Modelling a self-defined CNN for effectual classification of PCOS from ultrasound images

The proposed model establishes better trade-off compared to various other approaches and works effectually for PCOS prediction.

Authors

Institutions

Publication Details

Journal
Technology and Health Care
Published
2024-08-20
DOI
https://doi.org/10.3233/thc-230935
Citations
5
Primary Topic
Ultrasound and Hyperthermia Applications
Type
article
Field-Weighted Citation Impact
0.92
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Modelling a self-defined CNN for effectual classification of PCOS from ultrasound images

R. R. Rajalaxmi, Gokila Brindha Paramasivam
5 citations
Technology and Health Care
Ultrasound and Hyperthermia Applications
0.92
article

Modelling a self-defined CNN for effectual classification of PCOS from ultrasound images

R. R. Rajalaxmi, Gokila Brindha Paramasivam
article en
5 citations

Abstract

The proposed model establishes better trade-off compared to various other approaches and works effectually for PCOS prediction.

Technology and Health CareVol. 32(5)
Hong Kong College of Technology (CN)
Good health and well-being
Openalex Percentile: Top 28%
Ultrasound and Hyperthermia Applications
0.92
DOISourceOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Modelling a self-defined CNN for effectual classification of PCOS from ultrasound images — R. R. Rajalaxmi, Gokila Brindha Paramasivam · Technology and Health Care (2024) | TGRS Research Map | TGRS