P. Malathi^1,*, G. Charlyn Pushpa Latha²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1741-1758, 2023, DOI:10.32604/csse.2023.035185 - 09 February 2023

Abstract Breast cancer (BCa) is a leading cause of death in the female population across the globe. Approximately 2.3 million new BCa cases are recorded globally in females, overtaking lung cancer as the most prevalent form of cancer to be diagnosed. However, the mortality rates for cervical and BCa are significantly higher in developing nations than in developed countries. Early diagnosis is the only option to minimize the risks of BCa. Deep learning (DL)-based models have performed well in image processing in recent years, particularly convolutional neural network (CNN). Hence, this research proposes a DL-based CNN… More >

Ghada Atteia¹, El-Sayed M. El-kenawy^2,3, Nagwan Abdel Samee^1,*, Mona M. Jamjoom⁴, Abdelhameed Ibrahim⁵, Abdelaziz A. Abdelhamid^6,7, Ahmad Taher Azar^8,9, Nima Khodadadi^10,11, Reham A. Ghanem¹², Mahmoud Y. Shams¹³

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1883-1900, 2023, DOI:10.32604/cmc.2023.031723 - 06 February 2023

Abstract The rapid population growth results in a crucial problem in the early detection of diseases in medical research. Among all the cancers unveiled, breast cancer is considered the second most severe cancer. Consequently, an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical diagnoses. Utilizing recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective, reliable, and rapid responses, which could help in decreasing the death risk. In this paper, we propose… More >

Muhammad Zia Ur Rehman¹, Jawad Ahmad^2,*, Emad Sami Jaha³, Abdullah Marish Ali³, Mohammed A. Alzain⁴, Faisal Saeed⁵

Computer Systems Science and Engineering, Vol.46, No.1, pp. 897-911, 2023, DOI:10.32604/csse.2023.035382 - 20 January 2023

Abstract Breast cancer is one of the leading cancers among women. It has the second-highest mortality rate in women after lung cancer. Timely detection, especially in the early stages, can help increase survival rates. However, manual diagnosis of breast cancer is a tedious and time-consuming process, and the accuracy of detection is reliant on the quality of the images and the radiologist’s experience. However, computer-aided medical diagnosis has recently shown promising results, leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early stages. The research presented… More >

S. J. K. Jagadeesh Kumar¹, P. Parthasarathi², Mofreh A. Hogo³, Mehedi Masud⁴, Jehad F. Al-Amri⁵, Mohamed Abouhawwash^6,7,*

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2363-2378, 2023, DOI:10.32604/iasc.2023.033800 - 05 January 2023

Abstract Women from middle age to old age are mostly screened positive for Breast cancer which leads to death. Times over the past decades, the overall survival rate in breast cancer has improved due to advancements in early-stage diagnosis and tailored therapy. Today all hospital brings high awareness and early detection technologies for breast cancer. This increases the survival rate of women. Though traditional breast cancer treatment takes so long, early cancer techniques require an automation system. This research provides a new methodology for classifying breast cancer using ultrasound pictures that use deep learning and the… More >

YUANYUAN HUANG^#, HONGMEI WU^#, RUYUAN LIU, SONG JIN, WEILAI XIANG, CHANG YANG, LI XU, XIAONIAN ZHU^*

BIOCELL, Vol.47, No.3, pp. 445-454, 2023, DOI:10.32604/biocell.2023.025880 - 03 January 2023

Abstract The tripartite motif (TRIMs) protein family has E3 ubiquitin ligase activity among most of its members. They participate in multiple cellular processes and signaling pathways in living organisms, including cell cycle, growth, and metabolism, and mediate chromatin modification, transcriptional regulation, post-translational modification, and cellular autophagy. Previous studies have confirmed that the TRIMs protein family is involved in the development of various cancers and correlated with the prognosis of tumor patients. Here we summarize the biological roles of the TRIMs protein family in cancers. More >

JING LUO^1,2,#, JIANPING HE^1,#, YONG LUO^1,*, CHENG YI^1,*

BIOCELL, Vol.47, No.2, pp. 309-318, 2023, DOI:10.32604/biocell.2023.025377 - 18 November 2022

Abstract Chondroitin sulfate synthase 2 (CHPF) is characterized as an oncogenic and poor prognosis-related gene in breast cancer. However, this gene has alternative splicing products encoding proteins of different lengths. Breast cancer is a group of heterogeneous tumors with distinct clinical and genomic characteristics. In this study, we explored the expression profile and prognostic value of the two transcripts of CHPF using data from The Cancer Genome Atlas (TCGA)-BRCA. The functional regulation of the two transcripts was also studied in MCF-7 and BT-474 cells. Among the two transcripts of CHPF, ENST00000535926 expression was significantly upregulated in the tumor… More >

YOULIN TUO, XUBAO LIU^*

BIOCELL, Vol.47, No.2, pp. 319-328, 2023, DOI:10.32604/biocell.2023.025250 - 18 November 2022

Abstract Retinoic acid receptor responder 3 (RARRES3) has been characterized as a tumor suppressor in multiple types of cancer. This study aimed to examine the expression profile of RARRES3 across the PAM50 subtypes of breast cancer. The DNA methylation status of RARRES3 was checked in the basal-like subtype, and the underlying mechanisms of its dysregulation were explored. RNA-sequencing (seq) and methylation data from The Cancer Genome Atlas were used for in-silico analysis. Basal-like representative SUM149 and MDA-MB-468 cell lines were used for in vitro and in vivo studies. Compared to tumor-adjacent normal tissues, only the basal-like tumor tissues had… More >

Erkan Akkur¹, Fuat TURK^2,*, Osman Erogul¹

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1017-1031, 2023, DOI:10.32604/csse.2023.033003 - 03 November 2022

Abstract Breast cancer seriously affects many women. If breast cancer is detected at an early stage, it may be cured. This paper proposes a novel classification model based improved machine learning algorithms for diagnosis of breast cancer at its initial stage. It has been used by combining feature selection and Bayesian optimization approaches to build improved machine learning models. Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Ensemble Learning and Decision Tree approaches were used as machine learning algorithms. All experiments were tested on two different datasets, which are Wisconsin Breast Cancer Dataset (WBCD) and Mammographic Breast… More >

Muhammad Junaid Umer¹, Muhammad Sharif¹, Majed Alhaisoni², Usman Tariq³, Ye Jin Kim⁴, Byoungchol Chang^5,*

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1001-1016, 2023, DOI:10.32604/csse.2023.030463 - 03 November 2022

Abstract Breast cancer (BC) is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year. The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6% of total cases. Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths. The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis. Manual diagnosis of BC is a complex and challenging task. This work… 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 >