Amjad Rehman¹, Muhammad Mujahid¹, Robertas Damasevicius^2,*, Faten S Alamri³, Tanzila Saba¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2375-2397, 2024, DOI:10.32604/cmes.2024.056937 - 31 October 2024

Abstract This study aims to develop a computational pathology approach that can properly detect and distinguish histology nuclei. This is crucial for histopathological image analysis, as it involves segmenting cell nuclei. However, challenges exist, such as determining the boundary region of normal and deformed nuclei and identifying small, irregular nuclei structures. Deep learning approaches are currently dominant in digital pathology for nucleus recognition and classification, but their complex features limit their practical use in clinical settings. The existing studies have limited accuracy, significant processing costs, and a lack of resilience and generalizability across diverse datasets. We… More >

Rabia Javed¹, Tanzila Saba², Tahani Jaser Alahmadi^3,*, Sarah Al-Otaibi⁴, Bayan AlGhofaily², Amjad Rehman²

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 809-825, 2024, DOI:10.32604/cmc.2024.052755 - 15 October 2024

Abstract Cancer poses a significant threat due to its aggressive nature, potential for widespread metastasis, and inherent heterogeneity, which often leads to resistance to chemotherapy. Lung cancer ranks among the most prevalent forms of cancer worldwide, affecting individuals of all genders. Timely and accurate lung cancer detection is critical for improving cancer patients’ treatment outcomes and survival rates. Screening examinations for lung cancer detection, however, frequently fall short of detecting small polyps and cancers. To address these limitations, computer-aided techniques for lung cancer detection prove to be invaluable resources for both healthcare practitioners and patients alike.… More >

D. Banumathy^1,*, Osamah Ibrahim Khalaf², Carlos Andrés Tavera Romero³, P. Vishnu Raja⁴, Dilip Kumar Sharma⁵

Computer Systems Science and Engineering, Vol.48, No.3, pp. 863-866, 2024, DOI:10.32604/csse.2024.053657 - 20 May 2024

Abstract This article has no abstract. More >

VIGNESH BALAJI E¹, DIVYA RAMESH², MANISHA CHUNGAN SHAJU³, AKSHARA KUMAR⁴, SAMYAK PANDEY¹, RAKSHA NAYAK¹, V. ALKA⁵, SRISHTI MUNJAL⁶, AMIR SALIMI⁷, K. SREEDHARA RANGANATH PAI^1,*, SHANKAR M. BAKKANNAVAR²

Oncology Research, Vol.32, No.1, pp. 73-94, 2024, DOI:10.32604/or.2023.030401 - 15 November 2023

Abstract Exosomes, small tiny vesicle contains a large number of intracellular particles that employ to cause various diseases and prevent several pathological events as well in the human body. It is considered a “double-edged sword”, and depending on its biological source, the action of exosomes varies under physiological conditions. Also, the isolation and characterization of the exosomes should be performed accurately and the methodology also will vary depending on the exosome source. Moreover, the uptake of exosomes from the recipients’ cells is a vital and initial step for all the physiological actions. There are different mechanisms More > Graphic Abstract

JIAWEI FU^1,2,3,#, CHUNSHUAI WU^1,2,3,#, GUANHUA XU^1,2,3, JINLONG ZHANG¹, YIQIU LI¹, CHUNYAN JI^1,2,3, ZHIMING CUI^1,2,3,*

BIOCELL, Vol.47, No.4, pp. 739-749, 2023, DOI:10.32604/biocell.2023.026881 - 08 March 2023

Abstract Spinal cord injury (SCI), a complex neurological disorder, triggers a series of devastating neuropathological events such as ischemia, oxidative stress, inflammatory events, neuronal apoptosis, and motor dysfunction. However, the classical necrosome, which consists of receptor-interacting protein (RIP)1, RIP3, and mixed-lineage kinase domain-like protein, is believed to control a novel type of programmed cell death called necroptosis, through tumour necrosis factor-alpha/tumour necrosis factor receptor-1 signalling or other stimuli. Several studies reported that necroptosis plays an important role in neural cell damage, release of intracellular pro-inflammatory factors, lysosomal dysfunction and endoplasmic reticulum stress. Recent research indicates that More >

