ArticleComputational and mathematical methods in medicine2021
An Approach for Thoracic Syndrome Classification with Convolutional Neural Networks.
Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Deep Learning Model for the Automatic Classification of White Blood Cells.Computational intelligence and neuroscience · 2022Article
- An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction.Frontiers in public health · 2022Article
- Outcome Prediction of Hematologic Malignancy in Critically Sick People.BioMed research international · 2022Article
- Segmentation and Classification of Encephalon Tumor by Applying Improved Fast and Robust FCM Algorithm with PSO-Based ELM Technique.Computational intelligence and neuroscience · 2022Article
- A Perspective Roadmap for IoMT-Based Early Detection and Care of the Neural Disorder, Dementia.Journal of healthcare engineering · 2021Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There have been remarkable changes in our lives and the way we perceive the world with advances in computing technology. Healthcare sector is evolving with the intervention of the latest computer-driven technology and has made a remarkable change in the diagnosis and treatment of various diseases. Due to many governing factors including air pollution, there is a rapid rise in chest-related diseases and the number of such patients is rising at an alarming rate. In this research work, we have employed machine learning approach for the detecting various chest-related problems using convolutional neural networks (CNN) on an open dataset of chest X-rays. The method has an edge over the traditional approaches for image segmentation including thresholding,
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.