├── Testing Results.png ├── README.md ├── 02_Dataset_Preparation.ipynb ├── 05_FPR_Dataset_Creation.ipynb └── 04_FPR_Candidate_ROI_extraction.ipynb /Testing Results.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/abhikrm0102/Lung-Nodules-Detection-and-Classification-using-UNet-DenseNet/HEAD/Testing Results.png -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Lung-Nodules-Detection-and-Classification-using-UNet-DenseNet 2 | 3 | Achieved an accuracy of 99.96 % on Luna-16 dataset . 4 | 5 | Problem Statement 6 | 7 | Lung Nodule detection using Deep Learning 8 | The detection of lung cancer stands as a critical global health priority, emphasizing the significance of early identification for improved patient outcomes. Recent advancements in medical imaging have showcased the exceptional potential of deep learning techniques in addressing this concern. This study delves into the exploration of U-Net and DenseNet, two influential deep learning models, for the task of lung cancer detection using medical imaging data. The proposed methodology harnesses U-Net, a convolutional neural network (CNN) known for its adeptness in semantic segmentation, and DenseNet, a hybrid architecture characterized by dense connections among layers, to automate lung cancer detection from 3D computed tomography (CT) scans. 9 | 10 | •Project Scope 11 | 1. Develop a machine learning (ML) model for lung cancer detection using U-Net and DenseNet architectures. 12 | 2. Achieve an accuracy of at least 99.96% in lung nodule detection and classification. 13 | 3. Achieved validation of 99.9%. 14 | 5. Package the model into a user-friendly web application accessible to healthcare professionals. 15 | 6. Integrate the model seamlessly with existing medical imaging workflows for efficient clinical use. 16 | 17 | 18 | • Data Requirements: 19 | 1. Large dataset of CT scans annotated with lung nodules: 20 | 2. Minimum of 10,000 CT scans with diverse patient demographics. 21 | 3. Each CT scan is labeled with individual nodules, categorized as benign or malignant. 22 | 4. Standard format compatible with deep learning models (e.g., DICOM, NIfTI). 23 | 5. Additional clinical data (optional): 24 | 6. Patient demographics (age, gender, smoking history). 25 | 7. Histopathological diagnosis for confirmed cases. 26 | 8. Treatment history and follow-up data. 27 | 9. Training and Evaluation Methodology. 28 | 29 | • Data Preprocessing: 30 | 31 | 1. Standardize CT scans to a specific resolution and voxel size. 32 | 2. Apply normalization techniques to adjust intensity values. 33 | 3. Segment lung regions and isolate individual nodules using image processing techniques. 34 | 4. Augment the data (optional) to increase model generalizability by adding artificial variations. 35 | 5. Split the data into training, validation, and testing sets. 36 | 37 | 38 | • Model Training: 39 | 40 | 1. Combine U-Net architecture for nodule segmentation with DenseNet architecture for classification. 41 | 2. Fine-tune the pre-trained U-Net and DenseNet models on the lung nodule dataset. 42 | 3. Utilize appropriate loss functions tailored to nodule segmentation and classification. 43 | 4. Optimize hyperparameters (learning rate, optimizer settings) for optimal performance. 44 | 5. Monitor training progress on the validation set and adjust parameters accordingly. 45 | 46 | 47 | 48 | • Model Evaluation: 49 | 50 | 1. Evaluate the trained model's performance on the testing set. 51 | 2. Calculate accuracy, sensitivity, specificity, and other relevant metrics. 52 | 3. Analyze the model's strengths and weaknesses, focusing on false positives and negatives. 53 | 4. Utilize visualization techniques and saliency maps to understand the decision-making process. 54 | 55 | 56 | • Model Optimization: 57 | 58 | 1. Implement techniques like hyperparameter tuning and data augmentation to improve performance. 