Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2024.2314918
PulmonU-Net: a semantic lung disease segmentation model leveraging the benefit of multiscale feature concatenation and leaky ReLU
H. Mary Shyni
; Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
E. Chitra
; Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
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* Dopisni autor.
Sažetak
Pulmonary diseases impact lung functionality and can cause health complications. X-ray imaging is an initial diagnostic approach for evaluating lung conditions. Manual segmentation of lung
infections from X-rays is time-consuming and subjective. Automated segmentation has gained
interest to reduce clinician workload. Semantic segmentation involves labelling individual pixels
in X-rays to highlight infected regions. This article presents PulmonU-Net, an innovative semantic
segmentation model using PulmonNet modules as the base network to highlight infected areas
in chest X-rays. PulmonNet modules leverage global and local chest X-ray characteristics to create
intricate feature maps. Incorporating leaky ReLU activation enables uninterrupted neuron functioning during learning. By adding PulmonNet modules in the encoder’s deeper layers, the model
addresses vanishing gradients and improves dice similarity coefficient to 94.25%. Real-time testing and prediction visualization demonstrate PulmonU-Net’s effectiveness for automated lung
infection segmentation from chest X-rays.
Ključne riječi
Deep learning; lung diseases; feature concatenation; semantic segmentation; U-Net; chest X-rays
Hrčak ID:
323053
URI
Datum izdavanja:
13.2.2024.
Posjeta: 0 *