Improving misclassification for one class in a multi-class classification task

Here I am trying to use 3 convolution layer neural network to classify a set of images (train data: (3249) , validation data: (487), test data: (326))

I have one class which is misclassified and I cannot understand what to do next. I have tried to reduce the value of dropout layer, but results got worst.

I know that the next solutions could be useful if I had bad classification for all classes :

• Get more data

• Try New model architecture, try something better.

• Decrease number of features (you may need to do this manually)

• Introduce regularization such as the L2 regularization

• Make your network shallower (less layers)

• Use less number of hidden units

What do you thing could be a good choice if I have only misclassifcation of one class?
Number of total samples per class :

Black rot: 1180

Esca: 1383

healthy: 423

leaf blight: 1076

I had split the two datasets as follow:

x_train, _x, y_train, _y = train_test_split(x,y,test_size=0.2, stratify = y, random_state = 1)
x_valid,x_test, y_valid, y_test = train_test_split(_x,_y,test_size=0.4, stratify = _y, random_state = 1)