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This tutorial provides a step-by-step easy guide on how to implement and train a CNN model for Malaria cell classification using TensorFlow and Keras.
🔍 What You’ll Learn 🔍:
Data Preparation — In this part, you’ll download the dataset and prepare the data for training. This involves tasks like preparing the data , splitting into training and testing sets, and data augmentation if necessary.
CNN Model Building and Training — In part two, you’ll focus on building a Convolutional Neural Network (CNN) model for the binary classification of malaria cells. This includes model customization, defining layers, and training the model using the prepared data.
Model Testing and Prediction — The final part involves testing the trained model using a fresh image that it has never seen before. You’ll load the saved model and use it to make predictions on this new image to determine whether it’s infected or not.
Check out our tutorial here : https://youtu.be/WlPuW3GGpQo?si=xMGGR-Sj91u3w2dn
Link for the full code : https://ko-fi.com/s/98d022c834
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Here is the code for Convolutional neural network – Malaria Cells Classification :
Dataset name – Malaria cell images dataset : https://www.kaggle.com/datasets/iarunava/cell-images-for-detecting-malaria
Part 1 : Malaria Cell Image Preprocessing and Dataset Preparation :
This Python script processes images from a malaria cell classification dataset, resizing them for machine learning tasks.
It reads parasitized and uninfected cell images, assigns labels (0 for parasitized, 1 for uninfected), and saves the processed data as NumPy arrays for further analysis or model training.
# Import necessary libraries import numpy as np # For handling numerical data and arrays import pandas as pd # (Not used in the script but generally used for data handling) import cv2 # OpenCV for image processing import glob # For handling file paths and reading multiple images # Lists to store processed images and their corresponding labels allImages = [] allLables = [] # Define the target shape for resizing images input_shape = (124, 124) # Define the paths to the dataset (Parasitized and Uninfected cell images) ParasitedPath = "E:/Data-sets/Malaria Cell Classification/cell_images/Parasitized" UninfectedPath = "E:/Data-sets/Malaria Cell Classification/cell_images/Uninfected" # List containing both paths paths = [ParasitedPath, UninfectedPath] # Loop through both directories for path in paths: path2 = path + "/*.png" # Define the file search pattern (all PNG images) # Loop through all image files in the current directory for file in glob.glob(path2): print(file) # Print the file path (for debugging or tracking progress) # Load the image using OpenCV img = cv2.imread(file) # If the image is successfully loaded, process it if img is not None: # Resize the image to the specified input shape resized = cv2.resize(img, input_shape, interpolation=cv2.INTER_AREA) # Add the resized image to the list allImages.append(resized) # Assign labels: 0 for parasitized, 1 for uninfected if path == ParasitedPath: allLables.append(0) else: # If from the uninfected directory allLables.append(1) # Convert the lists to NumPy arrays for efficient storage and processing allImagesNP = np.array(allImages) print(allImagesNP.shape) # Print the shape of the processed image dataset allLablesNP = np.array(allLables) print(allLablesNP.shape) # Print the shape of the labels array # Save the processed images and labels as .npy files for future use print("Save the data") np.save("e:/temp/Malaria-images.npy", allImagesNP) np.save("e:/temp/Malaria-lables.npy", allLablesNP) print("Finish save the data ....")Link for the full code : https://ko-fi.com/s/98d022c834
Part 2 : Training a CNN for Malaria Cell Classification :
This script loads a preprocessed malaria dataset, normalizes image pixel values, splits the data into training and testing sets, and trains a Convolutional Neural Network (CNN) to classify parasitized and uninfected cells. The best-performing model is saved for future use.
