Brain tumour detection in magnetic resonance imaging using Levenberg–Marquardt backpropagation neural network

Abstract Magnetic resonance imaging (MRI) is a high‐quality medical image that is used to detect brain tumours in a complex and time‐consuming manner. In this study, a back propagation neural network (BPNN) along with the Levenberg–Marquardt algorithm (LMA) is proposed to classify MRIs and diagnose brain tumours in a simple and fast process. The BPNN has 10 neurons in the hidden layer, and the default function of the feedforward feeds is mean squared error (MSE). The LMA is optimized as a multivariable adaptive approach and considerably decreases the MSE of the BPNN, so the errors of the tumour classification are diminished. The proposed method follows four steps including preprocessing, skull removal, feature extraction, and classification. The input MRIs are converted to greyscale, resized, and thresholding is performed in the preprocessing step and followed by skull removal. Morphological operations of closing, opening, and dilation are used to segment abnormal areas in the MRIs, and the opening operator recognizes the tumour more accurately. Using statistical analysis and a grey‐level co‐occurrence matrix (GLCM) 12 features are extracted from the MRIs and used as the inputs of the BPNN. To evaluate the proposed method, 670 normal and 670 abnormal brain MRIs are used as input data, and the classification is performed in 0.494 s. The accuracy, sensitivity, specificity, precision, dice, recall, and MSE are 98.7%, 97.61%, 99.7%, 97.61%, 98.6%, 97.61%, and 0.005, respectively. The approach is accurate and fast for medical images classification

An approximate CNTFET 4:2 compressor based on gate diffusion input and dynamic threshold

Here, a new 4:2 approximate compressor is presented by the gate diffusion input (GDI) technique. Although GDI cells suffer from threshold voltage drop, the dynamic threshold approach and carbon nanotube field-effect transistors are merged to overcome the mentioned problem. The proposed cell has full-swing outputs, while its error and power delay product are at low rates. Therefore, low voltage multipliers that are used in image processing can benefit from the proposed compressor.Scopu

Voltage over‐scaling CNT‐based 8‐bit multiplier by high‐efficient GDI‐based counters

Abstract A new low‐power and high‐speed multiplier is presented based on the voltage over scaling (VOS) technique and new 5:3 and 7:3 counter cells. The VOS reduces power consumption in digital circuits, but different voltage levels of the VOS increase the delay in different stages of a multiplier. Hence, the proposed counters are implemented by the gate‐diffusion input technique to solve the speed limitation of the VOS‐based circuits. The proposed GDI‐based 5:3 and 7:3 counters save power and reduce the area by 2x and 2.5x, respectively. To prevent the threshold voltage (Vth) drop in the suggested GDI‐based circuits, carbon nanotube field‐effect transistor (CNTFET) technology is used. In the counters, the chirality vector and tubes of the CNTFETs are properly adjusted to attain full‐swing outputs with high driving capability. Also, their validation against heat distribution under different time intervals, as a major issue in the CNTFET technology is investigated, and their very low sensitivity is confirmed. The low complexity, high stability and efficient performance of the presented counter cells introduce the proposed VOS‐CNTFET‐GDI‐based multiplier as an alternative to the previous designs