
    "    print(\"{}\".format(classes[sorted_categories[i]])+\" ({:.3})\".format(proba[0][sorted_categories[i]]))\n",
    "\n",
    "_, acc = model.evaluate(X_test, y_test)\n",
    "print(\"Accuracy = \", (acc * 100.0), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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