Fufang Li, Manlin Luo^*, Ming Hu, Guobin Wang, Yan Chen

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2835-2850, 2023, DOI:10.32604/csse.2023.039765

Abstract Liver cancer has the second highest incidence rate among all types of malignant tumors, and currently, its diagnosis heavily depends on doctors’ manual labeling of CT scan images, a process that is time-consuming and susceptible to subjective errors. To address the aforementioned issues, we propose an automatic segmentation model for liver and tumors called Res2Swin Unet, which is based on the Unet architecture. The model combines Attention-Res2 and Swin Transformer modules for liver and tumor segmentation, respectively. Attention-Res2 merges multiple feature map parts with an Attention gate via skip connections, while Swin Transformer captures long-range dependencies and models the input… More >

HUI LI¹, CHENGFANG ZHOU², MEI KUANG², YUN LIU^1,*, JIEPING CHEN^1,2,*

BIOCELL, Vol.47, No.10, pp. 2177-2186, 2023, DOI:10.32604/biocell.2023.029373

Abstract Fizzy-related protein homolog 1 (FZR1) mainly functions as a specific activator of the anaphase-promoting complex/cyclosome (APC/C) in the cell cycle and controls the G0 and G1 phases of the cell cycle. We highlight recent work that has studied the role of FZR1 in tumorigenesis, growth, differentiation, and genome stability through cell-cycle control. We summarize the current state of knowledge regarding FZR1 structure, function, and the distinct ways of APC/C dysregulation in solid tumors and hematologic malignancies. We also discuss novel approaches for targeting the FZR1 as a cancer therapy and research area for future work. More >

SHENGJIE SONG, ZHIZHOU SHI^*

BIOCELL, Vol.47, No.10, pp. 2207-2215, 2023, DOI:10.32604/biocell.2023.027677

Abstract Metabolic reprogramming frequently occurs in the majority of cancers, wherein fatty acid oxidation (FAO) is usually induced and serves as a compensatory mechanism to improve energy consumption. Carnitine palmitoyltransferase 1A (CPT1A) is the rate-limiting enzyme for FAO and is widely involved in tumor growth, metastasis, and chemo-/radio-resistance. This review summarizes the most recent advances in understanding the oncogenic roles and mechanisms of CPT1A in tumorigenesis, including in proliferation and tumor growth, invasion and metastasis, and the tumor microenvironment. Importantly, CPT1A has been shown to be a biomarker for diagnosis and prognosis prediction and proved to be a candidate therapeutic target,… More > Graphic Abstract

Mohammed Aly^1,*, Abdullah Shawan Alotaibi²

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 557-582, 2023, DOI:10.32604/cmc.2023.042493

Abstract Tumor segmentation is a valuable tool for gaining insights into tumors and improving treatment outcomes. Manual segmentation is crucial but time-consuming. Deep learning methods have emerged as key players in automating brain tumor segmentation. In this paper, we propose an efficient modified U-Net architecture, called EMU-Net, which is applied to the BraTS 2020 dataset. Our approach is organized into two distinct phases: classification and segmentation. In this study, our proposed approach encompasses the utilization of the gray-level co-occurrence matrix (GLCM) as the feature extraction algorithm, convolutional neural networks (CNNs) as the classification algorithm, and the chi-square method for feature selection.… More >

Tawfeeq Shawly¹, Ahmed Alsheikhy^2,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 425-443, 2023, DOI:10.32604/cmc.2023.040561

Abstract According to the World Health Organization (WHO), Brain Tumors (BrT) have a high rate of mortality across the world. The mortality rate, however, decreases with early diagnosis. Brain images, Computed Tomography (CT) scans, Magnetic Resonance Imaging scans (MRIs), segmentation, analysis, and evaluation make up the critical tools and steps used to diagnose brain cancer in its early stages. For physicians, diagnosis can be challenging and time-consuming, especially for those with little expertise. As technology advances, Artificial Intelligence (AI) has been used in various domains as a diagnostic tool and offers promising outcomes. Deep-learning techniques are especially useful and have achieved… More >

P. Kalaiselvi^1,*, S. Anusuya²

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1209-1226, 2023, DOI:10.32604/cmc.2023.040264

