mlsanityData to 14 Oct 2026

Fused Deep Features Based Classification Framework for COVID-19 Classification with Optimized MLP

arXiv 2103.09904 ↗First posted 15 Mar 2021cs.LG eess.IVcited by 0

Not on the map. Why?

Abstract (arXiv)

The new type of Coronavirus disease called COVID-19 continues to spread quite rapidly. Although it shows some specific symptoms, this disease, which can show different symptoms in almost every individual, has caused hundreds of thousands of patients to die. Although healthcare professionals work hard to prevent further loss of life, the rate of disease spread is very high. For this reason, the help of computer aided diagnosis (CAD) and artificial intelligence (AI) algorithms is vital. In this study, a method based on optimization of convolutional neural network (CNN) architecture, which is the most effective image analysis method of today, is proposed to fulfill the mentioned COVID-19 detection needs. First, COVID-19 images are trained using ResNet-50 and VGG-16 architectures. Then, features in the last layer of these two architectures are combined with feature fusion. These new image features matrices obtained with feature fusion are classified for COVID detection. A multi-layer perceptron (MLP) structure optimized by the whale optimization algorithm is used for the classification process. The obtained results show that the performance of the proposed framework is almost 4.5% higher than VGG-16 performance and almost 3.5% higher than ResNet-50 performance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 26 references that are in the map. First posted on arXiv 15 Mar 2021.

Builds on

9 of 26 references.

References · 9

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2020Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Imagescited by 184
  4. 2019Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attackscited by 79
  5. 2020COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Imagescited by 60
  6. 2020COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Imagescited by 34
  7. 2020COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarioscited by 19
  8. 2020Coronavirus (COVID-19) Classification using CT Images by Machine Learning Methodscited by 13
  9. 2020Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CTcited by 13

Built on by

Not cited yet.