Pattern recognition (psychology)

Mathematics

Computer science

Computational Science and Engineering

Machine learning

Philosophy

Classifier (UML)

Mechanism (biology)

Deep learning

Correlation

Artificial intelligence

Geometry

Artificial neural network

Epistemology

Convolutional neural network

Convolution (computer science)

Pooling

Neural Computing and Applications

Modern medical clinics support medical examinations with computer systems which use Computational Intelligence on the way to detect potential health problems in more efficient way. One of the most important applications is evaluation of CT brain scans, where the most precise results come from deep learning approaches. In this article, we propose a novel correlation learning mechanism (CLM) for deep neural network architectures that combines convolutional neural network (CNN) with classic architecture. The support neural network helps CNN to find the most adequate filers for pooling and convolution layers. As a result, the main neural classifier learns faster and reaches higher efficiency. Results show that our CLM model is able to reach about 96% accuracy, and about 95% precision and recall. We have described our proposed mechanism and discussed numerical results to draw conclusions and show future works.

Deep neural network correlation learning mechanism for CT brain tumor detection