Abstract In recent days, Deep Learning (DL) techniques have become an emerging transformation in the field of machine learning, artificial intelligence, computer vision, and so on. Subsequently, researchers and industries have been highly endorsed in the medical field, predicting and controlling diverse diseases at specific intervals. Liver tumor prediction is a vital chore in analyzing and treating liver diseases. This paper proposes a novel approach for predicting liver tumors using Convolutional Neural Networks (CNN) and a depth-based variant search algorithm with advanced attention mechanisms (CNN-DS-AM). The proposed work aims to improve accuracy and robustness in diagnosing and treating liver diseases. The… More >

Xiaoliang Lei¹, Xiaosheng Yu^2,*, Hao Wu³, Chengdong Wu^2,*, Jingsi Zhang²

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3797-3811, 2023, DOI:10.32604/cmc.2023.042069

Abstract Accurate tumor segmentation from brain tissues in Magnetic Resonance Imaging (MRI) imaging is crucial in the pre-surgical planning of brain tumor malignancy. MRI images’ heterogeneous intensity and fuzzy boundaries make brain tumor segmentation challenging. Furthermore, recent studies have yet to fully employ MRI sequences’ considerable and supplementary information, which offers critical a priori knowledge. This paper proposes a clinical knowledge-based hybrid Swin Transformer multimodal brain tumor segmentation algorithm based on how experts identify malignancies from MRI images. During the encoder phase, a dual backbone network with a Swin Transformer backbone to capture long dependencies from 3D MR images and a… More >

Muhammad Attique Khan^1,2, Reham R. Mostafa³, Yu-Dong Zhang², Jamel Baili⁴, Majed Alhaisoni⁵, Usman Tariq⁶, Junaid Ali Khan¹, Ye Jin Kim⁷, Jaehyuk Cha^7,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3029-3047, 2023, DOI:10.32604/cmc.2023.038838

Abstract Manual diagnosis of brain tumors using magnetic resonance images (MRI) is a hectic process and time-consuming. Also, it always requires an expert person for the diagnosis. Therefore, many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the literature. This paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization algorithm. NasNet-Mobile, a pre-trained deep learning model, has been fine-tuned and two-way trained on original and enhanced MRI images. The haze-convolutional neural network (haze-CNN) approach is developed and employed on the original images for contrast enhancement.… More >

Kainat Nazir¹, Tahir Mustafa Madni¹, Uzair Iqbal Janjua¹, Umer Javed², Muhammad Attique Khan³, Usman Tariq⁴, Jae-Hyuk Cha^5,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2861-2877, 2023, DOI:10.32604/cmc.2023.039181

Abstract Brain tumor significantly impacts the quality of life and changes everything for a patient and their loved ones. Diagnosing a brain tumor usually begins with magnetic resonance imaging (MRI). The manual brain tumor diagnosis from the MRO images always requires an expert radiologist. However, this process is time-consuming and costly. Therefore, a computerized technique is required for brain tumor detection in MRI images. Using the MRI, a novel mechanism of the three-dimensional (3D) Kronecker convolution feature pyramid (KCFP) is used to segment brain tumors, resolving the pixel loss and weak processing of multi-scale lesions. A single dilation rate was replaced… More >

NAN TANG^1,#, YAJING ZHAN^1,#, JIAYAN MAO^2,#, ANKANG YIN¹, WEI WANG^3,*, JUAN WANG^3,*

BIOCELL, Vol.47, No.9, pp. 2009-2026, 2023, DOI:10.32604/biocell.2023.029269

Abstract Non-small cell lung cancer (NSCLC) is a malignant tumor with high incidence worldwide. Triptolide (TP), extracted from Tripterygium wilfordii Hook F, exhibits potent broad-spectrum antitumor activity. Although some mechanisms through which TP inhibits NSCLC are well understood, those that involve ribosomal proteins remain yet to be understood. In this study, the transcriptome and proteome were integrated and analyzed. Our data indicated ribosomal protein L4 (RPL4) to be a core hub protein in the protein-protein interaction network. RPL4 is overexpressed in NSCLC tissues and cells. Transfection with siRPL4 or TP treatment alone arrested the cell cycle in the G1 phase, induced… More > Graphic Abstract