Abstract

The Left Ventricular Wall (LVW) is frequently becoming a region of interest in ultrasound examinations. Its abnormalities often serve as early indicators of various cardiovascular diseases. However, the poor-quality ultrasound images make it essential to develop automated segmentation methods so doctors can observe LVW's structure and movement more clearly. This study proposes a deep learning approach to solve this challenge. The main improvement is the construction of Dense-UNet architecture coupled with network-based transfer learning, called Dense-UNet-121, Dense-UNet-169, and Dense-UNet-201 with transfer learning. They are hybrid deep learning architectures in which the structure and parameter values are partially transferred from models trained on different tasks and datasets. To validate their performances, they will be compared with the previous state-of-the-art model, i.e., UNet and Dense-UNet without transfer learning using publicly available datasets from Hamad Medical Corporation, Qatar University, and Tampere University (HMC-QU). The results demonstrate the consistent superiority over all previous state-of-the-art model. Additionally, the usage of transfer learning also provides higher accuracy, smoother learning curves, and a training duration that is 34.7-36.3% shorter. Dense-UNet-201 with transfer learning attains the highest accuracy values of 0.9951, 0.9708, and 0.9698, respectively for training, validation, and testing data, confirming the excellent result of this approach.

Original languageEnglish
Title of host publicationICITDA 2023 - Proceedings of the 2023 8th International Conference on Information Technology and Digital Applications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350344691
DOIs
Publication statusPublished - 2023
Event8th International Conference on Information Technology and Digital Applications, ICITDA 2023 - Yogyakarta, Indonesia
Duration: 17 Nov 202318 Nov 2023

Publication series

NameICITDA 2023 - Proceedings of the 2023 8th International Conference on Information Technology and Digital Applications

Conference

Conference8th International Conference on Information Technology and Digital Applications, ICITDA 2023
Country/TerritoryIndonesia
CityYogyakarta
Period17/11/2318/11/23

Keywords

  • Dense-UNet
  • left ventricular wall
  • network-based transfer learning
  • segmentation

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