  This paper follows series of our works on the applicability of various
machine learning methods to the morphological galaxy classification (Vavilova
et al., 2021, 2022). We exploited the sample of 315776 SDSS DR9 galaxies with
absolute stellar magnitudes of -24m<Mr<-19.4m at 0.003<z<0.1 as a target data
set for the CNN classifier based on the DenseNet-201. Because it is tightly
overlapped with the Galaxy Zoo 2 (GZ2) sample, we use these annotated data as
the training data set to classify galaxies into 34 detailed features. In the
presence of a pronounced difference of visual parameters between galaxies from
the GZ2 training data set and galaxies without known morphological parameters,
we applied novel procedures, which allowed us for the first time to get rid of
this difference for smaller and fainter SDSS galaxies.
  We describe in detail the adversarial validation technique as well as how we
managed the optimal train-test split of galaxies from the training data set. We
have also found optimal galaxy image transformations to increase the classifier
generalization ability. It can be considered as another way to improve the
human bias for those galaxy images that had a poor vote classification in the
GZ project. Such an approach, likely auto-immunization, when the CNN classifier
trained on very good images is able to retrain bad images from the same
homogeneous sample, can be considered co-planar to other methods of combating
the human bias.
  The accuracy of CNN classifier is in the range of 83.3-99.4 percent depending
on 32 features. As a result, for the first time, we assigned the detailed
morphological classification for more than 140K low-redshift galaxies,
especially at the fainter end. We accentuate on the typical problem points of
galaxy CNN image classification from the astronomical point of view. The
catalogs will be available through the VizieR.
