Conventional breast cancer detection techniques including X-ray mammography, magnetic resonance imaging, and ultrasound scanning suffer from shortcomings such as excessive cost, harmful radiation, and inconveniences to the patients. These challenges motivated researchers to investigate alternative methods including the use of microwaves. This article focuses on reviewing the background of microwave techniques for breast tumour detection. In particular, this study reviews the recent advancements in active microwave imaging, namely microwave tomography and radar-based techniques. The main objective of this paper is to provide researchers and physicians with an overview of the principles, techniques, and fundamental challenges associated with microwave imaging for breast cancer detection. Furthermore, this study aims to shed light on the fact that until today, there are very few commercially available and cost-effective microwave-based systems for breast cancer imaging or detection. This conclusion is not intended to imply the inefficacy of microwaves for breast cancer detection, but rather to encourage a healthy debate on why a commercially available system has yet to be made available despite almost 30 years of intensive research.

We studied the impact of 2D and 3D educational contents on learning and memory recall using electroencephalography (EEG) brain signals. For this purpose, we adopted a classification approach that predicts true and false memories in case of both short term memory (STM) and long term memory (LTM) and helps to decide whether there is a difference between the impact of 2D and 3D educational contents. In this approach, EEG brain signals are converted into topomaps and then discriminative features are extracted from them and finally support vector machine (SVM) which is employed to predict brain states. For data collection, half of sixty-eight healthy individuals watched the learning material in 2D format whereas the rest watched the same material in 3D format. After learning task, memory recall tasks were performed after 30 minutes (STM) and two months (LTM), and EEG signals were recorded. In case of STM, 97.5% prediction accuracy was achieved for 3D and 96.6% for 2D and, in case of LTM, it was 100% for both 2D and 3D. The statistical analysis of the results suggested that for learning and memory recall both 2D and 3D materials do not have much difference in case of STM and LTM.

Assessing a person's intelligence level is required in many situations such as career counseling and clinical applications. EEG evoked potentials in oddball task and fluid intelligence score are correlated because both reflect the cognitive processing and attention. A system for prediction of an individual’s fluid intelligence level using single trial Electroencephalography (EEG) signals has been proposed. For this purpose, we employed 2D and 3D contents and 34 subjects each for 2D and 3D, which were divided into low-ability (LA) and high-ability (HA) groups using Raven’s Advanced Progressive Matrices (RAPM) test. Using visual oddball cognitive task, neural activity of each group was measured and analyzed over three midline electrodes (Fz, Cz, and Pz). To predict whether an individual belongs to LA or HA group, features were extracted using wavelet decomposition of EEG signals recorded in visual oddball task and support vector machine (SVM) was used as a classifier. Two different types of Haar wavelet transform based features have been extracted from the band (0.3 to 30 Hz) of EEG signals. Statistical wavelet features and wavelet coefficient features from the frequency bands 0.0 – 1.875 Hz (delta low) and 1.875–3.75 Hz (delta high), resulted in the 100% and 98% prediction accuracies, respectively, both for 2D and 3D contents. The analysis of these frequency bands showed clear difference between LA and HA groups. Further, discriminative values of the features have been validated using statistical significance tests and inter-class and intra-class variation analysis. Also, statistical test showed that there was no effect of 2D and 3D content on the assessment of fluid intelligence level. Comparisons with state-of-the-art techniques showed the superiority of the proposed system.

In this work, we propose the use of an electrically small dipole antenna arrays as a microwave sensor combined with a classification algorithm for near field microwave breast tumor detection. The proposed sensor has high sensitive to detect any changes or abnormality in the dielectric properties of the breast tissues. Detecting the existence of the breast tumors are expected by estimating the variations in the response of the reflection coefficient of the sensor. The Principle Component Analysis (PCA) feature extraction technique is applied to accentuate the variance in the sensor responses for both healthy and tumourous cases. We show that when a numerical realistic breast with and without tumor cells is placed in the near field of the sensor, the sensor is capable of detection tumorous with various sizes placed in single locations at two different stand off 5 mm and 10 mm respectively.

In this paper, we propose a detection technique that combines a machine learning modality with microwave near-field probes for breast tumor diagnosis. The proposed technique uses a highly sensitive microwave probe to identify differences between normal and abnormal breasts. Distinguishing between healthy and non-healthy breast based on estimating the differences in the reflection coefficient of the probe response for both normal and abnormal cases. Machine learning techniques are applied to accentuate the variance in the sensor's responses for both healthy and tumorous cases. We investigated the detection of breast tumors if a woman has different breast sizes and she has an abnormality in one of them. We show that for two different breast phantom sizes, one with a tumor and one without, the sensor provides reliable detection. Simulation results of ninety different-size realistic breast phantoms (45 healthy breasts and 45 tumorous breasts) show that the proposed system provides highly encouraging reliable detection probability.