Semantics Convolutional Neural Network for Medical Images Analysis

Big Data Analysis is a solution that makes it possible to extract valuable information from the mass of data by using deep learning algorithms and especially the Convolutional Neural Network algorithm. In this article, we have proposed an approach that allows the addition of the semantic aspect in the classification layer of the Convolutional Neural Network algorithm. The proposed approach helps medical professionals to develop an automatic system for identifying various classes of lung cancers. First, the input data are processed to reduce the search space, and the image noise, and normalize data. Then, the preprocessed data are analyzed to reduce image space by preserving all important features. After that, the semantic memory method converts the feature vectors from the analysis layer into semantic feature vectors. Finally, the last layer classifies the input image into two classes. We evaluate our approach using the LUNA16 dataset. Our study led to better results and predictions by reducing false negatives and positives using the Semantic Convolutional Neural Network algorithm. In our approach, cancer tissues can be identified with a maximum of 97.27% for accuracy and 99.46% for AUC. This model has increased efficiency compared with state-of-the-art approaches.