59 | 2. Explore ensemble learning approaches by combining multiple models for enhanced accuracy. 60 | 3. Consider incorporating explainability methods like SHAP values for transparency and trust. 61 | 62 | ![Testing Results](https://github.com/abhikrm0102/Lung-Nodules-Detection-and-Classification-using-UNet-DenseNet/assets/153391038/4fb45f12-7f7d-479e-8f96-cd2c0b1c7c58) 63 | 64 | -------------------------------------------------------------------------------- /02_Dataset_Preparation.ipynb: -------------------------------------------------------------------------------- 1 | {"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyNyfdCYIpFKjPd5BStf250E"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"id":"-0R1DOosVj7k","executionInfo":{"status":"ok","timestamp":1701349641509,"user_tz":-330,"elapsed":5,"user":{"displayName":"Mohit","userId":"02249881747218603498"}}},"outputs":[],"source":["import numpy as np\n","import os\n","import random\n","from tqdm import tqdm\n","from IPython.display import clear_output\n"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/gdrive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nXxKTXJOVokV","executionInfo":{"status":"ok","timestamp":1701349696669,"user_tz":-330,"elapsed":26642,"user":{"displayName":"Mohit","userId":"02249881747218603498"}},"outputId":"197ae3f8-b0e6-420e-839a-aa0b6ba7d412"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive\n"]}]},{"cell_type":"code","source":["lung_names = os.listdir(\"/content/gdrive/MyDrive/archive/processed/lungs_roi\")\n","random.shuffle(lung_names)"],"metadata":{"id":"Fd6zJwqwV7a3","executionInfo":{"status":"ok","timestamp":1701349821824,"user_tz":-330,"elapsed":586,"user":{"displayName":"Mohit","userId":"02249881747218603498"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["n = len(lung_names)\n","s = int(n*.8)\n","train_lung_names = lung_names[:s]\n","test_lung_names = lung_names[s:]\n","print(len(train_lung_names))\n","print(len(test_lung_names))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WcpBpI0SWWu_","executionInfo":{"status":"ok","timestamp":1701349832773,"user_tz":-330,"elapsed":14,"user":{"displayName":"Mohit","userId":"02249881747218603498"}},"outputId":"53adfb7f-3739-458a-9313-d6cbe7d19749"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["180\n","46\n"]}]},{"cell_type":"code","source":["trainX = []\n","trainY = []\n","\n","for lname in tqdm(train_lung_names):\n"," mname = lname.replace(\"lungs\",\"masks\")\n"," lung = np.load(\"/content/gdrive/MyDrive/archive/processed/lungs_roi/\"+lname)\n"," mask = np.load(\"/content/gdrive/MyDrive/archive/processed/nodule_mask/\"+mname)\n"," trainX.append(lung)\n"," trainY.append(mask)\n","\n","trainX = np.array(trainX, dtype=np.uint8)\n","trainY = np.array(trainY, dtype=np.uint8)\n","\n","np.save(\"trainX.npy\", trainX)\n","np.save(\"trainY.npy\", trainY)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ga9Sin3VWjL_","executionInfo":{"status":"ok","timestamp":1701350051384,"user_tz":-330,"elapsed":1329,"user":{"displayName":"Mohit","userId":"02249881747218603498"}},"outputId":"99c526a1-1b8f-4320-b9d4-29634a02a29c"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 180/180 [00:01<00:00, 172.78it/s]\n"]}]},{"cell_type":"code","source":["testX = []\n","testY = []\n","\n","for lname in tqdm(test_lung_names):\n"," mname = lname.replace(\"lungs\",\"masks\")\n"," lung = np.load(\"/content/gdrive/MyDrive/archive/processed/lungs_roi/\"+lname)\n"," mask = np.load(\"/content/gdrive/MyDrive/archive/processed/nodule_mask/\"+mname)\n"," testX.append(lung)\n"," testY.append(mask)\n","\n","testX = np.array(testX, dtype=np.uint8)\n","testY = np.array(testY, dtype=np.uint8)\n","\n","np.save(\"testX.npy\", testX)\n","np.save(\"testY.npy\", testY)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xu5DpbhvWnh_","executionInfo":{"status":"ok","timestamp":1701350122566,"user_tz":-330,"elapsed":11018,"user":{"displayName":"Mohit","userId":"02249881747218603498"}},"outputId":"ab9a6630-79fc-473a-a334-56cd843d8bda"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 