Computer aided diagnosis with boosted learning for anomaly detection in microwave tomography

Most recently developed Computer Aided Diagnosis (CAD) systems and their related research is based on medical images that are usually obtained through conventional imaging techniques such as Magnetic Resonance Imaging (MRI), x-ray mammography, and ultrasound. With the development of a new imaging technology called Microwave Tomography Imaging (MTI), it has become inevitable to develop a CAD system that can show improved performance using the new format of data. In this paper, we propose the Multilayer Perceptron (MLP) model taking MT data as an input and we boost the learning process by using Dynamic Learning Rate (DLR) and momentum. In order to eliminate the indeterminate equation problem, we optimize the number of weights to the number of the training data. To make the model capable of escaping the local minima problem, we assign the learning rate dynamically. Since training a network with data having uneven distribution for the all possible classes can cause the network to be biased to the majority class, our model assign the low learning rate if unseen data belongs to the majority class. Along with this strategy, to speed up a back-propagation, the model employs the momentum optimizer to reduce the convergence time. In experiment, we train the model with two different datasets, 15 and 30, and evaluate the performance by the following measures; precision, recall, specificity, accuracy, and Matthews Correlation Coefficient (MCC). Differences in each measure are assessed by paired t-tests at the significance level of 0.01 for validation purposes. The results show that the proposed model outperforms the conventional model for the overall measures but only precision, accuracy, and MCC are statistically significant.