{"id":111084,"date":"2022-08-05T09:50:37","date_gmt":"2022-08-05T04:20:37","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=111084"},"modified":"2025-07-28T20:49:44","modified_gmt":"2025-07-28T15:19:44","slug":"pneumonia-detection-cnn-python","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/pneumonia-detection-cnn-python\/","title":{"rendered":"Pneumonia Detection using CNN in Python"},"content":{"rendered":"<div class='__iawmlf-post-loop-links' style='display:none;' data-iawmlf-post-links='[{&quot;id&quot;:253,&quot;href&quot;:&quot;https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1P5SOh_OeIC8ySRITgKYhal7wX9rclN9f\\\/view?usp=sharing&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251207203447\\\/https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1P5SOh_OeIC8ySRITgKYhal7wX9rclN9f\\\/view?usp=sharing&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-08 08:05:07&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-12 06:59:01&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-15 14:21:58&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-18 14:35:26&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-21 17:22:38&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-26 10:36:54&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-30 06:13:11&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-02 18:23:11&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-07 06:51:25&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-11 12:04:57&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-17 01:12:14&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-20 07:42:20&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-23 09:14:38&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-29 08:55:24&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-02 04:57:36&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-07 08:32:56&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-11 09:21:20&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-14 10:44:54&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-18 07:54:21&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-23 04:28:44&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-27 06:14:05&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-02 06:22:46&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-06 04:26:37&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-10 03:08:35&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-13 10:00:45&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-16 12:46:39&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-20 07:28:04&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-24 16:28:09&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-29 05:22:58&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-01 16:57:42&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-06 23:43:11&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-10 09:09:06&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-13 19:40:33&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-17 02:38:38&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-21 05:53:34&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-24 09:03:46&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-28 17:31:25&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-02 04:53:05&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-07 06:24:40&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-10 13:44:17&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-14 20:26:51&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-18 09:13:53&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-24 17:46:09&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-01 04:27:40&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-04 10:57:36&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-11 14:18:48&quot;,&quot;http_code&quot;:200}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-11 14:18:48&quot;,&quot;http_code&quot;:200},&quot;process&quot;:&quot;done&quot;}]'><\/div>\n<p>With this Machine Learning Project, we are going to build a pneumonia detection system. This system will be used to detect whether a person has pneumonia or not. This system will take input as an image and will detect if the person has pneumonia or not. For this project, the accuracy factor is very important. We are going to use the VGG16 CNN model for this project.<\/p>\n<p>So, let\u2019s start with the project.<\/p>\n<h3>Pneumonia Detection<\/h3>\n<p>Pneumonia is responsible for 16% of all deaths of children under five worldwide. About 1 million people seek medical care from hospitals in the United States each year, resulting in 50,000 deaths. Thousands of people are at risk of death as a result of complicated Coronavirus disease (COVID-19) due to pneumonia. Pneumonia is an inflammatory lung disease characterized by multiple illnesses caused by different microorganisms.<\/p>\n<p>A lower respiratory tract infection begins as an upper respiratory tract infection. During breathing, microscopic organisms are constantly exposed to the alveolar ducts and bronchioles.