46/46 [00:11<00:00, 4.18it/s]\n"]}]},{"cell_type":"code","source":["!cp trainX.npy /content/gdrive/MyDrive/archive/ProcessedData/trainX.npy\n","!cp trainY.npy /content/gdrive/MyDrive/archive/ProcessedData/trainY.npy\n","\n","!cp testX.npy /content/gdrive/MyDrive/archive/ProcessedData/testX.npy\n","!cp testY.npy /content/gdrive/MyDrive/archive/ProcessedData/testY.npy"],"metadata":{"id":"MwLbNhkCXi3I","executionInfo":{"status":"ok","timestamp":1701350245074,"user_tz":-330,"elapsed":817,"user":{"displayName":"Mohit","userId":"02249881747218603498"}}},"execution_count":14,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"CX-1UNZoYHrh"},"execution_count":null,"outputs":[]}]} -------------------------------------------------------------------------------- /05_FPR_Dataset_Creation.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [] 7 | }, 8 | "kernelspec": { 9 | "name": "python3", 10 | "display_name": "Python 3" 11 | }, 12 | "language_info": { 13 | "name": "python" 14 | }, 15 | "accelerator": "TPU" 16 | }, 17 | "cells": [ 18 | { 19 | "cell_type": "code", 20 | "execution_count": null, 21 | "metadata": { 22 | "id": "3-Zj0IG5019A" 23 | }, 24 | "outputs": [], 25 | "source": [ 26 | "import random\n", 27 | "import os\n", 28 | "import shutil\n", 29 | "from tqdm import tqdm" 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "source": [ 35 | "from google.colab import drive\n", 36 | "drive.mount('/content/gdrive')" 37 | ], 38 | "metadata": { 39 | "colab": { 40 | "base_uri": "https://localhost:8080/" 41 | }, 42 | "id": "qkyIgCoy08Cw", 43 | "outputId": "0dab0de4-b1d8-4909-ade9-57d681f3df31" 44 | }, 45 | "execution_count": null, 46 | "outputs": [ 47 | { 48 | "output_type": "stream", 49 | "name": "stdout", 50 | "text": [ 51 | "Mounted at /content/gdrive\n" 52 | ] 53 | } 54 | ] 55 | }, 56 | { 57 | "cell_type": "code", 58 | "source": [ 59 | "root = \"/content/gdrive/MyDrive/FPRProcessed/non-nodule-initial/\"\n", 60 | "target = \"/content/gdrive/MyDrive/FPRProcessed/non-nodule/\"" 61 | ], 62 | "metadata": { 63 | "id": "ayMPxDpZ1AFH" 64 | }, 65 | "execution_count": null, 66 | "outputs": [] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "source": [ 71 | "filelist = os.listdir(root)\n" 72 | ], 73 | "metadata": { 74 | "id": "-b76LU2t1Nd0" 75 | }, 76 | "execution_count": null, 77 | "outputs": [] 78 | }, 79 | { 80 | "cell_type": "code", 81 | "source": [ 82 | "len(filelist)\n" 83 | ], 84 | "metadata": { 85 | "colab": { 86 | "base_uri": "https://localhost:8080/" 87 | }, 88 | "id": "LMUPrAC61Pn1", 89 | "outputId": "b132fb59-8b0d-47c0-e42c-52c2b044f9ea" 90 | }, 91 | "execution_count": null, 92 | "outputs": [ 93 | { 94 | "output_type": "execute_result", 95 | "data": { 96 | "text/plain": [ 97 | "4275" 98 | ] 99 | }, 100 | "metadata": {}, 101 | "execution_count": 18 102 | } 103 | ] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "source": [ 108 | "filelist[:10]\n" 109 | ], 110 | "metadata": { 111 | "colab": { 112 | "base_uri": "https://localhost:8080/" 113 | }, 114 | "id": "Mo4-rxaI1RmZ", 115 | "outputId": "e8930ee3-2f66-43d3-a98a-0f8b8592d7f9" 116 | }, 117 | "execution_count": null, 118 | "outputs": [ 119 | { 120 | "output_type": "execute_result", 121 | "data": { 122 | "text/plain": [ 123 | "['candidate_6_0_32979.jpg',\n", 124 | " 'candidate_6_0_32980.jpg',\n", 125 | " 'candidate_6_0_32981.jpg',\n", 126 | " 'candidate_6_0_32982.jpg',\n", 127 | " 'candidate_6_0_32983.jpg',\n", 128 | " 'candidate_6_0_32984.jpg',\n", 129 | " 'candidate_6_0_32985.jpg',\n", 130 | " 'candidate_6_0_32986.jpg',\n", 131 | " 'candidate_6_0_32987.jpg',\n", 132 | " 'candidate_6_0_32988.jpg']" 133 | ] 134 | }, 135 | "metadata": {}, 136 | "execution_count": 19 137 | } 138 | ] 139 | }, 140 | { 141 | "cell_type": "code", 142 | "source": [ 143 | "random.shuffle(filelist)\n" 144 | ], 145 | "metadata": { 