M. Muthalakshmi^1,*, T. Merlin Inbamalar², C. Chandravathi³, K. Saravanan⁴

Computer Systems Science and Engineering, Vol.46, No.1, pp. 735-747, 2023, DOI:10.32604/csse.2023.032449 - 20 January 2023

Abstract This research work develops new and better prognostic markers for predicting Childhood MedulloBlastoma (CMB) using a well-defined deep learning architecture. A deep learning architecture could be designed using ideas from image processing and neural networks to predict CMB using histopathological images. First, a convolution process transforms the histopathological image into deep features that uniquely describe it using different two-dimensional filters of various sizes. A 10-layer deep learning architecture is designed to extract deep features. The introduction of pooling layers in the architecture reduces the feature dimension. The extracted and dimension-reduced deep features from the arrangement More >

Sukhendra Singh¹, Sur Singh Rawat, Manoj Gupta³, B. K. Tripathi⁴, Faisal Alanazi⁵, Arnab Majumdar⁶, Pattaraporn Khuwuthyakorn⁷, Orawit Thinnukool^7,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3063-3083, 2023, DOI:10.32604/cmc.2023.032363 - 31 October 2022

Abstract Currently, breast cancer has been a major cause of deaths in women worldwide and the World Health Organization (WHO) has confirmed this. The severity of this disease can be minimized to the large extend, if it is diagnosed properly at an early stage of the disease. Therefore, the proper treatment of a patient having cancer can be processed in better way, if it can be diagnosed properly as early as possible using the better algorithms. Moreover, it has been currently observed that the deep neural networks have delivered remarkable performance for detecting cancer in histopathological… More >

D. Banumathy^1,*, Osamah Ibrahim Khalaf², Carlos Andrés Tavera Romero³, P. Vishnu Raja⁴, Dilip Kumar Sharma⁵

Computer Systems Science and Engineering, Vol.44, No.1, pp. 595-612, 2023, DOI:10.32604/csse.2023.025611 - 01 June 2022

Abstract The most salient argument that needs to be addressed universally is Early Breast Cancer Detection (EBCD), which helps people live longer lives. The Computer-Aided Detection (CADs)/Computer-Aided Diagnosis (CADx) system is indeed a software automation tool developed to assist the health professions in Breast Cancer Detection and Diagnosis (BCDD) and minimise mortality by the use of medical histopathological image classification in much less time. This paper purposes of examining the accuracy of the Convolutional Neural Network (CNN), which can be used to perceive breast malignancies for initial breast cancer detection to determine which strategy is efficient… More >

LI YANG^1,2, KUN DENG³, ZHIQIANG MOU^1,2, PINGFU XIONG^1,2, JIAN WEN^1,2, JING LI^1,2,*

Oncology Research, Vol.30, No.5, pp. 243-258, 2022, DOI:10.32604/or.2022.027958 - 03 February 2023

Abstract Background: Considering the great heterogeneity of Hepatocellular carcinoma (HCC), more accurate prognostic models are urgently needed. This paper combined the advantages of genomics and pathomics to construct a prognostic model. Methods: First, we collected data from hepatocellular carcinoma patients with complete mRNA expression profiles and clinical annotations from the TCGA database. Then, based on immune-related genes, we used random forest plots to screen prognosis-related genes and build prognostic models. Bioinformatics was used to identify biological pathways, evaluate the tumor microenvironment, and perform drug susceptibility testing. Finally, we divided the patients into different subgroups according to the… More >

K. Shankar¹, E. Laxmi Lydia², Sachin Kumar^1,*, Ali S. Abosinne³, Ahmed alkhayyat⁴, A. H. Abbas⁵, Sarmad Nozad Mahmood⁶

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4541-4557, 2022, DOI:10.32604/cmc.2022.031247 - 28 July 2022

Abstract Oral Squamous Cell Carcinoma (OSCC) is a type of Head and Neck Squamous Cell Carcinoma (HNSCC) and it should be diagnosed at early stages to accomplish efficient treatment, increase the survival rate, and reduce death rate. Histopathological imaging is a wide-spread standard used for OSCC detection. However, it is a cumbersome process and demands expert’s knowledge. So, there is a need exists for automated detection of OSCC using Artificial Intelligence (AI) and Computer Vision (CV) technologies. In this background, the current research article introduces Improved Slime Mould Algorithm with Artificial Intelligence Driven Oral Cancer Classification… More >