{"id":132728,"date":"2024-08-26T18:00:31","date_gmt":"2024-08-26T12:30:31","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=132728"},"modified":"2026-06-01T12:18:32","modified_gmt":"2026-06-01T06:48:32","slug":"cell-classification-using-opencv","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/cell-classification-using-opencv\/","title":{"rendered":"OpenCV Project &#8211; Cell Classification"},"content":{"rendered":"<p>Cell classification is a crucial task in biomedical image analysis involving the categorization of cells into distinct groups based on visual characteristics. Leveraging advanced technologies such as convolutional neural networks (CNNs) and cell classification aids in automating disease diagnosis, drug development and understanding cellular behavior. By training models on diverse cell images, these systems contribute to more efficient and accurate analyses.<\/p>\n<h2>VGG16<\/h2>\n<p>VGG16 is a deep convolutional neural network architecture renowned for its simplicity and effectiveness in image classification. It was developed by the Visual Geometry Group (VGG) at Oxford. It consists of 16 weight layers, including convolutional and fully connected layers. VGG16\u2019s uniform structure facilitates feature extraction, making it a cornerstone in computer vision and deep learning applications.<\/p>\n<h3>Dataset<\/h3>\n<p>The cell dataset consists of four classes &#8211; eosinophils, monocytes, lymphocytes and neutrophils. Each class represents distinct cell types, contributing to a diverse and comprehensive dataset for training and evaluating machine learning models, particularly in the field of cell classification using technologies like CNNs.<\/p>\n<h3>Prerequisites For Cell Classification Using OpenCV<\/h3>\n<p>Proficiency in Python and familiarity with the OpenCV library are prerequisites, as are the following system requirements.<\/p>\n<ul>\n<li>Python 3.7 (64-bit) and above<\/li>\n<li>Any Python editor (VS code, Pycharm)<\/li>\n<li>GPU 4.00 GB+<\/li>\n<\/ul>\n<p><strong>Note:-<\/strong> In this Tutorial, Google Colab is used.<\/p>\n<h3>Download the OpenCV Cell Classification Project<\/h3>\n<p>Please download the source code for the OpenCV Cell Classification Project: <a href=\"https:\/\/drive.google.com\/file\/d\/1Aub6pMTDVhT4gsnAdptQOROcOAT0kQUN\/view?usp=drive_link\"><strong>OpenCV<\/strong> <strong>Cell Classification Project Code.<\/strong><\/a><\/p>\n<h3>Installation<\/h3>\n<p>Open Windows cmd as administrator<\/p>\n<p><strong>1. Install OpenCV library.<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">pip install opencv-python<\/pre>\n<p><strong>2. Install TensorFlow library.<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">pip install tensorflow<\/pre>\n<p><strong>3. Install matplotlib library.<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">pip install matplotlib<\/pre>\n<h3>Let\u2019s Implement<\/h3>\n<p>1. Import all the packages.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">import os\r\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\r\nfrom tensorflow.keras.applications import VGG16\r\nfrom tensorflow.keras import layers, models\r\nfrom tensorflow.keras.optimizers import Adam\r\nimport matplotlib.pyplot as plt\r\nfrom tensorflow.keras.preprocessing import image\r\nfrom tensorflow.keras.models import load_model\r\nimport numpy as np<\/pre>\n<p>2. Set the dataset path.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">Data = '\/content\/drive\/MyDrive\/Data_Flair\/Cell\/data\/TRAIN'<\/pre>\n<p>3. It defines the input image shape, number of classes, batch size and number of epochs.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">input_shape = (224, 224, 3)\r\nnum_classes = 4  \r\nbatch_size = 32\r\nepochs = 20<\/pre>\n<p>4. It defines an image data generator and creates a directory-based data generator for training with specified target size, batch size and categorical loss mode.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">train_datagen = ImageDataGenerator(\r\n    rescale=1.\/255,\r\n    shear_range=0.2,\r\n    zoom_range=0.2,\r\n    horizontal_flip=True\r\n)\r\n\r\ntrain_generator = train_datagen.flow_from_directory(\r\n    Data,\r\n    target_size=input_shape[:2],\r\n    batch_size=batch_size,\r\n    class_mode='categorical'\r\n)<\/pre>\n<p>5. It loads the VGG16 model with pre-trained Imagenet weights, excluding the top classification layer, and sets it as trainable.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">base_model = VGG16(weights='imagenet', include_top=False, input_shape=input_shape)\r\nbase_model.trainable = True<\/pre>\n<p>6. It freezes the first layers of the base model up to the fourth-to-last layer for fine-tuning.