<\/p>\n<p>In the upper respiratory tract (the nasal passages, the pharynx, paranasal sinuses, and the larynx above the vocal folds), the flora compete with pathogens for nutrients, while in the lower respiratory tract (the bronchi and bronchioles, trachea, and alveoli), cough reflexes, immunoglobulins, and complement proteins help expel mucus and foreign matter.<\/p>\n<p>Pneumonia progresses based on the body&#8217;s immune response, the virulence of the causative organism, and the amount of organisms in the pre-stage infection.<\/p>\n<h3>The Model Architecture<\/h3>\n<p>Chest X-Ray radiographs (CXR) are commonly used as a diagnostic tool for pneumonia disease. Clinical history, vital signs, and various laboratory examinations are used to confirm the diagnosis of the areas of increased opacity.<\/p>\n<p>There are always other scenarios and conditions in the lungs that can complicate the pneumonia diagnosis on CXR, such as bleeding, fluid overload, volume loss, post-radiation, and post-surgical changes.<\/p>\n<p>Radiologists have known differences or variability in how they interpret chest radiographs.<\/p>\n<p>Since the turn of the century, computer-aided systems have been widely used for pneumonia detection in order to improve efficiency and accuracy.<\/p>\n<p>In many medical imaging analysis tasks, including detection, classification, and segmentation, deep learning approaches outperformed or outnumbered conventional machine learning methods.<\/p>\n<p>This paper by Karen Simonyan and Andrew Zisserman reports VGG-16 architecture, composed of 13 convolutional layers, two fully connected layers, and one SoftMax classifier. An eight-layer network consisting of convolutional and fully connected layers was created by Karen and Andrew. Convolutions are stacked in threes on top of each other in order to simplify the process.<\/p>\n<p>The precise structure of the VGG-16 network is as follows:<\/p>\n<p>1. The first convolutional layer and the second convolutional layer consist of 64 feature kernel filters each with a size of 3\u00d73. As input image (RGB image with depth 3 passed into first and second convolutional layer, dimensions changes to 224x224x64. Then the resulting output is passed to max pooling layer with a stride of 2.<\/p>\n<p>2. In the third and fourth convolutional layers, there are 124 feature kernel filters, and the size of the filters is 3\u00d73. As a result, the resulting output is 56x56x128 after two layers of max pooling with stride 2.<\/p>\n<p>3. There are three layers of convolutional neural networks, the fifth, the sixth, and the seventh, which have kernel sizes of 3\u00d73. A 256 feature map is used by all three. Then there is a layer with stride 2 called max pooling.<\/p>\n<p>4. A total of seven convolutional layers are in the range of eight to thirteen, each with a kernel size of three by three. There are 512 kernel filters in all of these sets of convolutional layers. After these layers, there is a max pooling layer that has a stride of 1.<\/p>\n<p>5. A fully connected hidden layer (fourteenth layer) contains 4096 units, followed by a 1000-unit softmax output layer (fourteenth layer).<\/p>\n<h3>Project Prerequisites<\/h3>\n<p>The required modules for this project are:<\/p>\n<ul>\n<li>Pandas &#8211; pip install pandas<\/li>\n<li>Numpy- pip install numpy<\/li>\n<li>Tensorflow &#8211; pip install tensorflow<\/li>\n<\/ul>\n<h3>Pneumonia Detection Project<\/h3>\n<p>This pneumonia detection dataset contains 1000s of images of X-rays. This dataset is divided into training, testing, and Val for the final testing of the model. Further, each dataset is divided into 2 categories a pneumonia image and a normal x-ray image. Please download pneumonia detection machine learning project code and dataset from the following link: <a href=\"https:\/\/drive.google.com\/file\/d\/1P5SOh_OeIC8ySRITgKYhal7wX9rclN9f\/view?usp=sharing\"><strong>Pneumonia Detection Project<\/strong><\/a><\/p>\n<h3>Steps to Implement Pneumonia Detection<\/h3>\n<p>1. Importing all the modules to be used in the project.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">import numpy as np\r\nimport matplotlib.pyplot as plt\r\n%matplotlib inline\r\n\r\nfrom tensorflow.keras.layers import Input,Conv2D,MaxPooling2D,Dropout,Flatten,Dense,Activation,BatchNormalization,add\r\nfrom tensorflow.keras.models  import Model,Sequential\r\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\r\nfrom tensorflow.keras.utils import plot_model\r\nfrom tensorflow.keras.applications.vgg16 import VGG16,preprocess_input\r\nimport os\r\n<\/pre>\n<p>2. Here we are loading the VGG16 model and defining our model and its layers which are going to be used to detect pneumonia.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">vgg = VGG16(weights= 'imagenet', include_top = False, input_shape = (224,224,3))layers) \r\n \r\n \r\nfor layer in vgg.layers:\r\n    layer.trainable = False #making all the layers non-trainable\r\n      \r\nx = Flatten()(vgg.output)\r\n \r\npredictions = Dense(2,activation='softmax')(x) \r\n \r\n \r\nmodel = Model(inputs=vgg.input, outputs=predictions)\r\nmodel.summary()\r\n<\/pre>\n<p>3. Here we are initializing the data generator for training the model and we are loading the VGG16 model with ImageNet weights without the Fully Connected Layers.