Artificial Intelligence Scheme for Medical Images Classification and Prediction

Abstract

Medicine is the industry where smart technologies and artificial intelligence are most commonly used. Medical imaging is usually used for tumor diagnosis; this includes Computer Tomography and Magnetic Resonance Imaging. Early tumor detection in various organs based on such images is important. This study intended to present an Adaptive Convolution Neural Networks (ACNN) based method for tumor detection in the brain. The ACNN will utilize a modified stochastic gradient descent (MSGD) training algorithm with adaptive momentum and learning rates to speed up the convergence of the error, which will speed up the classification process and improve the accuracy. MSGD is implemented such as when the loss increases, the learning rate increase, and vice versa. The proposed modifications allow the network to increase the learning rate at the beginning of the training process and slow down as the network outcomes reach stabilized conditions. The proposed method results were compared against the performance of several conventional combinations of CNN with several machine learning classifiers. The test results show that the proposed method outperformed the performance of the CNN with all the above-said adaptations. Accordingly, the contributions of this study are (1) improving the ACNN training algorithm for the tumor classification problem and (2) proposing original CNN architectures specialized for tumor classification.