scholarly journals A Semantic Web Rule and Ontologies Based Architecture for Diagnosing Breast Cancer Using Select and Test Algorithm

2021 ◽  
pp. 100034
Author(s):  
Olaide Nathaniel Oyelade ◽  
Irunokhai Eric Aghiomesi ◽  
Owamoyo Najeem ◽  
Ahamed Aminu Sambo
Keyword(s):  
Breast Cancer ◽  
Semantic Web ◽  
Test Algorithm

scholarly journals A Survey on Ontology-Based Systems to Support the Prospection, Diagnosis and Treatment of Breast Cancer

2016 ◽  
Author(s):  
Cassia Isac ◽  
José Viterbo ◽  
Aura Conci
Keyword(s):  
Breast Cancer ◽  
Semantic Web ◽  
Cancer Detection ◽  
Medical Images ◽  
Breast Cancer Detection ◽  
Data Sources ◽  
Diagnosis And Treatment ◽  
Huge Amount ◽  
Storage And Retrieval ◽  
Comprehensive Study

In a scenario where there is a huge amount of available data sources, the Semantic Web has played a key role in sharing, retrieval, selection, and combination of data organized in various formats. The storage and retrieval of medical images manipulated by systems that support breast cancer detection can take great advantage from the use of such technology. In this paper we present a comprehensive study on ontology-based systems that support the manipulation of medical images related to breast cancer, identifying the main features of each approach.

scholarly journals A Knowledge Representation Model Based on Select and Test Algorithm for Diagnosing Breast Cancer

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Olaide N Oyelade ◽  
Pam B Dung ◽  
Najeem Owamoyo
Keyword(s):  
Breast Cancer ◽  
Knowledge Representation ◽  
Diagnostic Systems ◽  
Knowledge Model ◽  
Representation Model ◽  
Proposed Model ◽  
Computer Aided ◽  
Improved Performance ◽  
Test Algorithm ◽  
Ambiguous Data

There exist several terminal diseases whose fatality rate escalates with time of which breast cancer is a frontline disease among such. Computer aided systems have also been well researched through the use intelligent algorithms capable of detecting, diagnosing, and proffering treatment for breast cancer.  While good research breakthrough has been attained in terms of algorithmic solution towards diagnosis of breast cancer, however, not much has been done to sufficiently model knowledge frameworks for diagnostic algorithms that are knowledge-based. While Select and Test (ST) algorithm have proven relevant for implementing diagnostic systems, through support for reasoning, however the knowledge representation pattern that enables inference of missing or ambiguous data still limits the effectiveness of ST algorithm. This paper therefore proposes a knowledge representation model to systematically model knowledge to aid the performance of ST algorithm. Our proposal is specifically targeted at developing systematic knowledge representation for breast cancer. The approach uses the ontology web language (OWL) to implement the design of the knowledge model proposed.   This study aims at carefully crafting a knowledge model whose implementation seamlessly work with ST algorithm. Furthermore, this study adapted the proposed model into an implementation of ST algorithm an obtained an improved performance compared to the simple knowledge model proposed by the author of ST algorithm. Our knowledge mode resulted in an accuracy gain of 23.5% and obtained and AUC of (0.49, 1.0). This proposed model has therefore shown that combining an inference-oriented knowledge model with an inference-oriented reasoning algorithm improves the performance of computer aided diagnostic (CADx) systems. In future, we intend to enhance the proposed model to support rules. Keywords— Semantic web, ontology, OWL, breast cancer, Select and Test (ST) algorithm, knowledge representation

scholarly journals Modified Firefly Algorithm for Optimizing Biomedical Breast Cancer Queries

2021 ◽  
Author(s):  
Gomathi Ramalingam
Keyword(s):  
Breast Cancer ◽  
Semantic Web ◽  
Execution Time ◽  
Firefly Algorithm ◽  
Optimal Solution ◽  
Query Languages ◽  
Query Execution ◽  
Web Data ◽  
Query Plan ◽  
Modified Firefly Algorithm

Abstract Querying and retrieving Semantic Web data is a challenging task due to the increment in its volume. Many query languages were designed to retrieve Semantic Web data. A popular querying method of communication in Semantic Web is SPARQL. The query languages were designed with some optimization strategies, and it was found in literature that these query languages were not able to handle large volume of data efficiently. In this research, a Modified Firefly Algorithm (MFA) is applied to optimize the SPARQL queries so that it can retrieve data from a large Semantic Web repository efficiently by reducing query execution time. Every query will have multiple query plans generated with different cost values. The challenge is to choose the best query plan which reduces the query cost and query execution time. The proposed algorithm uses the best query plan in the previous iteration to calculate the distance between two query plans using the radius parameter. The proposed algorithm generates a query plan which is a global optimal solution. MFA is evaluated using the BioPortal dataset with triples containing breast cancer. Experimental analysis is conducted to identify the significant improvement in performance of the proposed work with the existing nature inspired query optimization algorithms. The efficiency of MFA is compared with other algorithms in terms of query execution time and the performance is evaluated.

scholarly journals A Novel Clustering Algorithm Introducing New Denoising Technique