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">fine_tune_at = -4\r\nfor layer in base_model.layers[:fine_tune_at]:\r\n    layer.trainable = False<\/pre>\n<p>7. It specifies the model architecture.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">model = models.Sequential([\r\n    base_model,\r\n    layers.Flatten(),\r\n    layers.Dense(256, activation='relu'),\r\n    layers.Dropout(0.5),\r\n    layers.Dense(128, activation='relu'),  \r\n    layers.Dropout(0.5),\r\n    layers.Dense(num_classes, activation='softmax')\r\n])<\/pre>\n<p>8. It compiles the model and starts to train the model.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">model.compile(optimizer=Adam(learning_rate=1e-5), \r\n              loss='categorical_crossentropy',\r\n              metrics=['accuracy'])\r\n\r\nhistory = model.fit(train_generator, epochs=epochs)<\/pre>\n<p>9. It prints the training accuracy of the model.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">_, training_accuracy = model.evaluate(train_generator)\r\nprint(f'Training Accuracy: {training_accuracy * 100:.2f}%')<\/pre>\n<p><strong>The output of this step<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2024\/01\/training-accuracy.webp\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-132743 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2024\/01\/training-accuracy.webp\" alt=\"training accuracy\" width=\"295\" height=\"25\" \/><\/a><\/p>\n<p>10. It defines and calls a function to plot training accuracy and loss over epochs using matplotlib.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">def plot_history(history):\r\n    plt.figure(figsize=(12, 6))\r\n    plt.subplot(1, 2, 1)\r\n    plt.plot(history.history['accuracy'])\r\n    plt.title('Training Accuracy')\r\n    plt.xlabel('Epoch')\r\n    plt.ylabel('Accuracy')\r\n    plt.subplot(1, 2, 2)\r\n    plt.plot(history.history['loss'])\r\n    plt.title('Training Loss')\r\n    plt.xlabel('Epoch')\r\n    plt.ylabel('Loss')\r\n    plt.show()\r\n\r\nplot_history(history)<\/pre>\n<p><strong>The output of this step<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2024\/01\/function-to-plot-training.webp\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-132744 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2024\/01\/function-to-plot-training.webp\" alt=\"function to plot training\" width=\"1010\" height=\"547\" \/><\/a><\/p>\n<p>11. It saves the trained model.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">model.save('cell_classification_model_improved.h5')<\/pre>\n<p>12. It loads the pre-trained model, predicts the class of an input image, and displays the image with the predicted class label using matplotlib.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">model = load_model('cell_classification_model_improved.h5')  \r\nInput_img = '\/content\/drive\/MyDrive\/Data_Flair\/Cell\/data\/TRAIN\/NEUTROPHIL\/_151_8029.jpeg'\r\nimg = image.load_img(Input_img, target_size=(224, 224))\r\nimg_array = image.img_to_array(img)\r\nimg_array = np.expand_dims(img_array, axis=0)\r\nimg_array \/= 255.0  \r\npredictions = model.predict(img_array)\r\npredicted_class = np.argmax(predictions, axis=1)\r\nclass_names = {0: 'EOSINOPHIL', 1: 'LYMPHOCYTE', 2: 'MONOCYTE', 3: 'NEUTROPHIL'}\r\nplt.imshow(img)\r\nplt.title(f'Predicted Class: {class_names[predicted_class[0]]}')\r\nplt.show()<\/pre>\n<h3>OpenCV Cell Classification Output<\/h3>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2024\/01\/cell-classification-output.webp\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-132776 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2024\/01\/cell-classification-output.webp\" alt=\"cell classification output\" width=\"798\" height=\"357\" \/><\/a><\/p>\n<h3><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-132777\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2024\/01\/cell-classification-output-image.webp\" alt=\"cell classification output image\" width=\"798\" height=\"357\" data-wp-editing=\"1\" \/><\/h3>\n<h3>Conclusion<\/h3>\n<p>In conclusion, the application of Convolutional Neural Networks (CNNs) for cell classification has proven to be a transformative approach, demonstrating remarkable accuracy and efficiency. By harnessing the power of deep learning, CNNs enable the automated and precise identification of cell types, paving the way for advancements in medical diagnostics, drug discovery, and beyond. The ability to analyze complex cellular structures with high accuracy underscores the potential of CNNs in revolutionizing our understanding of biology and facilitating breakthroughs in various scientific and medical domains.