Development of fuzzy approach to predict the fetus safety and growth using AFI

Amuthadevi, C.; Subarnan, Gayathri Monicka

doi:10.1007/s11227-019-03099-8

Cited by 8 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2019, Amuthadevi and Subarnan ( 19 ) concentrated on the analysis of amniotic fluid index (AFI), and the shape and contour features during different stages of pregnancy, and they proposed a fuzzy approach in order to assist in predicting abnormalities regarding the weight of the neonatal, head circumferences and probable need of intensive care after labor. The evaluation of these variables would guide decision-making regarding delivery, but also prevent premature delivery and increase the number of live births.…”

Section: Discussionmentioning

confidence: 99%

Fuzzy Logic Intelligent Systems and Methods in Midwifery and Obstetrics

2021

View full text Add to dashboard Cite

Background: Fuzzy logic can be used to model and manipulate imprecise and subjective knowledge imitating the human reasoning. Objective: The aim of this systematic review was to analyze research studies pertaining to fuzzy logic and fuzzy intelligent systems applications in midwifery and obstetrics. Methods: A thorough literature review was performed in four electronic databases (PubMed, APA PsycINFO, SCOPUS, ScienceDirect). Only the papers that discussed fuzzy logic and fuzzy intelligent systems applications in midwifery and obstetrics were considered in this review. Selected papers were critically evaluated as for their relevance and a contextual synthesis was conducted. Results: Twentynine papers were included in this systematic review as they met the inclusion and methodological criteria specified in this study. The results suggest that fuzzy logic and fuzzy intelligent systems have been successfully applied in midwifery and obstetrics topics, such as diagnosis, pregnancy risk assessment, fetal monitoring, bladder tumor, etc. Conclusion: This systematic review suggests that fuzzy logic is applicable to midwifery and obstetrics domains providing the means for developing affective intelligent systems that can assist human experts in dealing with complex diagnosis and problem solving. However, its full potential is not yet been examined, thus presenting an opportunity for further research.

show abstract

Section: Discussionmentioning

confidence: 99%

Fuzzy Logic Intelligent Systems and Methods in Midwifery and Obstetrics

2021

View full text Add to dashboard Cite

show abstract

“…The method of identifying brain tumour images from MRI scan images using CNN is expected to aid in more accurate diagnosis of brain tumours, potentially saving the lives of thousands of individuals. The work's future goals include developing a model that can partition or segment the tumour's region using deep learning models like U-Net,which can be trained with a small number of training examples, but it performs well in the task of image segmentation [17] which would aid surgeons in making informed surgical decisions and predicting patient survival rates.…”

Section: Discussionmentioning

confidence: 99%

Leveraging Classification of Brain Tumour using Deep Learning Architectures

Karunya¹,

G.R²,

Swathi³

2021

Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, In

View full text Add to dashboard Cite

Despite advances in human intellect and biomedical in the last few decades, people continue to suffer from various cancers due to their volatile nature. This disease is still a major issue for the entire humanity. Brain tumour is one of the most crucial and serious illnesses. Oftheentire primary central nervous system tumours, Brain tumorsmake up 85 to 90%. It isestimatedthat 18,600 adults, including 8,100 women and 10,500 men, will die of primary cancerous tumors of the brain and centralnervoussystem tumors this year. Among the children of various age groups also it is seen as one of the most crucial cancers. Thus, accurate and timely handling of this disease is decisive. In order to speed up the process of brain tumour detection (augmented with accuracy, reliability and experience)Deep learning models can be used.To efficiently diagnose brain tumour kinds and compare classification performance, the proposed work makes optimal use of a newly modelled Convolutional Neural Network and ResNet 50,a pre-trained network.

show abstract

“…Additionally, Amuthadevi and Subarnan [14] focused on measuring the AFI, as well as the geometry and shadowed properties of AF at various phases of gestation, and developed a fuzzy technique to help with forecasting anomalies in infant weight, head circumference (HC), and the requirement for critical care following delivery. The assessment of these factors would prove useful for delivery decisions, and ultimately aid in avoiding premature birth.…”

Section: Classificationmentioning

confidence: 99%

Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities

Khan

Aslam

Anis

et al. 2022

Sensors

View full text Add to dashboard Cite

A fetal ultrasound (US) is a technique to examine a baby’s maturity and development. US examinations have varying purposes throughout pregnancy. Consequently, in the second and third trimester, US tests are performed for the assessment of Amniotic Fluid Volume (AFV), a key indicator of fetal health. Disorders resulting from abnormal AFV levels, commonly referred to as oligohydramnios or polyhydramnios, may pose a serious threat to a mother’s or child’s health. This paper attempts to accumulate and compare the most recent advancements in Artificial Intelligence (AI)-based techniques for the diagnosis and classification of AFV levels. Additionally, we provide a thorough and highly inclusive breakdown of other relevant factors that may cause abnormal AFV levels, including, but not limited to, abnormalities in the placenta, kidneys, or central nervous system, as well as other contributors, such as preterm birth or twin-to-twin transfusion syndrome. Furthermore, we bring forth a concise overview of all the Machine Learning (ML) and Deep Learning (DL) techniques, along with the datasets supplied by various researchers. This study also provides a brief rundown of the challenges and opportunities encountered in this field, along with prospective research directions and promising angles to further explore.

show abstract

Development of fuzzy approach to predict the fetus safety and growth using AFI

Cited by 8 publications

References 11 publications

Fuzzy Logic Intelligent Systems and Methods in Midwifery and Obstetrics

Fuzzy Logic Intelligent Systems and Methods in Midwifery and Obstetrics

Leveraging Classification of Brain Tumour using Deep Learning Architectures

Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities

Contact Info

Product

Resources

About