A YOLOv3 Deep Neural Network Model to Detect Brain Tumor in Portable Electromagnetic Imaging System

Abstract

This paper presents the detection of brain tumors through the YOLOv3 deep neural network model in a portable electromagnetic (EM) imaging system. YOLOv3 is a popular object detection model with high accuracy and improved computational speed. Initially, the scattering parameters are collected from the nine-antenna array setup with a tissue-mimicking head phantom, where one antenna acts as a transmitter and the other eight antennas act as receivers. The images are then reconstructed from the post-processed scattering parameters by applying the modified delay-multiply-and-sum algorithm that contains 416×416 pixels. Fifty sample images are collected from the different head regions through the EM imaging system. The images are later augmented to generate a final image data set for training, validation, and testing, where the data set contains 1000 images, including fifty samples with a single and double tumor. 80% of the images are utilized for training the network, whereas 10% are used for validation, and the rest 10% are utilized for testing purposes. The detection performance is investigated with the different image data sets. The achieved detection accuracy and F1 scores are 95.62% and 94.50%, respectively, which ensure better detection accuracy. The training accuracy and validation losses are 96.74% and 9.21%, respectively. The tumor detection with its location in different cases from the testing images is evaluated through YOLOv3, which demonstrates its potential in the portable electromagnetic head imaging system.