Abstract

Myocardial infarction (MI) is usually diagnosed by various methods, such as cardiac imaging modalities, by observing the structure and movement of the left ventricular (LV) wall. However, left ventricular ultrasound images often suffer from noise and poor image quality. This challenge can adapt segmentation methods on medical images to help health professionals observe the LV wall more clearly. This research segmented the LV wall using a deep learning architecture that adaptively adjusts the width and depth of the network based on the input resolution, called Adaptive EfficientNet-DeepLabV3+. The modification involves replacing the DeepLabV3+encoder with EfficientNet and incorporating a scaling function to adjust the depth and width of the network based on a given resolution, using scaling coefficients in a scaling function that increases or decreases these parameters equally. To evaluate its performance, it compares the architecture's adaptability with a standard DeepLabV3+model, using publicly available datasets provided by Hamad Medical Corporation, Qatar University, and Tampere University (HMC-QU) with variations in input resolution. The test results show that Adaptive EfficientNet-Deep-LabV3+ can adaptively adjust the depth and width of the network, as indicated by changes in the number of model parameters based on the input resolution while maintaining high accuracy, reaching 0.99225, and achieving an intersection over onion (IOU) of 0.80908 on test data.

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

  • EfficienNet-Deeplab V3+
  • left ventricular wall
  • segmentation
  • ultrasound image

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