Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria

+ +

Done during Google Code-In. Org: Tensorflow.

+ +

Imports

+ +

%tensorflow_version 2.x #This is for telling Colab that you want to use TF 2.0, ignore if running on local machine
+
+from PIL import Image # We use the PIL Library to resize images
+import numpy as np
+import os
+import cv2
+import tensorflow as tf
+from tensorflow.keras import datasets, layers, models
+import pandas as pd
+import matplotlib.pyplot as plt
+from keras.models import Sequential
+from keras.layers import Conv2D,MaxPooling2D,Dense,Flatten,Dropout
+

+ +

Dataset

+ +

Fetching the Data

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!wget ftp://lhcftp.nlm.nih.gov/Open-Access-Datasets/Malaria/cell_images.zip
+!unzip cell_images.zip
+

+ +

Processing the Data

+ +

We resize all the images as 50x50 and add the numpy array of that image as well as their label names (Infected or Not) to common arrays.

+ +

data = []
+labels = []
+
+Parasitized = os.listdir("./cell_images/Parasitized/")
+for parasite in Parasitized:
+    try:
+        image=cv2.imread("./cell_images/Parasitized/"+parasite)
+        image_from_array = Image.fromarray(image, 'RGB')
+        size_image = image_from_array.resize((50, 50))
+        data.append(np.array(size_image))
+        labels.append(0)
+    except AttributeError:
+        print("")
+
+Uninfected = os.listdir("./cell_images/Uninfected/")
+for uninfect in Uninfected:
+    try:
+        image=cv2.imread("./cell_images/Uninfected/"+uninfect)
+        image_from_array = Image.fromarray(image, 'RGB')
+        size_image = image_from_array.resize((50, 50))
+        data.append(np.array(size_image))
+        labels.append(1)
+    except AttributeError:
+        print("")
+

+ +

Splitting Data

+ +

df = np.array(data)
+labels = np.array(labels)
+(X_train, X_test) = df[(int)(0.1*len(df)):],df[:(int)(0.1*len(df))]
+(y_train, y_test) = labels[(int)(0.1*len(labels)):],labels[:(int)(0.1*len(labels))]
+

+ +

s=np.arange(X_train.shape[0])
+np.random.shuffle(s)
+X_train=X_train[s]
+y_train=y_train[s]
+X_train = X_train/255.0
+

+ +

Model

+ +

Creating Model

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By creating a sequential model, we create a linear stack of layers.

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Note: The input shape for the first layer is 50,50 which corresponds with the sizes of the resized images

+ +

model = models.Sequential()
+model.add(layers.Conv2D(filters=16, kernel_size=2, padding='same', activation='relu', input_shape=(50,50,3)))
+model.add(layers.MaxPooling2D(pool_size=2))
+model.add(layers.Conv2D(filters=32,kernel_size=2,padding='same',activation='relu'))
+model.add(layers.MaxPooling2D(pool_size=2))
+model.add(layers.Conv2D(filters=64,kernel_size=2,padding="same",activation="relu"))
+model.add(layers.MaxPooling2D(pool_size=2))
+model.add(layers.Dropout(0.2))
+model.add(layers.Flatten())
+model.add(layers.Dense(500,activation="relu"))
+model.add(layers.Dropout(0.2))
+model.add(layers.Dense(2,activation="softmax"))#2 represent output layer neurons 
+model.summary()
+

+ +

Compiling Model

+ +

We use the Adam optimiser as it is an adaptive learning rate optimisation algorithm that's been designed specifically for training deep neural networks, which means it changes its learning rate automatically to get the best results

+ +

model.compile(optimizer="adam",
+              loss="sparse_categorical_crossentropy", 
+             metrics=["accuracy"])
+

+ +

Training Model

+ +

We train the model for 10 epochs on the training data and then validate it using the testing data

+ +

history = model.fit(X_train,y_train, epochs=10, validation_data=(X_test,y_test))
+

+ +

Train on 24803 samples, validate on 2755 samples
+Epoch 1/10
+24803/24803 [==============================] - 57s 2ms/sample - loss: 0.0786 - accuracy: 0.9729 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
+Epoch 2/10
+24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0746 - accuracy: 0.9731 - val_loss: 0.0290 - val_accuracy: 0.9996
+Epoch 3/10
+24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0672 - accuracy: 0.9764 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
+Epoch 4/10
+24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0601 - accuracy: 0.9789 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
+Epoch 5/10
+24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0558 - accuracy: 0.9804 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
+Epoch 6/10
+24803/24803 [==============================] - 57s 2ms/sample - loss: 0.0513 - accuracy: 0.9819 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
+Epoch 7/10
+24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0452 - accuracy: 0.9849 - val_loss: 0.3190 - val_accuracy: 0.9985
+Epoch 8/10
+24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0404 - accuracy: 0.9858 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
+Epoch 9/10
+24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0352 - accuracy: 0.9878 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
+Epoch 10/10
+24803/24803 [==============================] - 58s 2ms/sample - loss: 0.0373 - accuracy: 0.9865 - val_loss: 0.0000e+00 - val_accuracy: 1.0000
+

+ +

Results

+ +

accuracy = history.history['accuracy'][-1]*100
+loss = history.history['loss'][-1]*100
+val_accuracy = history.history['val_accuracy'][-1]*100
+val_loss = history.history['val_loss'][-1]*100
+
+print(
+    'Accuracy:', accuracy,
+    '\nLoss:', loss,
+    '\nValidation Accuracy:', val_accuracy,
+    '\nValidation Loss:', val_loss
+)
+

+ +

Accuracy: 98.64532351493835 
+Loss: 3.732407123270176 
+Validation Accuracy: 100.0 
+Validation Loss: 0.0
+

+ +

We have achieved 98% Accuracy!

+ +

Link to Colab Notebook

+ +