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">target_shape = (224,224) \r\n \r\ntrain_dir = \"train\" \r\nval_dir = \"val\"     # -- Directories for data\r\ntest_dir = \"test\"   # \r\n \r\nvgg = VGG16(weights= 'imagenet', include_top = False, input_shape = (224,224,3))\r\nfor layer in vgg.layers:\r\n    layer.trainable = False # Making all the layers non-trainable\r\n<\/pre>\n<p>4. Here defining the model and its layers.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">x = Flatten()(vgg.output) \r\npredictions = Dense(2,activation='softmax')(x) \r\nmodel = Model(inputs=vgg.input, outputs=predictions)\r\nmodel.summary()\r\n<\/pre>\n<p>5. Here we are getting our training and testing data.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">train_gen = ImageDataGenerator(rescale=1\/255.0,\r\n                               horizontal_flip=True,\r\n                               zoom_range=0.2,\r\n                               shear_range=0.2) \r\n \r\n \r\n# Making the data loader for validation data\r\ntest_gen = ImageDataGenerator(rescale=1\/255.0) \r\n \r\n \r\n# Function to make iterable object for training\r\ntrain_data_gen = train_gen.flow_from_directory(train_dir,\r\n                                               target_shape,\r\n                                               batch_size=16,\r\n                                               class_mode='categorical') \r\n \r\n# Function to make iterable object for training\r\ntest_data_gen = train_gen.flow_from_directory(test_dir,\r\n                                               target_shape,\r\n                                               batch_size=16,\r\n                                               class_mode='categorical')\r\n<\/pre>\n<p>6. Here We are fitting our training data into our model. Also, we are passing the test data and generating the output.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\r\nhist = model.fit_generator(train_data_gen,\r\n        steps_per_epoch=20,\r\n        epochs=20,\r\n        validation_data=test_data_gen,\r\n        validation_steps=10)\r\n<\/pre>\n<p>7. Here we are plotting an accuracy and loss curve.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">plt.style.use(\"ggplot\")\r\nplt.figure()\r\nplt.plot(hist.history[\"loss\"], label=\"train_loss\")\r\nplt.plot(hist.history[\"val_loss\"], label=\"val_loss\")\r\nplt.plot(hist.history[\"accuracy\"], label=\"train_acc\")\r\nplt.plot(hist.history[\"val_accuracy\"], label=\"val_acc\")\r\nplt.title(\"Model Training\")\r\nplt.xlabel(\"Epoch #\")\r\nplt.ylabel(\"Loss\/Accuracy\")\r\nplt.legend()\r\nplt.savefig(\"epochs.png\")\r\n<\/pre>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2022\/08\/pneumonia-detection-output.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-111085\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2022\/08\/pneumonia-detection-output.png\" alt=\"pneumonia detection output\" width=\"812\" height=\"433\" \/><\/a><\/p>\n<h3>Summary<\/h3>\n<p>Pneumonia is a serious infection that affects the lungs. Doctors usually find it by looking at chest X-ray images. But this takes time and needs a trained expert. Using a Convolutional Neural Network (CNN) in Python, we can build a machine learning project that checks X-ray images and tells if a person has pneumonia. CNN is very good at working with image data because it looks at small parts of the image and learns patterns.<\/p>\n<p>In this Machine Learning project, we learned how to build a pneumonia detection system that is used to detect whether a person has pneumonia or not. In this project, we have used VGG16 CNN. This is used in a lot of detection and classification problems.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>With this Machine Learning Project, we are going to build a pneumonia detection system. This system will be used to detect whether a person has pneumonia or not. This system will take input as&#46;&#46;&#46;<\/p>\n","protected":false},"author":1,"featured_media":111086,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[36],"tags":[21686,21728,20697,22695,27136,27137,27138],"class_list":["post-111084","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-learning","tag-deep-learning-project","tag-deep-learning-project-ideas","tag-machine-learning-project","tag-machine-learning-project-for-beginners","tag-pneumonia-detection","tag-pneumonia-detection-using-cnn","tag-pneumonia-detection-using-machine-learning"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Pneumonia Detection using CNN in Python - DataFlair<\/title>\n<meta name=\"description\" content=\"Develop a machine learning project to create a model for Pneumonia Detection using CNN, Tensorflow, NumPy, Pandas.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/pneumonia-detection-cnn-python\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pneumonia Detection using CNN in Python - 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