146 | "id": "GnmoB5sr1Ykj" 147 | }, 148 | "execution_count": null, 149 | "outputs": [] 150 | }, 151 | { 152 | "cell_type": "code", 153 | "source": [ 154 | "filelist[:10]\n" 155 | ], 156 | "metadata": { 157 | "colab": { 158 | "base_uri": "https://localhost:8080/" 159 | }, 160 | "id": "au8pP8mI1kGr", 161 | "outputId": "f8eac114-892d-4f88-db1d-fccab399fc25" 162 | }, 163 | "execution_count": null, 164 | "outputs": [ 165 | { 166 | "output_type": "execute_result", 167 | "data": { 168 | "text/plain": [ 169 | "['candidate_6_0_31815.jpg',\n", 170 | " 'candidate_6_0_12679.jpg',\n", 171 | " 'candidate_6_0_32353.jpg',\n", 172 | " 'candidate_6_0_30111.jpg',\n", 173 | " 'candidate_6_0_29845.jpg',\n", 174 | " 'candidate_6_0_41185.jpg',\n", 175 | " 'candidate_6_0_12314.jpg',\n", 176 | " 'candidate_6_0_32330.jpg',\n", 177 | " 'candidate_6_0_31818.jpg',\n", 178 | " 'candidate_6_0_12693.jpg']" 179 | ] 180 | }, 181 | "metadata": {}, 182 | "execution_count": 21 183 | } 184 | ] 185 | }, 186 | { 187 | "cell_type": "code", 188 | "source": [ 189 | "# Number of non nodule images is too high compared to the number of nodule images.\n", 190 | "# Therefore, we will use only 2000 non nodule images.\n", 191 | "finalfiles = filelist[:2000]\n", 192 | "for file in tqdm(finalfiles, total=2000):\n", 193 | " s = root+file\n", 194 | " d = target+file\n", 195 | " shutil.copy(s,d)" 196 | ], 197 | "metadata": { 198 | "colab": { 199 | "base_uri": "https://localhost:8080/" 200 | }, 201 | "id": "SNKvVYYu1l_v", 202 | "outputId": "43411bb1-93ff-45f0-ff56-f9ee80065780" 203 | }, 204 | "execution_count": null, 205 | "outputs": [ 206 | { 207 | "output_type": "stream", 208 | "name": "stderr", 209 | "text": [ 210 | "100%|██████████| 2000/2000 [00:58<00:00, 34.33it/s] \n" 211 | ] 212 | } 213 | ] 214 | }, 215 | { 216 | "cell_type": "code", 217 | "source": [ 218 | "os.chdir(\"/content/gdrive/MyDrive\")\n", 219 | "os.mkdir(\"FPRDataset\")\n", 220 | "os.mkdir(\"FPRDataset/train\")\n", 221 | "os.mkdir(\"FPRDataset/train/nodule\")\n", 222 | "os.mkdir(\"FPRDataset/train/non-nodule\")\n", 223 | "os.mkdir(\"FPRDataset/test\")\n", 224 | "os.mkdir(\"FPRDataset/test/nodule\")\n", 225 | "os.mkdir(\"FPRDataset/test/non-nodule\")" 226 | ], 227 | "metadata": { 228 | "id": "BKsdNFNh13Ut" 229 | }, 230 | "execution_count": null, 231 | "outputs": [] 232 | }, 233 | { 234 | "cell_type": "code", 235 | "source": [ 236 | "nodule_files = os.listdir(\"/content/gdrive/MyDrive/FPRProcessed/nodule/\")\n", 237 | "non_nodule_files = os.listdir(\"/content/gdrive/MyDrive/FPRProcessed/non-nodule/\")" 238 | ], 239 | "metadata": { 240 | "id": "IOgJNMuQ2QYU" 241 | }, 242 | "execution_count": null, 243 | "outputs": [] 244 | }, 245 | { 246 | "cell_type": "code", 247 | "source": [ 248 | "random.shuffle(nodule_files)\n", 249 | "random.shuffle(non_nodule_files)" 250 | ], 251 | "metadata": { 252 | "id": "c_DyfHa82V9c" 253 | }, 254 | "execution_count": null, 255 | "outputs": [] 256 | }, 257 | { 258 | "cell_type": "code", 259 | "source": [ 260 | "n = len(nodule_files)\n", 261 | "split = int(0.75*n)\n", 262 | "for i in nodule_files[:split]:\n", 263 | " shutil.copy(f\"/content/gdrive/MyDrive/FPRProcessed/nodule/{i}\", f\"/content/gdrive/MyDrive/FPRDataset/train/nodule/{i}\")\n", 264 | "for i in nodule_files[split:]:\n", 265 | " shutil.copy(f\"/content/gdrive/MyDrive/FPRProcessed/nodule/{i}\", f\"/content/gdrive/MyDrive/FPRDataset/test/nodule/{i}\")" 266 | ], 267 | "metadata": { 268 | "id": "rcAfa2932Yd1" 269 | }, 270 | "execution_count": null, 271 | "outputs": [] 272 | }, 273 | { 274 | "cell_type": "code", 275 | "source": [ 276 | "n = len(non_nodule_files)\n", 277 | "split = int(0.75*n)\n", 278 | "for i in non_nodule_files[:split]:\n", 279 | " shutil.copy(f\"/content/gdrive/MyDrive/FPRProcessed/non-nodule/{i}\", f\"/content/gdrive/MyDrive/FPRDataset/train/non-nodule/{i}\")\n", 280 | "for i in non_nodule_files[split:]:\n", 281 | " shutil.copy(f\"/content/gdrive/MyDrive/FPRProcessed/non-nodule/{i}\", f\"/content/gdrive/MyDrive/FPRDataset/test/non-nodule/{i}\")" 282 | ], 283 | "metadata": { 284 | "id": "Yr_lpssf2bjU" 285 | }, 286 | "execution_count": null, 287 | "outputs": [] 288 | }, 289 | { 290 | "cell_type": "code", 291 | "source": [], 292 | "metadata": { 293 | "id": "CeR1tcDr2xJa" 294 | }, 295 | "execution_count": null, 296 | "outputs": [] 297 | } 298 | ] 299 | } -------------------------------------------------------------------------------- /04_FPR_Candidate_ROI_extraction.