# Import necessary libraries import numpy as np # For numerical operations import cv2 # For image processing # Load the saved dataset (preprocessed images and labels) allImages = np.load("e:/temp/Malaria-images.npy") allLables = np.load("e:/temp/Malaria-lables.npy") # Print the shapes of the loaded datasets to verify dimensions print(allImages.shape) # Shape of the images dataset print(allLables.shape) # Shape of the labels dataset # Define the expected input shape for the model input_shape = (124, 124, 3) # Image dimensions with 3 color channels (RGB) shape = (124, 124) # 2D shape for image display # Display the first image in the dataset img = allImages[0] # Extract the first image label = allLables[0] # Get its corresponding label print(label) # Print the label (0 = parasitized, 1 = uninfected) # Show the image using OpenCV cv2.imshow("img", img) cv2.waitKey(0) # Wait for a key press cv2.destroyAllWindows() # Close the image window # Normalize image pixel values to be between 0 and 1 for better training performance allImagesForModel = allImages / 255.0 # Split the dataset into training and testing sets (70% training, 30% testing) from sklearn.model_selection import train_test_split print("Splitting data into training and testing sets...") X_train, X_test, y_train, y_test = train_test_split(allImagesForModel, allLables, test_size=0.3, random_state=42) # Print shapes of the train and test datasets print("X_train, X_test, y_train, y_test ----->>> shapes:") print(X_train.shape) print(X_test.shape) print(y_train.shape) print(y_test.shape) # Import necessary Keras libraries to define and train the CNN model from keras.models import Sequential from keras.layers import Dense, Conv2D, MaxPooling2D, Flatten from keras.callbacks import ModelCheckpoint # Define a Convolutional Neural Network (CNN) model model = Sequential() # First convolutional layer with 16 filters, kernel size (3x3), and ReLU activation model.add(Conv2D(input_shape=input_shape, filters=16, kernel_size=(3,3), padding="same", activation="relu")) model.add(Conv2D(filters=16, kernel_size=(3,3), padding="same", activation="relu")) model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2))) # Max pooling to reduce spatial dimensions # Second convolutional block with 32 filters model.add(Conv2D(filters=32, kernel_size=(3,3), padding="same", activation="relu")) model.add(Conv2D(filters=32, kernel_size=(3,3), padding="same", activation="relu")) model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2))) # Max pooling again # Flatten the feature maps into a 1D vector for the dense layers model.add(Flatten()) # Fully connected dense layers model.add(Dense(1024, activation="relu")) # Hidden layer with 1024 neurons model.add(Dense(1, activation="sigmoid")) # Output layer (sigmoid activation for binary classification) # Compile the model with Adam optimizer and binary cross-entropy loss (for binary classification) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # Print the model summary to see the architecture print(model.summary()) # Define batch size and number of epochs for training batch = 32 epochs = 10 # Calculate steps per epoch for training and validation stepsPerEpoch = np.ceil(len(X_train) / batch) validationSteps = np.ceil(len(X_test) / batch) # Define the path to save the best model based on validation accuracy best_model_file = "e:/temp/Malaria_binary.h5" # Create a ModelCheckpoint callback to save the best model during training best_model = ModelCheckpoint(best_model_file, monitor="val_accuracy", verbose=1, save_best_only=True) # Train the model history = model.fit( X_train, y_train, batch_size=batch, epochs=epochs, verbose=1, validation_data=(X_test, y_test), validation_steps=validationSteps, steps_per_epoch=stepsPerEpoch, shuffle=True, # Shuffle training data to improve generalization callbacks=[best_model] # Save the best model during training )Link for the full code : https://ko-fi.com/s/98d022c834
Part3 : Loading a Trained Model and run inference on a test image
This script loads a pre-trained Convolutional Neural Network (CNN) model for malaria cell classification. It processes a test image, makes a prediction, and displays the image with the classification result (infected or uninfected).
# Import necessary libraries import tensorflow as tf # Deep learning framework import os # File system operations import cv2 # OpenCV for image processing import numpy as np # Numerical computations # Load the trained model from the saved file best_model_file = "e:/temp/Malaria_binary.h5" model = tf.keras.models.load_model(best_model_file) # Print model summary to check the architecture print(model.summary()) # Define input image size input_shape = (124, 124) # Define class labels categories = ["infected", "uninfected"] # Function to preprocess an image before feeding it into the model def prepareImage(img): # Resize the image to match the input size expected by the model resized = cv2.resize(img, input_shape, interpolation=cv2.INTER_AREA) # Expand dimensions to match the input shape expected by TensorFlow (batch size, height, width, channels) imgResult = np.expand_dims(resized, axis=0) # Normalize pixel values to the range [0,1] imgResult = imgResult / 255.0 return imgResult # Load a test image from the specified path testImagePath = "TensorFlowProjects/Malaria Cell Classification/testInfected.jpg" img = cv2.imread(testImagePath) # Read the image using OpenCV # Prepare the image for prediction imgForModel = prepareImage(img) # Run prediction using the trained model result = model.predict(imgForModel, verbose=1) print(result) # Print raw prediction output # Convert the prediction into a binary classification (0 = infected, 1 = uninfected) if result > 0.5: result = 1 else: result = 0 # Print the final classification result print(result) # Assign the corresponding category label text = categories[result] # Reload the image to display the classification result img = cv2.imread(testImagePath) # Define font for text overlay font = cv2.FONT_HERSHEY_COMPLEX # Overlay the predicted label on the image cv2.putText(img, text, (0, 20), font, 1, (0, 255, 255), 2) # Yellow text # Display the image with classification result cv2.imshow("img", img) cv2.waitKey(0) # Wait for a key press before closing the image windowLink for the full code : https://ko-fi.com/s/98d022c834
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