Description
Title: NEURAL NETWORKS-BASED APPROACH TO CLASSIFYING DATA WITH HIGHLY LOCALIZED UNMARKED FEATURES
Abstract: Modern automation technology is being used in difficult fields like medical imaging to meet the growing demand for high-quality healthcare. In this paper, a novel method for solving classification issues on datasets with sparse highly localized features is proposed. It is based on the amplification of features using a saliency map. This method does not make use of any prior knowledge about feature localization, in contrast to earlier efforts. We present an experimental study based on the Diabetic Retinopathy Classification Problem. The results show that our method outperforms a naive approach based solely on residual neural networks in solving a two-class Diabetic Retinopathy Classification Problem by over 20%. There are 35;120 images in the dataset, all with different qualities, inconsistent resolutions, and odd aspect ratios.
Keywords: classi cation, neural networks, medical image analysis
Paper Quality: SCOPUS / Web of Science Level Research Paper
Paper type: Analysis Based Research Paper
Subject: Computer Science
Writer Experience: 20+ Years
Plagiarism Report: Turnitin Plagiarism Report will be less than 10%
Restriction: Only one author may purchase a single paper. The paper will then indicate that it is out of stock.
What will I get after the purchase?
A turnitin plagiarism report of less than 10% in a pdf file and a full research paper in a word document.
In case you have any questions related to this research paper, please feel free to call/ WhatsApp on +919726999915
Reviews
There are no reviews yet.