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [] 7 | }, 8 | "kernelspec": { 9 | "name": "python3", 10 | "display_name": "Python 3" 11 | }, 12 | "language_info": { 13 | "name": "python" 14 | }, 15 | "accelerator": "TPU" 16 | }, 17 | "cells": [ 18 | { 19 | "cell_type": "code", 20 | "execution_count": null, 21 | "metadata": { 22 | "colab": { 23 | "base_uri": "https://localhost:8080/" 24 | }, 25 | "id": "O8ZTHKw-uMNT", 26 | "outputId": "3daf0acc-b858-4973-cfa5-d470608c6fc9" 27 | }, 28 | "outputs": [ 29 | { 30 | "output_type": "stream", 31 | "name": "stdout", 32 | "text": [ 33 | "Collecting SimpleITK\n", 34 | " Downloading SimpleITK-2.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (52.7 MB)\n", 35 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.7/52.7 MB\u001b[0m \u001b[31m17.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 36 | "\u001b[?25hInstalling collected packages: SimpleITK\n", 37 | "Successfully installed SimpleITK-2.3.1\n" 38 | ] 39 | } 40 | ], 41 | "source": [ 42 | "!pip install SimpleITK" 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "source": [ 48 | "!pip install kaggle" 49 | ], 50 | "metadata": { 51 | "colab": { 52 | "base_uri": "https://localhost:8080/" 53 | }, 54 | "id": "Rc1WMf6kuO6F", 55 | "outputId": "6d50be0d-eb0e-468b-a66a-5293d4eb6ede" 56 | }, 57 | "execution_count": null, 58 | "outputs": [ 59 | { 60 | "output_type": "stream", 61 | "name": "stdout", 62 | "text": [ 63 | "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.16)\n", 64 | "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n", 65 | "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2023.11.17)\n", 66 | "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n", 67 | "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.31.0)\n", 68 | "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.1)\n", 69 | "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.1)\n", 70 | "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.7)\n", 71 | "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0)\n", 72 | "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n", 73 | "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n", 74 | "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.3.2)\n", 75 | "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.6)\n" 76 | ] 77 | } 78 | ] 79 | }, 80 | { 81 | "cell_type": "code", 82 | "source": [ 83 | "from google.colab import drive\n", 84 | "drive.mount('/content/gdrive')" 85 | ], 86 | "metadata": { 87 | "colab": { 88 | "base_uri": "https://localhost:8080/" 89 | }, 90 | "id": "HNnn2HzbuZKO", 91 | "outputId": "728cf20c-0eb0-4965-c748-19a6035c1d6a" 92 | }, 93 | "execution_count": null, 94 | "outputs": [ 95 | { 96 | "output_type": "stream", 97 | "name": "stdout", 98 | "text": [ 99 | "Mounted at /content/gdrive\n" 100 | ] 101 | } 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "source": [ 107 | "! mkdir ~/.kaggle" 108 | ], 109 | "metadata": { 110 | "id": "yBQ6k_QXubbu" 111 | }, 112 | "execution_count": null, 113 | "outputs": [] 114 | }, 115 | { 116 | "cell_type": "code", 117 | "source": [ 118 | "!cp /content/gdrive/MyDrive/kaggle.json ~/.kaggle/kaggle.json" 119 | ], 120 | "metadata": { 121 | "id": "-Nm4N1sVugsL" 122 | }, 123 | "execution_count": null, 124 | "outputs": [] 125 | }, 126 | { 127 | "cell_type": "code", 128 | "source": [ 129 | "! kaggle datasets download -d wuwu55/luna16" 130 | ], 131 | "metadata": { 132 | "colab": { 133 | "base_uri": "https://localhost:8080/" 134 | }, 135 | "id": "Nmi1njjguwAZ", 136 | "outputId": "0075e8b9-4c3d-40c2-b37d-e97a91169826" 137 | }, 138 | "execution_count": null, 139 | "outputs": [ 140 | { 141 | "output_type": "stream", 142 | "name": "stdout", 143 | "text": [ 144 | "Downloading luna16.zip to /content\n", 145 | "100% 17.6G/17.6G [03:01<00:00, 142MB/s]\n", 146 | "100% 17.6G/17.6G [03:01<00:00, 104MB/s]\n" 147 | ] 148 | } 149 | ] 150 | }, 151 | { 152 | "cell_type": "code", 153 | "source": [ 154 | "! unzip luna16.zip" 155 | ], 156 | "metadata": { 157 | "colab": { 158 | "base_uri": "https://localhost:8080/" 159 | }, 160 | "id": "b5sPQLETuyaW", 161 | "outputId": "1382ed15-3921-46f1-8abf-60aa289cfcfb" 162 | }, 163 | "execution_count": null, 164 | "outputs": [ 165 | { 166 | "output_type": "stream", 167 | "name": "stdout", 168 | "text": [ 169 | "Archive: luna16.zip\n", 170 | " inflating: subset6/subset6/1.3.6.1.4.1.14519.5.2.1.6279.6001.106630482085576298661469304872.mhd \n", 171 | " inflating: subset6/subset6/1.3.6.1.4.1.14519.5.2.1.6279.6001.106630482085576298661469304872.raw \n", 172 | " inflating: subset6/subset6/1.3.6.1.4.1.14519.5.2.1.6279.6001.107109359065300889765026303943.mhd \n", 173 | " inflating: subset6/subset6/1.3.6.1.4.1.14519.5.2.1.6279.6001.107109359065300889765026303943.raw \n", 174 | " inflating: subset6/subset6/1.3.6.1.4.1.14519.5.2.1.6279.6001.117040183261056772902616195387.mhd \n", 175 | 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"outputs": [] 724 | }, 725 | { 726 | "cell_type": "code", 727 | "source": [ 728 | "root = \"/content/subset6/\"\n", 729 | "target_root = \"/content/gdrive/MyDrive/FPRProcessed\"" 730 | ], 731 | "metadata": { 732 | "id": "uGGMOExwxj0U" 733 | }, 734 | "execution_count": null, 735 | "outputs": [] 736 | }, 737 | { 738 | "cell_type": "code", 739 | "source": [ 740 | "subset = 6\n", 741 | "file_list = glob(root+f\"subset{subset}/*.mhd\")\n", 742 | "print(\"Files Count:\",len(file_list))" 743 | ], 744 | "metadata": { 745 | "colab": { 746 | "base_uri": "https://localhost:8080/" 747 | }, 748 | "id": "3uyH9pvWyLOB", 749 | "outputId": "1b4df51f-082f-46bd-c862-956ea8d59fa9" 750 | }, 751 | "execution_count": null, 752 | "outputs": [ 753 | { 754 | "output_type": "stream", 755 | "name": "stdout", 756 | "text": [ 757 | "Files Count: 89\n" 758 | ] 759 | } 760 | ] 761 | }, 762 | { 763 | "cell_type": "code", 764 | "source": [ 765 | "candidates_df = pd.read_csv(\"/content/gdrive/MyDrive/candidates.csv\")" 766 | ], 767 | "metadata": { 768 | "id": "v9r7z-n9yNlq" 769 | }, 770 | "execution_count": null, 771 | "outputs": [] 772 | }, 773 | { 774 | "cell_type": "code", 775 | "source": [ 776 | "candidates_df.head()\n" 777 | ], 778 | "metadata": { 779 | "colab": { 780 | "base_uri": "https://localhost:8080/", 781 | "height": 206 782 | }, 783 | "id": "PCrrjzo9ySOO", 784 | "outputId": "c8064d72-a8b0-436f-cf67-11e2a1f464b6" 785 | }, 786 | "execution_count": null, 787 | "outputs": [ 788 | { 789 | "output_type": "execute_result", 790 | "data": { 791 | "text/plain": [ 792 | " seriesuid coordX coordY coordZ \\\n", 793 | "0 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222... -56.08 -67.85 -311.92 \n", 794 | "1 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222... 53.21 -244.41 -245.17 \n", 795 | "2 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222... 