If you have already extracted a feature vector $X$ from your images. Then indeed you can use an artificial neural network (ANN) to classify these given you have labelled instances.

You can do this in python using multiple different libraries such as scikit-learn, Keras, or tensorflow quite easily. Scikit-learn is the easiest to implement but the least flexible. Keras allows for more complex ANNs, and tensorflow allows for every kind of custom structure you can imagine.

Scikit-learn

This is the easiest way to implement a single-layer ANN.

from sklearn.neural_network import MLPClassifier X = [[0., 0.], [1., 1.]] y = [0, 1] clf = MLPClassifier(solver='adam', alpha=1e-5, hidden_layer_sizes=(32,), random_state=1) clf.fit(X, y) clf.score(X, y) 

https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn.neural_network.MLPClassifier

Keras

First we build a model we want to use. This is an example of a simple single-layered neural network. $k$ is the number of features per instance and $num_classes$ is the number of output classes you are expecting.

from __future__ import print_function import keras from keras.models import Sequential from keras.layers import Dense, Flatten from keras.models import model_from_json from keras import backend as K input_shape = (k,) model = Sequential() model.add(Dense(32, activation='tanh', input_shape=input_shape)) model.add(Dense(num_classes, activation='linear')) model.compile(loss=keras.losses.mean_squared_error, optimizer=keras.optimizers.Adadelta(), metrics=['accuracy']) 

You can see the summary of the model by using

model.summary() 

Then we can train this model using

batch_size = 128 epochs = 10 model.fit(x_train, y_train_binary, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test_binary)) 

You will need to tune all the hyper-parameters in this code sample to better suit your data.

https://keras.io/api/models/sequential/