<span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:2526,&quot;href&quot;:&quot;https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1Aub6pMTDVhT4gsnAdptQOROcOAT0kQUN\\\/view?usp=drive_link&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20260601064757\\\/https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1Aub6pMTDVhT4gsnAdptQOROcOAT0kQUN\\\/view?usp=drive_link&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2026-06-02 06:40:51&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-09 10:50:20&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-07-04 04:18:18&quot;,&quot;http_code&quot;:200}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-07-04 04:18:18&quot;,&quot;http_code&quot;:200},&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Cell classification is a crucial task in biomedical image analysis involving the categorization of cells into distinct groups based on visual characteristics. Leveraging advanced technologies such as convolutional neural networks (CNNs) and cell classification&#46;&#46;&#46;<\/p>\n","protected":false},"author":86671,"featured_media":132742,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[27755],"tags":[30241,32933,30242,30240,30239,23025,30129,30121,30118],"class_list":["post-132728","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-opencv-tutorials","tag-cell-classification","tag-cell-classification-project","tag-cell-classification-project-using-opencv","tag-cell-classification-using-opencv","tag-opencv-cell-classification-project","tag-opencv-project","tag-opencv-projects-for-practice","tag-opencv-projects-ideas","tag-python-opencv-projects"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>OpenCV Project - Cell Classification - DataFlair<\/title>\n<meta name=\"description\" content=\"OpenCV cell classification has proven to be a transformative approach, demonstrating remarkable accuracy and efficiency.\" \/>\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\/cell-classification-using-opencv\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"OpenCV Project - Cell Classification - DataFlair\" \/>\n<meta property=\"og:description\" content=\"OpenCV cell classification has proven to be a transformative approach, demonstrating remarkable accuracy and efficiency.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/data-flair.training\/blogs\/cell-classification-using-opencv\/\" \/>\n<meta property=\"og:site_name\" content=\"DataFlair\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataFlairWS\/\" \/>\n<meta property=\"article:published_time\" content=\"2024-08-26T12:30:31+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-06-01T06:48:32+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2023\/12\/opencv-cell-classification.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"628\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"TechVidvan Team\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:site\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"TechVidvan Team\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"OpenCV Project - Cell Classification - DataFlair","description":"OpenCV cell classification has proven to be a transformative approach, demonstrating remarkable accuracy and efficiency.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/data-flair.training\/blogs\/cell-classification-using-opencv\/","og_locale":"en_US","og_type":"article","og_title":"OpenCV Project - Cell Classification - DataFlair","og_description":"OpenCV cell classification has proven to be a transformative approach, demonstrating remarkable accuracy and efficiency.","og_url":"https:\/\/data-flair.training\/blogs\/cell-classification-using-opencv\/","og_site_name":"DataFlair","article_publisher":"https:\/\/www.facebook.com\/DataFlairWS\/","article_published_time":"2024-08-26T12:30:31+00:00","article_modified_time":"2026-06-01T06:48:32+00:00","og_image":[{"width":1200,"height":628,"url":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2023\/12\/opencv-cell-classification.webp","type":"image\/webp"}],"author":"TechVidvan Team","twitter_card":"summary_large_image","twitter_creator":"@DataFlairWS","twitter_site":"@DataFlairWS","twitter_misc":{"Written by":"TechVidvan Team","Est. reading time":"4 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/data-flair.training\/blogs\/cell-classification-using-opencv\/#article","isPartOf":{"@id":"https:\/\/data-flair.training\/blogs\/cell-classification-using-opencv\/"},"author":{"name":"TechVidvan Team","@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/person\/0e594f928e31fc96628ac40f6ae74f49"},"headline":"OpenCV Project &#8211; 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