103.66 -121.80 -286.62 \n", 796 | "3 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222... -33.66 -72.75 -308.41 \n", 797 | "4 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\n" 1086 | ] 1087 | }, 1088 | "metadata": {}, 1089 | "execution_count": 12 1090 | } 1091 | ] 1092 | }, 1093 | { 1094 | "cell_type": "code", 1095 | "source": [ 1096 | "print(\"Total Candidates:\",len(candidates_df))\n", 1097 | "print(\"Positives:\",candidates_df['class'].sum())" 1098 | ], 1099 | "metadata": { 1100 | "colab": { 1101 | "base_uri": "https://localhost:8080/" 1102 | }, 1103 | "id": "OEolPMh0yUMG", 1104 | "outputId": "e28c4b42-d4b3-477f-bd25-838d410e0c5b" 1105 | }, 1106 | "execution_count": null, 1107 | "outputs": [ 1108 | { 1109 | "output_type": "stream", 1110 | "name": "stdout", 1111 | "text": [ 1112 | "Total Candidates: 551065\n", 1113 | "Positives: 1351\n" 1114 | ] 1115 | } 1116 | ] 1117 | }, 1118 | { 1119 | "cell_type": "code", 1120 | "source": [ 1121 | "candidates_df.info()\n" 1122 | ], 1123 | "metadata": { 1124 | "colab": { 1125 | "base_uri": "https://localhost:8080/" 1126 | }, 1127 | "id": "12AGaCMKyWWO", 1128 | "outputId": "46c77445-712e-481d-e419-2e8dcd11d1f5" 1129 | }, 1130 | "execution_count": null, 1131 | "outputs": [ 1132 | { 1133 | "output_type": "stream", 1134 | "name": "stdout", 1135 | "text": [ 1136 | "\n", 1137 | "RangeIndex: 551065 entries, 0 to 551064\n", 1138 | "Data columns (total 5 columns):\n", 1139 | " # Column Non-Null Count Dtype \n", 1140 | "--- ------ -------------- ----- \n", 1141 | " 0 seriesuid 551065 non-null object \n", 1142 | " 1 coordX 551065 non-null float64\n", 1143 | " 2 coordY 551065 non-null float64\n", 1144 | " 3 coordZ 551065 non-null float64\n", 1145 | " 4 class 551065 non-null int64 \n", 1146 | "dtypes: float64(3), int64(1), object(1)\n", 1147 | "memory usage: 21.0+ MB\n" 1148 | ] 1149 | } 1150 | ] 1151 | }, 1152 | { 1153 | "cell_type": "code", 1154 | "source": [ 1155 | "def get_filename(file_list, file):\n", 1156 | " for f in file_list:\n", 1157 | " if file in f:\n", 1158 | " return f" 1159 | ], 1160 | "metadata": { 1161 | "id": "Rq5ZqwmOyYbX" 1162 | }, 1163 | "execution_count": null, 1164 | "outputs": [] 1165 | }, 1166 | { 1167 | "cell_type": "code", 1168 | "source": [ 1169 | "def load_mhd(file):\n", 1170 | " mhdimage = stk.ReadImage(file)\n", 1171 | " ct_scan = stk.GetArrayFromImage(mhdimage)\n", 1172 | " origin = np.array(list(mhdimage.GetOrigin()))\n", 1173 | " space = np.array(list(mhdimage.GetSpacing()))\n", 1174 | " return ct_scan, origin, space\n" 1175 | ], 1176 | "metadata": { 1177 | "id": "tfUTBaQQyazU" 1178 | }, 1179 | "execution_count": null, 1180 | "outputs": [] 1181 | }, 1182 | { 1183 | "cell_type": "code", 1184 | "source": [ 1185 | "candidates_df[\"filename\"] = candidates_df[\"seriesuid\"].map(lambda file: get_filename(file_list, file))\n", 1186 | "candidates_df = candidates_df.dropna()\n", 1187 | "print(len(candidates_df))\n" 1188 | ], 1189 | "metadata": { 1190 | "colab": { 1191 | "base_uri": "https://localhost:8080/" 1192 | }, 1193 | "id": "FGxBsuLeycn1", 1194 | "outputId": "e5297915-1a6f-4c1b-e04f-15ad9beaf638" 1195 | }, 1196 | "execution_count": null, 1197 | "outputs": [ 1198 | { 1199 | "output_type": "stream", 1200 | "name": "stdout", 1201 | "text": [ 1202 | "52537\n" 1203 | ] 1204 | } 1205 | ] 1206 | }, 1207 | { 1208 | "cell_type": "code", 1209 | "source": [ 1210 | "candidates_df.head()\n" 1211 | ], 1212 | "metadata": { 1213 | "colab": { 1214 | "base_uri": "https://localhost:8080/", 1215 | "height": 206 1216 | }, 1217 | "id": "hivB1vnPyegE", 1218 | "outputId": "d2c74871-b3b0-4b9f-f537-f4d90b979a72" 1219 | }, 1220 | "execution_count": null, 1221 | "outputs": [ 1222 | { 1223 | "output_type": "execute_result", 1224 | "data": { 1225 | "text/plain": [ 1226 | " seriesuid coordX coordY \\\n", 1227 | "12105 1.3.6.1.4.1.14519.5.2.1.6279.6001.106630482085... -76.16 52.87 \n", 1228 | "12106 1.3.6.1.4.1.14519.5.2.1.6279.6001.106630482085... -97.16 -12.95 \n", 1229 | "12107 1.3.6.1.4.1.14519.5.2.1.6279.6001.106630482085... -120.27 29.06 \n", 1230 | "12108 1.3.6.1.4.1.14519.5.2.1.6279.6001.106630482085... 20.46 24.86 \n", 1231 | "12109 1.3.6.1.4.1.14519.5.2.1.6279.6001.106630482085... 79.98 -12.95 \n", 1232 | "\n", 1233 | " coordZ class filename \n", 1234 | "12105 -209.26 0 /content/subset6/subset6/1.3.6.1.4.1.14519.5.2... \n", 1235 | "12106 -93.76 0 /content/subset6/subset6/1.3.6.1.4.1.14519.5.2... \n", 1236 | "12107 -284.61 0 /content/subset6/subset6/1.3.6.1.4.1.14519.5.2... \n", 1237 | "12108 -277.57 0 /content/subset6/subset6/1.3.6.1.4.1.14519.5.2... \n", 1238 | "12109 -237.43 0 /content/subset6/subset6/1.3.6.1.4.1.14519.5.2... " 1239 | ], 1240 | "text/html": [ 1241 | "\n", 1242 | "
