A Lightweight Deep Learning Based Microwave Brain Image Network Model for Brain Tumor Classification Using Reconstructed Microwave Brain (RMB) Images

Abstract

Computerized brain tumor classification from the reconstructed microwave brain (RMB) images is important for the examination and observation of the development of brain disease. In this paper, an eight-layered lightweight classifier model called microwave brain image network (MBINet) using a self-organized operational neural network (Self-ONN) is proposed to classify the reconstructed microwave brain (RMB) images into six classes. Initially, an experimental antenna sensor-based microwave brain imaging (SMBI) system was implemented, and RMB images were collected to create an image dataset. It consists of a total of 1320 images: 300 images for the non-tumor, 215 images for each single malignant and benign tumor, 200 images for each double benign tumor and double malignant tumor, and 190 images for the single benign and single malignant tumor classes. Then, image resizing and normalization techniques were used for image preprocessing. Thereafter, augmentation techniques were applied to the dataset to make 13,200 training images per fold for 5-fold cross-validation. The MBINet model was trained and achieved accuracy, precision, recall, F1-score, and specificity of 96.97%, 96.93%, 96.85%, 96.83%, and 97.95%, respectively, for six-class classification using original RMB images. The MBINet model was compared with four Self-ONNs, two vanilla CNNs, ResNet50, ResNet101, and DenseNet201 pre-trained models, and showed better classification outcomes (almost 98%). Therefore, the MBINet model can be used for reliably classifying the tumor(s) using RMB images in the SMBI system.

Mathematical Modeling and Statistical Exploration of Residual Computing Based Convolutional Neural Network Based Classifier for Complex Image Demarcation

“…Hossain et al [14] proposed that in order to categorize the reconstructed microwave brain pictures into six classes, this research offers an eight-layered light weight classifier model termed microwave brain image network utilizing a self-organized operational neural network. Aarthi et al [15] proposed a study aims to design an automated brain tumor detection system using a segmentation based classification method.…”

Design and assessment of improved Convolutional Neural Network based brain tumor segmentation and classification system

“…In terms of recall (0.9913), precision (0.9906), accuracy (99.20%), and F1-Score in the CE-MRI dataset, the proposed BRAIN-RENet and HOG feature spaces along with a classification algorithm greatly exceed state-of-the-art approaches. In 2022, Hossain et al 15 reported that in order to categorize the reconstructed microwave brain (RMB) photographs into six categories, an eight-layered lightweight classifier model dubbed the microwave brain image network (MBINet) employing a self-organized operational neural network (Self-ONN) is required. 1320 RMB pictures were collected and stored using an experimental antenna sensor-based microwave brain imaging (SMBI) method.…”

BT Detection Using Improved Whale Optimization and Convolutional Neural Networks

“…To achieve accurate BTD, level set segmentation and threshold steps are then applied. In terms of low calculation time, the proposed method can benefit from K-means clustering of image segmentation [11].…”