Abstract
Brain tumor (BT) is a serious cancerous disease caused by an uncontrollable
and abnormal distribution of cells. Recent advances in deep learning (DL) have helped
the healthcare industry in medical imaging for the diagnosis of many diseases. One of
the major problems encountered in the automatic classification of BT when using
machine learning (ML) techniques is the availability and quality of the learning from
data; these are often inaccessible, very confidential, and of poor quality. On the other
hand, there are more than 120 types of BT [1] that we must recognize. In this paper, we
present an approach for the automatic classification of medical images (MI) of BT
using image fusion (IF) with an auto-coding technique for data augmentation (DA) and
DL. The objective is to design and develop a diagnostic support system to assist the
practitioner in analyzing never-seen BT images. To address this problem, we propose
two contributions to perform data augmentation at two different levels: before and
during the learning process. Starting from a small dataset, we conduct the first phase of
classical DA, followed by the second one based on the image fusion technique. Our
approach allowed us to increase the accuracy to a very acceptable level compared to
other methods in the literature for ten tumor classes.
Keywords: Artificial Intelligence, Autoencoder, Brain Tumor, CNN, Data Augmentation, Deep Learning, Image Fusion, Machine Learning, Medicine 4.0, Visual Recognition.