  Deep learning forms a hierarchical network structure for representation of
multiple input features. The adaptive structural learning method of Deep Belief
Network (DBN) can realize a high classification capability while searching the
optimal network structure during the training. The method can find the optimal
number of hidden neurons for given input data in a Restricted Boltzmann Machine
(RBM) by neuron generation-annihilation algorithm. Moreover, it can generate a
new hidden layer in DBN by the layer generation algorithm to actualize a deep
data representation. The proposed method showed higher classification accuracy
for image benchmark data sets than several deep learning methods including
well-known CNN methods. In this paper, a new object detection method for the
DBN architecture is proposed for localization and category of objects. The
method is a task for finding semantic objects in images as Bounding Box
(B-Box). To investigate the effectiveness of the proposed method, the adaptive
structural learning of DBN and the object detection were evaluated on the Chest
X-ray image benchmark data set (CXR8), which is one of the most commonly
accessible radio-logical examination for many lung diseases. The proposed
method showed higher performance for both classification (more than 94.5%
classification for test data) and localization (more than 90.4% detection for
test data) than the other CNN methods.
