CNN with imbalanced data stuck with 70% testing accuracy - tensorflow

Question

I'm working on image classification task for diabetic retinopathy with fundus image data. There are 5 classes. The data distribution is 1805 images (class 1), 370 images (class 2), 999 images (class 3), 193 images (class 4), 295 images (class 5).
Here are the steps that I have tried to run:
Preprocessing (resized 224 * 224)
The divide of train and test data is 85% : 15%
x_train, xtest, y_train, ytest = train_test_split(
x_train, y_train,
test_size = 0.15,
random_state=SEED,
stratify = y_train
)
Data agumentation
ImageDataGenerator(
zoom_range=0.15,
fill_mode='constant',
cval=0.,
horizontal_flip=True,
vertical_flip=True,
)
Training with the ResNet-50 model and cross-validation
def getResNet():
modelres = ResNet50(weights=None, include_top=False, input_shape= (IMAGE_HEIGHT,IMAGE_HEIGHT, 3))
x = modelres.output
x = GlobalAveragePooling2D()(x)
x = Dense(5, activation= 'softmax')(x)
model = Model(inputs = modelres.input, outputs = x)
return model
num_folds = 5
skf = StratifiedKFold(n_splits = 5, shuffle=True, random_state=2021)
cvscores = []
fold = 1
for train, val in skf.split(x_train, y_train.argmax(1)):
print('Fold: ', fold)
Xtrain = x_train[train]
Xval = x_train[val]
Ytrain = y_train[train]
Yval = y_train[val]
data_generator = create_datagen().flow(Xtrain, Ytrain, batch_size=32, seed=2021)
model = getResNet()
model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=0.0001),
metrics=['accuracy'])
with tf.compat.v1.device('/device:GPU:0'):
model_train = model.fit(data_generator,
validation_data=(Xval, Yval),
epochs=30, batch_size = 32, verbose=1)
model_name = 'cnn_keras_aug_Fold_'+str(fold)+'.h5'
model.save(model_name)
scores = model.evaluate(xtest, ytest, verbose=0)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
cvscores.append(scores[1] * 100)
fold = fold +1
The maximum results I got from this method were training accuracy of 81.2%, validation accuracy of 72.2%, and test accuracy of 70.73%.
Can anyone give me an idea to improve the model so that I can get the test accuracy above 90% as possible?
Later, I will use this model as a pre-trained model to train diabetic retinopathy data as well but from other sources.
BTW, I've tried replacing my preprocessing with this method:
def preprocessing(path):
image = cv2.imread(path)
image = crop_image_from_gray(image)
green = image[:,:,1]
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
cl = clahe.apply(green)
image[:,:,0] = image[:,:,0]
image[:,:,2] = image[:,:,2]
image[:,:,1] = cl
image = cv2.resize(image, (224,224))
return image
I've also tried to replace my model with VGG16, EfficientNetB0. However, none of that had much effect on my results. I'm still stucked with about 70% accuracy.
Please help me come up with ideas to improve my modeling results. I hope.

Answer 1

Your training accuracy is 81.2%. It is generally impossible to have testing accuracy higher that training accuracy, i.e. with current setup you will not achieve 90%.
However, your validation (and also testing) accuracy is about 70-72%. I can suggest that on your small dataset your model is overfitting. So if you add model regularization (e.g. dropout), it is possible that the gap between your training and your validation (and test) will decrease. This way you can improve your validation score.
To further increase the score, you need to check your data manually and try to understand which classes contribute the most to the errors and figure out how those errors can be reduced (e.g. updating your preprocessing pipeline).