Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

• DOI: 10.3390/brainsci11030352
• Publication Date: 2021-03-10T18:27:20Z
• Authors: Isselmou Abd El K...; Guizhi Xu; Zhang Shuai; Sani Saminu; Imran Javaid; Isah Salim Ahmad
• Keywords: Feature (linguistics); Contextual image classification

The classification of brain tumors is a difficult task in the field medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose tumor without surgical intervention. In recent years, deep techniques have made excellent progress processing However, there are many difficulties classifying using magnetic resonance imaging; first, difficulty structure intertwining tissues it; secondly, due high density nature brain. We propose differential convolutional neural network model (differential deep-CNN) classify different types tumor, including abnormal normal (MR) images. Using operators deep-CNN architecture, we derived additional feature maps original CNN maps. derivation process led an improvement performance proposed approach accordance with results evaluation parameters used. advantage analysis pixel directional pattern images contrast calculations its ability large database accuracy technical problems. Therefore, gives overall performance. To test train this model, used dataset consisting 25,000 imaging (MRI) images, which includes experimental showed that achieved 99.25%. This study demonstrates can be facilitate automatic tumors.