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\n" 1526 | ] 1527 | }, 1528 | "metadata": {}, 1529 | "execution_count": 18 1530 | } 1531 | ] 1532 | }, 1533 | { 1534 | "cell_type": "code", 1535 | "source": [ 1536 | "clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8)) # CLAHE(Contrast Limited Adaptive Histogram Equalization) filter for enhancing the contrast of an image\n", 1537 | "for i,file in tqdm(enumerate(np.unique(candidates_df['filename'].values)), total=len(np.unique(candidates_df['filename'].values))):\n", 1538 | " candidates = candidates_df[candidates_df[\"filename\"]==file]\n", 1539 | " ct, origin, space = load_mhd(file)\n", 1540 | " num_z, height, width = ct.shape\n", 1541 | " ct_norm = cv2.normalize(ct, None, 0, 255, cv2.NORM_MINMAX)\n", 1542 | " for idx, row in candidates.iterrows():\n", 1543 | " node_x = int(row[\"coordX\"]) # x-coordinate of the candidate\n", 1544 | " node_y = int(row[\"coordY\"]) # y-coordinate of the candidate\n", 1545 | " node_z = int(row[\"coordZ\"]) # z-coordinate of the candidate\n", 1546 | " c = int(row[\"class\"]) # class of the candidate (1: nodule, 0: non-nodule)\n", 1547 | "\n", 1548 | " center = np.array([node_x, node_y, node_z]) # nodule center\n", 1549 | " v_center = np.rint((center-origin)/space) # nodule center in voxel space (still x,y,z ordering)\n", 1550 | "\n", 1551 | " img_norm = ct_norm[int(v_center[2]),:,:] # a slice of the CT scan containing the candidate\n", 1552 | " img_norm = cv2.resize(img_norm, (512,512)) # resize the image to 512x512\n", 1553 | " img_norm_improved = clahe.apply(img_norm.astype(np.uint8)) # apply CLAHE filter to the image\n", 1554 | "\n", 1555 | " x=abs(int(v_center[0]))\n", 1556 | " y=abs(int(v_center[1]))\n", 1557 | " box = img_norm_improved[max(0,y-25):min(y+25,512),max(0,x-25):min(x+25,512)] # extract a box of size 25x25 around the candidate\n", 1558 | " if box.shape != (50,50):\n", 1559 | " box = cv2.resize(box, (50,50))\n", 1560 | "\n", 1561 | " if c: # if the candidate is a nodule\n", 1562 | " # applying different image transformations to increase the number of nodule candidates\n", 1563 | " cv2.imwrite(os.path.join(target_root+\"/nodule/\", f\"candidate_{subset}_{c}_{idx}.jpg\"),box)\n", 1564 | " cv2.imwrite(os.path.join(target_root+\"/nodule/\", f\"candidate_{subset}_{c}_{idx}_1.jpg\"),cv2.rotate(box,cv2.ROTATE_90_CLOCKWISE))\n", 1565 | " cv2.imwrite(os.path.join(target_root+\"/nodule/\", f\"candidate_{subset}_{c}_{idx}_2.jpg\"),cv2.rotate(box, cv2.ROTATE_90_COUNTERCLOCKWISE))\n", 1566 | " cv2.imwrite(os.path.join(target_root+\"/nodule/\", f\"candidate_{subset}_{c}_{idx}_3.jpg\"),cv2.rotate(box, cv2.ROTATE_180))\n", 1567 | " cv2.imwrite(os.path.join(target_root+\"/nodule/\", f\"candidate_{subset}_{c}_{idx}_4.jpg\"),cv2.flip(box, 1))\n", 1568 | " else: # if the candidate is not a nodule\n", 1569 | " cv2.imwrite(os.path.join(target_root+\"/non-nodule-initial/\", f\"candidate_{subset}_{c}_{idx}.jpg\"),box)" 1570 | ], 1571 | "metadata": { 1572 | "colab": { 1573 | "base_uri": "https://localhost:8080/" 1574 | }, 1575 | "id": "4cyDeQ6kyhLV", 1576 | "outputId": "39641cdf-e8d8-4761-dde0-489c2838d51f" 1577 | }, 1578 | "execution_count": null, 1579 | "outputs": [ 1580 | { 1581 | "output_type": "stream", 1582 | "name": "stderr", 1583 | "text": [ 1584 | "100%|██████████| 89/89 [09:22<00:00, 6.32s/it]\n" 1585 | ] 1586 | } 1587 | ] 1588 | }, 1589 | { 1590 | "cell_type": "code", 1591 | "source": [], 1592 | "metadata": { 1593 | "id": "kTLaNc_wynkQ" 1594 | }, 1595 | "execution_count": null, 1596 | "outputs": [] 1597 | } 1598 | ] 1599 | } --------------------------------------------------------------------------------