STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Microwave Engineering and Technologies Review Article

Comparative Analysis of Data Augmentation Techniques in CNN-based Classification of Atelectasis

  1. Vaishali Suryawanshi Department of Computer Engineering, Thadomal Shahani Engineering College, Bandra West, Mumbai
  2. Nidhi Kadam Department of Computer Engineering, Thadomal Shahani Engineering College, Bandra West, Mumbai
  3. Tanuja Sarode Department of Computer Engineering, Thadomal Shahani Engineering College, Bandra West, Mumbai

Abstract

This research delves into the critical issue of atelectasis, its causes, and potential complications if left untreated. Leveraging deep learning algorithms, particularly convolutional neural networks (CNN), the paper explores their application in medical image analysis, focusing on the detection of atelectasis using the “chestX-ray8” database. The study compares various data augmentation techniques for improved accuracy, showcasing the importance of augmentation in enhancing model generalization. Through meticulous experimentation and evaluation, the research underscores the significance of balanced augmentation strategies in refining CNN performance for medical image analysis, offering valuable insights for future research and practice in radiology applications. The several data augmentation methods used in CNN-based Atelectasis classification are compared in this review study. Geometric modifications, intensity-based augmentations, and sophisticated techniques like generative adversarial networks (GANs) are all covered in this paper. We examine each technique’s advantages and disadvantages, how they affect model performance, and whether or not they can be used in clinical situations through a methodical examination. The purpose of this article is to help practitioners and researchers choose the best augmentation techniques to increase the accuracy and robustness of CNN models for the categorization of atelectasis.

Keywords

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