Performance matrices on the LIDC-IDRI dataset.

<div><p>Accurate segmentation of lung lesions in CT-scan images is essential to diagnose lung cancer. The challenges in lung nodule diagnosis arise due to their small size and diverse nature. We designed a transformer-based model EDTNet (Encoder Decoder Transformer Network) for PNS (Pulm...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Dhirendra Prasad Yadav (20235097) (author)
مؤلفون آخرون:	Bhisham Sharma (20235100) (author), Julian L. Webber (20235103) (author), Abolfazl Mehbodniya (11988747) (author), Shivank Chauhan (20235106) (author)
منشور في:	2024
الموضوعات:	Biotechnology Ecology Cancer Mental Health Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified sorensen &# 8211 resunet ++, u improve segmentation capabilities facilitate symmetrical interaction dice coefficient ). demonstrates superior quantitative 84 %, 96 81 %, 96 27 %, 95 range spatial dependencies diagnose lung cancer spatial aware attention enhanced spatial attention edtnet achieved 96 based vision transformer based model edtnet spatial features provide attention lung lesions based transformer based encoders edtnet performance visual results union ), traditional cnn suboptimal performance small size skip connections several models scan images sampling layers intricate details expanding layer diverse nature demonstrated sensitivity capture long
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Performance matrices on the LIDC-IDRI dataset.

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