2020 ◽  
Vol 9 (3) ◽  
pp. 1841-1847
Keyword(s):  
Breast Cancer ◽  
Clustering Algorithm ◽  
Search Algorithm ◽  
Early Stage ◽  
Noise Removal ◽  
Structural Features ◽  
Experimental Results ◽  
Noise Elimination ◽  
Elimination Algorithm ◽  
Test Algorithm

Breast cancer is one of common cancers in the developing countries. Detection at an early stage is very crucial for better chance of treatment. The techniques used to detect breast cancer are complex and time consuming. Computerized extraction of tumor areas from mammogram images is challenging due to shape and density of breast tumors which can sometimes surrounded by mucous (mucin). One of the challenges is to detect boundaries which can be blurred under noise factor. In this paper, we are introducing a clustering technique combined with specific structural features operations. A new noise elimination algorithm eases the noise problem and enhance the segmentation process using discrete cosine transform. Followed is the segmentation phase where classifying breast tumor from normal tumor are performed using a combined DCT and fuzzy c means algorithm. The contributions of the research are utilizing new filtering technique for noise removal. We also use Fuzzy C mean clustering algorithm using DCT information to determine the initial number of clusters. The tumor extracted segments are then transferred to the frequency domain using DCT and is used to for classifications. A test algorithm is implemented to classify new mammograms. Experimental results for all the proposed algorithms are extensively performed. The noise removal algorithm are proven robust. The experimental results of the search algorithm depicted different match and mismatch cases. 93% of the cases were a match case and predicted correctly. 5% were light cases and could not be detected from the images.

Virus-Like Particles in a Human Breast Cancer: Structural Similarity to Previously Reported Particles

1973 ◽  
Vol 31 ◽  
pp. 378-379
Author(s):  
G. Kasnic ◽  
S. E. Stewart ◽  
C. Urbanski
Keyword(s):  
Breast Cancer ◽  
Biological Activity ◽  
Human Breast Cancer ◽  
Osteogenic Sarcoma ◽  
Human Tumor ◽  
Structural Similarity ◽  
Cell Tumor ◽  
Human Breast ◽  
Human Tumor Cell ◽  
Type Particle

We have reported the maturation of an intracisternal A-type particle in murine plasma cell tumor cultures and three human tumor cell cultures (rhabdomyosarcoma, lung adenocarcinoma, and osteogenic sarcoma) after IUDR-DMSO activation. In all of these studies the A-type particle seems to develop into a form with an electron dense nucleoid, presumably mature, which is also intracisternal. A similar intracisternal A-type particle has been described in leukemic guinea pigs. Although no biological activity has yet been demonstrated for these particles, on morphologic grounds, and by the manner in which they develop within the cell, they may represent members of the same family of viruses.

Further Ultrastructural Observations on Metastatic Nodules of Human Breast Cancer

1968 ◽  
Vol 26 ◽  
pp. 224-225
Author(s):  
John L. Swedo ◽  
R. W. Talley ◽  
John H. L. Watson
Keyword(s):  
Breast Cancer ◽  
Human Breast Cancer ◽  
Estrogen Therapy ◽  
Specimen Preparation ◽  
Human Breast ◽  
Autophagic Vacuoles ◽  
Previous Communication ◽  
Linear Profiles ◽  
Metastatic Nodule

Since the report, which described the ultrastructure of a metastatic nodule of human breast cancer after estrogen therapy, additional ultrastructural observations, including some which are correlative with pertinent findings in the literature concerning mycoplasmas, have been recorded concerning the same subject. Specimen preparation was identical to that in.The mitochondria possessed few cristae, and were deteriorated and vacuolated. They often contained particulates and fibrous structures, sometimes arranged in spindle-shaped bundles, Fig. 1. Another apparent aberration was the occurrence, Fig. 2 (arrows) of linear profiles of what seems to be SER, which lie between layers of RER, and are often recognizably continuous with them.It was noted that the structure of the round bodies, interpreted as within autophagic vacuoles in the previous communication, and of vesicular bodies, described morphologically closely resembled those of some mycoplasmas. Specifically, they simulated or reflected the various stages of replication reported for mycoplasmas grown on solid nutrient. Based on this observation, they are referred to here as “mycoplasma-like” structures, in anticipation of confirmatory evidence from investigations now in progress.

The proliferation of breast cancer cells are inhibited by the human intrabody targeting cyclinD1

2010 ◽  
Vol 34 (8) ◽  
pp. S49-S49
Author(s):  
Lei Wang ◽  
Xun Zhou ◽  
Lihong Zhou ◽  
Yong Chen ◽  
Xun Zhu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Breast Cancer Cells

Study of the mechanism responsible for multidrug resistance in breast cancer MCF-7 cell mediated by 14-3-3σ

2010 ◽  
Vol 34 (8) ◽  
pp. S47-S47
Author(s):  
Guopei Zheng ◽  
Sisi Yi ◽  
Yafei Li ◽  
Fangren Kong ◽  
Yanhui Yu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Multidrug Resistance ◽  
Mcf 7
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