The overall performances of the BPNN and CpNN are tested using 380 images.
Nonetheless, this deep structure is the main factor in achieving a higher recognition rate compared to other networks such as BPNN and CpNN. Lastly, Figure 9 shows an example of the CNN testing paradigm.
Firstly, it is seen that shallow (traditional) networks (BPNN and CpNN) could not achieve high recognition rates compared to other deep networks, which is obviously due to their deficiency in extracting the important features from input images.
For comparative analysis, backpropagation neural network (BPNN) and competitive neural network (CpNN) are carried out for the classification of the chest X-ray diseases.