Deep learning and computer-aided detection (CAD) methods play a pivotal role in the early detection and diagnosis of various cancer types. The significance of AI in the medical field has become particularly pronounced during the coronavirus pandemic. This study aims to develop a deep learning-based system for segmenting and detecting nodules in the lung parenchyma, utilizing the Luna-16 challenge dataset. The algorithm is divided into two phases: the first phase involves lung segmentation using the previously developed LungQuant algorithm to identify the region of interest (ROI), and the second phase employs a specifically designed and fine-tuned Attention Res-UNet for nodule segmentation. Additionally, the explainable AI (XAI) technique, Grad-CAM, was used to demonstrate the reliability of the proposed algorithm for clinical application. In the initial phase, the LungQuant algorithm achieved an average Dice Similarity Coefficient (DSC) of 90%. For nodule segmentation, the DSC scores were 81% test sets. The model also achieved average sensitivity and specificity metrics of 0.86 and 0.92.
Explainability Applied to a Deep-Learning Based Algorithm for Lung Nodule Segmentation
Zafaranchi, Arman
Primo
;Lizzi, Francesca;Scapicchio, CamillaPenultimo
;Fantacci, MariaUltimo
2024-01-01
Abstract
Deep learning and computer-aided detection (CAD) methods play a pivotal role in the early detection and diagnosis of various cancer types. The significance of AI in the medical field has become particularly pronounced during the coronavirus pandemic. This study aims to develop a deep learning-based system for segmenting and detecting nodules in the lung parenchyma, utilizing the Luna-16 challenge dataset. The algorithm is divided into two phases: the first phase involves lung segmentation using the previously developed LungQuant algorithm to identify the region of interest (ROI), and the second phase employs a specifically designed and fine-tuned Attention Res-UNet for nodule segmentation. Additionally, the explainable AI (XAI) technique, Grad-CAM, was used to demonstrate the reliability of the proposed algorithm for clinical application. In the initial phase, the LungQuant algorithm achieved an average Dice Similarity Coefficient (DSC) of 90%. For nodule segmentation, the DSC scores were 81% test sets. The model also achieved average sensitivity and specificity metrics of 0.86 and 0.92.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.