Abdullah A. Asiri¹, Toufique A. Soomro^2,3,*, Ahmed Ali⁴, Faisal Bin Ubaid⁵, Muhammad Irfan^6,*, Khlood M. Mehdar⁷, Magbool Alelyani⁸, Mohammed S. Alshuhri⁹, Ahmad Joman Alghamdi¹⁰, Sultan Alamri¹⁰

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 255-287, 2025, DOI:10.32604/cmes.2025.061683 - 11 April 2025

Abstract Global mortality rates are greatly impacted by malignancies of the brain and nervous system. Although, Magnetic Resonance Imaging (MRI) plays a pivotal role in detecting brain tumors; however, manual assessment is time-consuming and susceptible to human error. To address this, we introduce ICA2-SVM, an advanced computational framework integrating Independent Component Analysis Architecture-2 (ICA2) and Support Vector Machine (SVM) for automated tumor segmentation and classification. ICA2 is utilized for image preprocessing and optimization, enhancing MRI consistency and contrast. The Fast-Marching Method (FMM) is employed to delineate tumor regions, followed by SVM for precise classification. Validation on More >

Shanmugasundaram Hariharan¹, D. Anandan², Murugaperumal Krishnamoorthy³, Vinay Kukreja⁴, Nitin Goyal⁵, Shih-Yu Chen^6,7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 91-122, 2025, DOI:10.32604/cmes.2024.057214 - 17 December 2024

Abstract Liver cancer remains a leading cause of mortality worldwide, and precise diagnostic tools are essential for effective treatment planning. Liver Tumors (LTs) vary significantly in size, shape, and location, and can present with tissues of similar intensities, making automatically segmenting and classifying LTs from abdominal tomography images crucial and challenging. This review examines recent advancements in Liver Segmentation (LS) and Tumor Segmentation (TS) algorithms, highlighting their strengths and limitations regarding precision, automation, and resilience. Performance metrics are utilized to assess key detection algorithms and analytical methods, emphasizing their effectiveness and relevance in clinical contexts. The More >

Ahmed A. Alsheikhy¹, Ahmad S. Azzahrani¹, A. Khuzaim Alzahrani², Tawfeeq Shawly³

Computer Systems Science and Engineering, Vol.48, No.3, pp. 853-853, 2024, DOI:10.32604/csse.2024.051630 - 20 May 2024

Abstract This article has no abstract. More >

Chuandong Qin^1,2, Yu Cao^1,*, Liqun Meng¹

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1975-1994, 2024, DOI:10.32604/cmc.2024.049228 - 15 May 2024

Abstract Brain tumors come in various types, each with distinct characteristics and treatment approaches, making manual detection a time-consuming and potentially ambiguous process. Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes. Machine learning models have become key players in automating brain tumor detection. Gradient descent methods are the mainstream algorithms for solving machine learning models. In this paper, we propose a novel distributed proximal stochastic gradient descent approach to solve the L-Smooth Support Vector Machine (SVM) classifier for brain tumor detection. Firstly, the smooth hinge loss is… More >

K. Umapathi^1,*, S. Shobana¹, Anand Nayyar², Judith Justin³, R. Vanithamani³, Miguel Villagómez Galindo⁴, Mushtaq Ahmad Ansari⁵, Hitesh Panchal^6,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1875-1901, 2024, DOI:10.32604/cmc.2024.047961 - 15 May 2024

Abstract Breast cancer detection heavily relies on medical imaging, particularly ultrasound, for early diagnosis and effective treatment. This research addresses the challenges associated with computer-aided diagnosis (CAD) of breast cancer from ultrasound images. The primary challenge is accurately distinguishing between malignant and benign tumors, complicated by factors such as speckle noise, variable image quality, and the need for precise segmentation and classification. The main objective of the research paper is to develop an advanced methodology for breast ultrasound image classification, focusing on speckle noise reduction, precise segmentation, feature extraction, and machine learning-based classification. A unique approach… More >

