Why is my CNN/Image Classifier model accuracy so low?

Question

I'm currently trying to build a CNN that can detect whether a patient has pnemonia caused by covid or not, and no matter what parameters I change the model accuracy is staying at 49%/50% so its basically useless because it's the same as a coin flip. Here is my code, I thought I would try using the VGG-16 model.

from tensorflow.keras.optimizers import Adam
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, GlobalAveragePooling2D
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from keras.preprocessing.image import ImageDataGenerator

# Loading in the dataset
traindata = ImageDataGenerator(rescale=1/255)
trainingdata = traindata.flow_from_directory(
    directory="Covid-19CT/TrainingData",
    target_size=(224,224),
    batch_size=100,
    class_mode="binary")
testdata = ImageDataGenerator(rescale=1/255)
testingdata = testdata.flow_from_directory(
    directory="Covid-19CT/TestingData",
    target_size=(224,224),
    batch_size=100,
    class_mode="binary")

# Initialize the model w/ Sequential & add layers + input and output <- will refer to the VGG 16 model architecture
model = Sequential()

model.add(Conv2D(input_shape=(224,224,3),filters=64,kernel_size=(2,2),padding="same", activation="relu"))
model.add(Conv2D(filters=64, kernel_size=(3,3), padding="same", activation ="relu"))
model.add(MaxPool2D(pool_size=(2,2), strides=2))

model.add(Conv2D(filters=128, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=128, kernel_size=(3,3), padding="same", activation="relu"))
model.add(MaxPool2D(pool_size=(2,2), strides=2))

model.add(Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"))
model.add(MaxPool2D(pool_size=(2,2), strides=2))

model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(MaxPool2D(pool_size=(2,2), strides=2))

model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(MaxPool2D(pool_size=(2,2), strides=2))

model.add(GlobalAveragePooling2D())
model.add(Dense(units=4096, activation="relu"))
model.add(Dense(units=4096, activation="relu"))
model.add(Dense(units=1000, activation="relu"))
model.add(Dense(units=1, activation="softmax"))

# Compile the model
model_optimizer = Adam(lr=0.001)
model.compile(optimizer=model_optimizer, loss=keras.losses.binary_crossentropy, metrics=['accuracy'])
# Add the callbacks
checkpoint = ModelCheckpoint(filepath="Covid-19.hdf5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto')
early = EarlyStopping(monitor='val_acc', min_delta=0, patience=50, verbose=1, mode='auto')
fit = model.fit_generator(steps_per_epoch=25, generator=trainingdata, validation_data=testingdata, validation_steps=10,epochs=10,callbacks=[checkpoint,early])

This always gives:

Epoch 1/10 6/25 [======>.......................] - ETA: 1:22:37 - loss: 7.5388 - accuracy: 0.5083

<- Well, it just always gives a really poor accuracy...

Additional info:

• Some of the images in the data set are JPG others are PNG (Not sure if this is the culprit)
• The Dataset has 2072 images for training Covid CTs and 2098 images for training NonCovid CTs
• The Dataset has 576 images for testing Covid CTs and 532 images for testing NonCovid CTs
• File structure looks like this: Covid19ModelImages -> Training Data & Testing Data - Training Data has 2 subfolders Covid19CT and noncovid19 CT and testing data also has 2 subfolders Covid19CT and noncovid19CT
• Also: Am I just being too impatient? I never let it run past the 1st epoch cause I just assume its never going to get better than 50%, could it be that the model will improve more on the next epochs?

If anyone would be willing to help out, or if you need any other additional info to maybe help you gain a better understanding of the problem, please let me know!

Answer 1

Since you are using binary cross entropy, the activation function in the dense layer with 1 unit should be "sigmoid". Since you are not using a GPU you have very long training times per epoch. To see if the model is working correctly you may want to reduce this time. There are few things you could do. Try reducing the image size say to 128 by 128. With 224 X 224 you have 50176 pixels to process versus 16384 for the 128 X 128 image so you reduce the computations by about a factor of 3. Also you have two dense layers with 4096 units. This is also computationally expense. It may also lead to overfitting. Try your model initially without these layers and see how it performs. I am not a fan of early stopping because it is a crutch to avoid dealing with the over fitting issue. If you encounter over fitting add a dropout layer to help avoid it. Finally I recommend you use an adjustable learning rate. The callback ReduceLROnPlateau makes this easy to do. Set it to monitor validation loss. You can set the parameters to reduce the learning rate a factor<1 if the loss fails to decrease after "patience" number of consecutive epochs. I usually use factor=.5 and patience=1. This also enables you to use a larger initial learning rate for faster convergence. Documentation is here. You need to let your model run for several epochs to see if the training loss and validation loss are decreasing.