Irfan Ahmed Usmani^1,*, Muhammad Tahir Qadri¹, Razia Zia¹, Asif Aziz², Farheen Saeed³

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4369-4392, 2022, DOI:10.32604/cmc.2022.030698

Abstract Knowledge-based transfer learning techniques have shown good performance for brain tumor classification, especially with small datasets. However, to obtain an optimized model for targeted brain tumor classification, it is challenging to select a pre-trained deep learning (DL) model, optimal values of hyperparameters, and optimization algorithm (solver). This paper first presents a brief review of recent literature related to brain tumor classification. Secondly, a robust framework for implementing the transfer learning technique is proposed. In the proposed framework, a Cartesian product matrix is generated to determine the optimal values of the two important hyperparameters: batch size and learning rate. An extensive… More >

R. Priyadharsini^*, A. Kunthavai

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 261-279, 2023, DOI:10.32604/iasc.2023.025884

Abstract Radio waves and strong magnetic fields are used by Magnetic Resonance Imaging (MRI) scanners to detect tumours, wounds and visualize detailed images of the human body. Wi-Fi and other medical devices placed in the MRI procedure room produces RF noise in MRI Images. The RF noise is the result of electromagnetic emissions produced by Wi-Fi and other medical devices that interfere with the operation of the MRI scanner. Existing techniques for RF noise mitigation involve RF shielding techniques which induce eddy currents that affect the MRI image quality. RF shielding techniques are complex and lead to RF leakage. VLC (Visible… More >

Bin Zhang, Yanjun Jin^*

Energy Engineering, Vol.119, No.4, pp. 1467-1482, 2022, DOI:10.32604/ee.2022.018233

Abstract Under complex grid conditions, the grid voltage usually has an imbalance, low order harmonics, and a small of DC bias. When the grid voltage contains low order harmonics and a small amount of DC bias component, the inverter's output current cannot meet the grid connection requirements, and there is a three-phase current imbalance in the control strategy of common VSG under unbalanced voltage. A theoretical analysis of non-ideal power grids is carried out, and a VSG control strategy under complex operating conditions is proposed. Firstly, the third-order generalized integrator (TOGI) is used to eliminate the influence of the DC component… More >

Abdullah A. Asiri¹, Amna Iqbal², Javed Ferzund², Tariq Ali^2,*, Muhammad Aamir², Khalaf A. Alshamrani¹, Hassan A. Alshamrani¹, Fawaz F. Alqahtani¹, Muhammad Irfan³, Ali H. D. Alshehri¹

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 641-655, 2022, DOI:10.32604/cmc.2022.029000

Abstract Abnormal growth of brain tissues is the real cause of brain tumor. Strategy for the diagnosis of brain tumor at initial stages is one of the key step for saving the life of a patient. The manual segmentation of brain tumor magnetic resonance images (MRIs) takes time and results vary significantly in low-level features. To address this issue, we have proposed a ResNet-50 feature extractor depended on multilevel deep convolutional neural network (CNN) for reliable images segmentation by considering the low-level features of MRI. In this model, we have extracted features through ResNet-50 architecture and fed these feature maps to… More >

Muhammad I. Masud^1,2,*, Nasir Shaikh-Husin², Iqbal A. Khan¹, Abu K. Bin A’Ain²

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2135-2149, 2022, DOI:10.32604/cmc.2022.026831

Abstract A new carbon nanotube field effect transistor (CNTFET) based grounded active inductor (GAI) circuit is presented in this work. The suggested GAI offers a tunable inductance with a very wide inductive bandwidth, high quality factor (QF) and low power dissipation. The tunability of the realized circuit is achieved through CNTFET based varactor. The proposed topology shows inductive behavior in the frequency range of 0.1–101 GHz and achieves to a maximum QF of 9125. The GAI operates at 0.7 V with 0.337 mW of power consumption. To demonstrate the performance of GAI, a broadband low noise amplifier (LNA) circuit is designed by utilizing… More >

B. Priestly Shan¹, O. Jeba Shiney¹, Sharzeel Saleem², V. Rajinikanth³, Atef Zaguia⁴, Dilbag Singh^5,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2749-2763, 2022, DOI:10.32604/cmc.2022.023057

Abstract Low contrast of Magnetic Resonance (MR) images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis. State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images. Drastic changes in brightness features, induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings. To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well. This method termed as Power-law and Logarithmic Modification-based Histogram Equalization (PLMHE) partitions the histogram of the image into two… More >

J. Anitha^*, M. Kalaiarasu

Computer Systems Science and Engineering, Vol.43, No.1, pp. 363-379, 2022, DOI:10.32604/csse.2022.022402

Abstract Digital Image Processing (DIP) is a well-developed field in the biological sciences which involves classification and detection of tumour. In medical science, automatic brain tumor diagnosis is an important phase. Brain tumor detection is performed by Computer-Aided Diagnosis (CAD) systems. The human image creation is greatly achieved by an approach namely medical imaging which is exploited for medical and research purposes. Recently Automatic brain tumor detection from MRI images has become the emerging research area of medical research. Brain tumor diagnosis mainly performed for obtaining exact location, orientation and area of abnormal tissues. Cancer and edema regions inference from brain… More >

S. Sreedhar Kumar¹, Syed Thouheed Ahmed^2,*, Qin Xin³, S. Sandeep⁴, M. Madheswaran⁵, Syed Muzamil Basha²

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 281-299, 2022, DOI:10.32604/cmc.2022.023693

Abstract This paper presents, a new approach of Medical Image Pixels Clustering (MIPC), aims to trace the dissimilar patterns over the Magnetic Resonance (MR) image through the process of automatically identify the appropriate number of distinct clusters based on different improved unsupervised clustering schemes for enrichment, pattern predication and deeper investigation. The proposed MIPC consists of two stages: clustering and validation. In the clustering stage, the MIPC automatically identifies the distinct number of dissimilar clusters over the gray scale MR image based on three different improved unsupervised clustering schemes likely improved Limited Agglomerative Clustering (iLIAC), Dynamic Automatic Agglomerative Clustering (DAAC) and… More >

Yanyue Li¹, Changlong Liu¹, Wenbo Bao^1,*, Baoqing Xue¹, Peng Lv¹, Nan Wang¹, Hui Li¹, Wenguo Ma²

FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.3, pp. 737-749, 2022, DOI:10.32604/fdmp.2022.019139

Abstract

Due to long-term water injection, often oilfields enter the so-called medium and high water cut stage, and it is difficult to achieve good oil recovery and water reduction through standard methods (single profile control and flooding measures). Therefore, in this study, a novel method based on “plugging, profile control, and flooding” being implemented at the same time is proposed. To assess the performances of this approach, physical simulations, computer tomography, and nuclear magnetic resonance are used. The results show that the combination of a gel plugging agent, a polymer microsphere flooding agent, and a high-efficiency oil displacement agent leads to… More >

Duraimurugan Nagendiran^1,*, S. P. Chokkalingam²

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 983-996, 2022, DOI:10.32604/iasc.2022.023982

Abstract Uncontrollable growth of cells may lead to brain tumors and may cause permanent damages to the brain or even death. To make early diagnosis and treatment, identifying the position and size of tumors is identified as a tedious and troublesome problem among the existing computer-aided diagnosis systems. Moreover, the progression of tumors may vary among the patients with respect to shape, location, and volume. Therefore, to effectively classify and diagnose the brain tumor images according to severity stages follows the sequence of processing such as pre-processing, segmentation, feature extraction, and classification techniques to carrying out the appropriate treatment. To enhance… More >