added malaria cell classification tutorial

Author: x4nth055

README.md

@@ -46,6 +46,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy
         - [How to Perform YOLO Object Detection using OpenCV and PyTorch in Python](https://www.thepythoncode.com/article/yolo-object-detection-with-opencv-and-pytorch-in-python). ([code](machine-learning/object-detection))
         - [How to Blur Faces in Images using OpenCV in Python](https://www.thepythoncode.com/article/blur-faces-in-images-using-opencv-in-python). ([code](machine-learning/blur-faces))
         - [Skin Cancer Detection using TensorFlow in Python](https://www.thepythoncode.com/article/skin-cancer-detection-using-tensorflow-in-python). ([code](machine-learning/skin-cancer-detection))
+        - [How to Perform Malaria Cells Classification using TensorFlow 2 and Keras in Python](https://www.thepythoncode.com/article/malaria-cells-classification). ([code](machine-learning/malaria-classification))
     - [Building a Speech Emotion Recognizer using Scikit-learn](https://www.thepythoncode.com/article/building-a-speech-emotion-recognizer-using-sklearn). ([code](machine-learning/speech-emotion-recognition))
     - [How to Convert Speech to Text in Python](https://www.thepythoncode.com/article/using-speech-recognition-to-convert-speech-to-text-python). ([code](machine-learning/speech-recognition))
     - [Top 8 Python Libraries For Data Scientists and Machine Learning Engineers](https://www.thepythoncode.com/article/top-python-libraries-for-data-scientists).

machine-learning/malaria-classification/README.md

@@ -0,0 +1,3 @@
+# [How to Perform Malaria Cells Classification using TensorFlow 2 and Keras in Python](https://www.thepythoncode.com/article/malaria-cells-classification)
+To run this:
+- `pip3 install -r requirements.txt`

machine-learning/malaria-classification/malaria-classification.py

@@ -0,0 +1,112 @@
+import cv2
+import tensorflow as tf
+from tensorflow.keras.models import Sequential 
+from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten, Activation
+from sklearn.model_selection import train_test_split
+import numpy as np
+import matplotlib.pyplot as plt
+
+import glob
+import os
+
+# after you extract the dataset, 
+# put cell_images folder in the working directory
+img_dir="cell_images"  
+img_size=70
+
+def load_img_data(path):
+    image_files = glob.glob(os.path.join(path, "Parasitized/*.png")) + \
+                  glob.glob(os.path.join(path, "Uninfected/*.png"))
+    X, y = [], []
+    for image_file in image_files:
+        # 0 for uninfected and 1 for infected
+        label = 0 if "Uninfected" in image_file else 1
+        # load the image in gray scale
+        img_arr = cv2.imread(image_file, cv2.IMREAD_GRAYSCALE)
+        # resize the image to (70x70)
+        img_resized = cv2.resize(img_arr, (img_size, img_size))
+        X.append(img_resized)
+        y.append(label)
+    return X, y
+
+# load the data
+X, y = load_img_data(img_dir)
+# reshape to (n_samples, 70, 70, 1) (to fit the NN)
+X = np.array(X).reshape(-1, img_size, img_size, 1)
+# scale pixels from the range [0, 255] to [0, 1] 
+# to help the neural network learn much faster
+X = X / 255 
+
+# shuffle & split the dataset
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, stratify=y)
+print("Total training samples:", X_train.shape)
+print("Total validation samples:", X_test.shape[0])
+
+model = Sequential()
+model.add(Conv2D(64, (3, 3), input_shape=X_train.shape[1:]))
+model.add(Activation("relu"))
+model.add(MaxPool2D(pool_size=(2, 2)))
+
+model.add(Conv2D(64, (3, 3)))
+model.add(Activation("relu"))
+model.add(MaxPool2D(pool_size=(2, 2)))
+
+model.add(Conv2D(64, (3, 3)))
+model.add(Activation("relu"))
+model.add(MaxPool2D(pool_size=(2, 2)))
+
+model.add(Flatten())
+
+model.add(Dense(64))
+model.add(Activation("relu"))
+
+model.add(Dense(64))
+model.add(Activation("relu"))
+
+model.add(Dense(1))
+model.add(Activation("sigmoid"))
+
+model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
+
+# train the model with 3 epochs, 64 batch size
+model.fit(X_train, np.array(y_train), batch_size=64, epochs=3, validation_split=0.2)
+# if you already trained the model, uncomment below and comment above
+# so you can only load the previously trained model
+# model.load_weights("malaria-cell-cnn.h5")
+
+loss, accuracy = model.evaluate(X_test, np.array(y_test), verbose=0)
+print(f"Testing on {len(X_test)} images, the results are\n Accuracy: {accuracy} | Loss: {loss}")
+
+# save the model & weights
+model.save("malaria-cell-cnn.h5")
+
+# testing some images
+uninfected_cell = "cell_images/testing-samples/C1_thinF_IMG_20150604_104919_cell_82.png"
+infected_cell = "cell_images/testing-samples/C38P3thinF_original_IMG_20150621_112116_cell_204.png"
+
+_, ax = plt.subplots(1, 2)
+ax[0].imshow(plt.imread(uninfected_cell))
+ax[0].title.set_text("Uninfected Cell")
+ax[1].imshow(plt.imread(infected_cell))
+ax[1].title.set_text("Parasitized Cell")
+plt.show()
+
+img_arr_uninfected = cv2.imread(uninfected_cell, cv2.IMREAD_GRAYSCALE)
+img_arr_infected = cv2.imread(infected_cell, cv2.IMREAD_GRAYSCALE)
+# resize the images to (70x70)
+img_arr_uninfected = cv2.resize(img_arr_uninfected, (img_size, img_size))
+img_arr_infected = cv2.resize(img_arr_infected, (img_size, img_size))
+# scale to [0, 1]
+img_arr_infected = img_arr_infected / 255
+img_arr_uninfected = img_arr_uninfected / 255
+# reshape to fit the neural network dimensions
+# (changing shape from (70, 70) to (1, 70, 70, 1))
+img_arr_infected = img_arr_infected.reshape(1, *img_arr_infected.shape)
+img_arr_infected = np.expand_dims(img_arr_infected, axis=3)
+img_arr_uninfected = img_arr_uninfected.reshape(1, *img_arr_uninfected.shape)
+img_arr_uninfected = np.expand_dims(img_arr_uninfected, axis=3)
+# perform inference
+infected_result = model.predict(img_arr_infected)[0][0]
+uninfected_result = model.predict(img_arr_uninfected)[0][0]
+print(f"Infected: {infected_result}")
+print(f"Uninfected: {uninfected_result}")

machine-learning/malaria-classification/requirements.txt

@@ -0,0 +1,5 @@
+tensorflow
+sklearn
+numpy
+matplotlib
+opencv-python