scholarly journals Deep Convolution Neural Networks Learned Image Classification for Early Cancer Detection Using Lightweight

2021 ◽  
Author(s):  
Seshadri Ramana K ◽  
Bala Chowdappa K ◽  
Obulesu ooruchintala ◽  
Deena Babu Mandru ◽  
kallam suresh
Keyword(s):  
Cancer Detection ◽  
Early Cancer ◽  
The Body ◽  
Early Cancer Detection ◽  
Detection Accuracy ◽  
Convolutional Network ◽  
Max Pooling ◽  
Input Layer ◽  
Deep Convolutional Network ◽  
Fully Connected

Abstract Cancer is uncontrolled cell growth in any part of the body. Early cancer detection aims to identify patients who exhibit symptoms early on in order to maximise their chances of a successful treatment. Cancer disease mortality is decreased through early detection and treatment. Numerous researchers proposed a variety of image processing and machine learning approaches for cancer detection. However, existing systems did not improve detection accuracy or efficiency. A Deep Convolutional Neural Learning Classifier Model based on the Least Mean Square Filterative Ricker Wavelet Transform (L-DCNLC) is proposed to address the aforementioned issues. The L-DCNLC Model's primary objective is to detect cancer earlier by utilising a fully connected max pooling deep convolutional network with increased accuracy and reduced time consumption. The fully connected max pooling deep convolutional network is composed of one input layer, three hidden layers, and one output layer. Initially, the input layer of the L-DCNLC Model considers the number of patient images in the database as input.

scholarly journals Small-area data on socioeconomic status and immigrant groups for evaluating equity of early cancer detection and care

2021 ◽  
pp. 1-6
Author(s):  
Ulf Strömberg ◽  
Brandon L. Parkes ◽  
Amir Baigi ◽  
Carl Bonander ◽  
Anders Holmén ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Cancer Detection ◽  
Small Area ◽  
Early Cancer ◽  
Early Cancer Detection ◽  
Area Data

Dying To Be Men: Masculinity and Early Cancer Detection Among Nigerian Men

2021 ◽  
pp. 0272684X2110049
Author(s):  
Darlingtina Esiaka ◽  
Candidus Nwakasi ◽  
Kelsey Brodie ◽  
Aaron Philip ◽  
Kalu Ogba
Keyword(s):  
Public Health ◽  
Cancer Detection ◽  
Attachment Styles ◽  
Early Cancer ◽  
Sub Saharan Africa ◽  
Early Cancer Detection ◽  
Anxious Attachment ◽  
Incidence And Mortality ◽  
Nigerian Men ◽  
Sub Saharan

Cancer incidence and mortality in Nigeria are increasing at an alarming rate, especially among Nigerian men. Despite the numerous public health campaigns and education on the importance of early cancer detection in Nigeria, there exist high rate of fatal/advanced stage cancer diagnoses among Nigerian men, even among affluent Nigerian men. However, there is limited information on patterns of cancer screening and psychosocial predictors of early cancer detection behaviors among Nigerian men. In this cross-sectional study, we examined demographic and psychosocial factors influencing early cancer detection behaviors among Nigerian men. Participants (N = 143; Mage = 44.73) responded to survey assessing: masculinity, attachment styles, current and future cancer detection behaviors, and sociodemographic characteristics. We found that among the participants studied, education, masculinity and anxious attachment were significantly associated with current cancer detection behaviors. Additionally, education and anxious attachment were significantly associated with future cancer detection behaviors. Our finding is best served for clinicians and public health professionals, especially those in the field of oncology in Sub-Saharan Africa. Also, the study may be used as a groundwork for future research and health intervention programs targeting men in Sub-Saharan Africa.

Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma

2021 ◽  
Author(s):  
Lin Huang ◽  
Kun Qian
Keyword(s):  
Machine Learning ◽  
Lung Adenocarcinoma ◽  
Cancer Detection ◽  
High Performance ◽  
Large Scale ◽  
Early Cancer ◽  
Early Stage ◽  
Early Cancer Detection ◽  
Ionization Mass ◽  
Efficient Test

Abstract Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address quick detection, low invasiveness, and high performance. Here, we conduct machine learning of serum metabolic patterns to detect early-stage LA. We extract direct metabolic patterns by the optimized ferric particle-assisted laser desorption/ionization mass spectrometry within 1 second using only 50 nL of serum. We define a metabolic range of 100-400 Da with 143 m/z features. We diagnose early-stage LA with sensitivity~70-90% and specificity~90-93% through the sparse regression machine learning of patterns. We identify a biomarker panel of seven metabolites and relevant pathways to distinguish early-stage LA from controls (p < 0.05). Our approach advances the design of metabolic analysis for early cancer detection and holds promise as an efficient test for low-cost rollout to clinics.

Knowledge of Testicular Cancer Risk and Need for Self-Examination in College Students: A Call for Equal Time for Men in Teaching of Early Cancer Detection Techniques

PEDIATRICS ◽  
1984 ◽  
Vol 74 (6) ◽  
pp. 1093-1096
Author(s):  
John M. Goldenring ◽  
Elizabeth Purtell
Keyword(s):  
Testicular Cancer ◽  
Cancer Detection ◽  
Early Cancer ◽  
Early Cancer Detection ◽  
Men And Women ◽  
Equal Time ◽  
Detection Techniques ◽  
The Past ◽  
Knowledge And Practice ◽  
Self Examination

College athletes were surveyed about their knowledge and practice of early cancer detection techniques. Males were almost completely unaware of their risk for testicular cancer (87%). Only 9.6% had been taught testicular self-examination and only half of these by their physician. Six percent actually examined themselves regularly. In comparison, more than 60% of women had been taught breast self-examination (75% by a physician) and about one third were doing regular examinations. More than 90% of the young men and women had been seen by physicians for a physical examination within the past 3 years. Physicians need to begin educating males about testicular cancer and its early detection.

scholarly journals Standards for Plasma and Serum Proteomics in Early Cancer Detection: A Needs Assessment Report from the National Institute of Standards and Technology-National Cancer Institute Standards, Methods, Assays, Reagents and Technologies Workshop, August 18–19, 2005

2006 ◽  
Vol 52 (9) ◽  
pp. 1669-1674 ◽  
Author(s):  
Peter E Barker ◽  
Paul D Wagner ◽  
Stephen E Stein ◽  
David M Bunk ◽  
Sudhir Srivastava ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Needs Assessment ◽  
Reference Materials ◽  
Cancer Detection ◽  
National Cancer Institute ◽  
Early Cancer ◽  
Early Cancer Detection ◽  
Assessment Report ◽  
Serum Proteomics

Abstract NIST and the National Cancer Institute cosponsored a workshop on August 18–19, 2005, to examine needs for reference materials for early cancer detection. This meeting focused on standards, methods, assays, reagents, and technologies. Needs for plasma and serum proteomics, DNA methylation, and specimen reference collections were discussed, and recommendations from participants were solicited. This report summarizes the discussion and recommendations for proteomics reference materials.

Early cancer detection

2017 ◽  
pp. 89-102
Author(s):  
Renelle Myers ◽  
Stephen Lam
Keyword(s):  
Cancer Detection ◽  
Early Cancer ◽  
Early Cancer Detection
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