M. Adimoolam¹, K. Maithili², N. M. Balamurugan³, R. Rajkumar⁴, S. Leelavathy⁵, Raju Kannadasan⁶, Mohd Anul Haq^7,*, Ilyas Khan⁸, ElSayed M. Tag El Din⁹, Arfat Ahmad Khan¹⁰

Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 33-55, 2024, DOI:10.32604/iasc.2024.039009 - 29 March 2024

Abstract At present, the prediction of brain tumors is performed using Machine Learning (ML) and Deep Learning (DL) algorithms. Although various ML and DL algorithms are adapted to predict brain tumors to some range, some concerns still need enhancement, particularly accuracy, sensitivity, false positive and false negative, to improve the brain tumor prediction system symmetrically. Therefore, this work proposed an Extended Deep Learning Algorithm (EDLA) to measure performance parameters such as accuracy, sensitivity, and false positive and false negative rates. In addition, these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural… More >

Nure Alam Chowdhury¹, Lulu Wang^2,*, Md Shazzadul Islam³, Linxia Gu¹, Mehmet Kaya^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1301-1322, 2024, DOI:10.32604/cmes.2023.030844 - 17 November 2023

Abstract This study designs a microstrip patch antenna with an inverted T-type notch in the partial ground to detect tumor cells inside the human breast. The size of the current antenna is small enough (18 mm × 21 mm × 1.6 mm) to distribute around the breast phantom. The operating frequency has been observed from 6–14 GHz with a minimum return loss of −61.18 dB and the maximum gain of current proposed antenna is 5.8 dBi which is flexible with respect to the size of antenna. After the distribution of eight antennas around the breast phantom, the return loss curves were observed in the presence and More > Graphic Abstract

Ahmed A. Alsheikhy^1,*, Ahmad S. Azzahrani¹, A. Khuzaim Alzahrani², Tawfeeq Shawly³

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2021-2037, 2023, DOI:10.32604/csse.2023.036107 - 09 February 2023

Abstract A brain tumor is an excessive development of abnormal and uncontrolled cells in the brain. This growth is considered deadly since it may cause death. The brain controls numerous functions, such as memory, vision, and emotions. Due to the location, size, and shape of these tumors, their detection is a challenging and complex task. Several efforts have been conducted toward improved detection and yielded promising results and outcomes. However, the accuracy should be higher than what has been reached. This paper presents a method to detect brain tumors with high accuracy. The method works using… More >

M. Uvaneshwari¹, M. Baskar^2,*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1811-1826, 2023, DOI:10.32604/csse.2023.035455 - 09 February 2023

Abstract The Brain Tumor (BT) is created by an uncontrollable rise of anomalous cells in brain tissue, and it consists of 2 types of cancers they are malignant and benign tumors. The benevolent BT does not affect the neighbouring healthy and normal tissue; however, the malignant could affect the adjacent brain tissues, which results in death. Initial recognition of BT is highly significant to protecting the patient’s life. Generally, the BT can be identified through the magnetic resonance imaging (MRI) scanning technique. But the radiotherapists are not offering effective tumor segmentation in MRI images because of… More >

P. M. Siva Raja^1,*, S. Brinthakumari², K. Ramanan³

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2589-2599, 2023, DOI:10.32604/csse.2023.028542 - 09 February 2023

Abstract Brain tumor detection and division is a difficult tedious undertaking in clinical image preparation. When it comes to the new technology that enables accurate identification of the mysterious tissues of the brain, magnetic resonance imaging (MRI) is a great tool. It is possible to alter the tumor’s size and shape at any time for any number of patients by using the Brain picture. Radiologists have a difficult time sorting and classifying tumors from multiple images. Brain tumors may be accurately detected using a new approach called Nonlinear Teager-Kaiser Iterative Infomax Boost Clustering-Based Image Segmentation (NTKFIBC-IS).… More >