With advancements in deep learning algorithms, such as CNNs, AI-aided automatic analysis of liver lesion images has emerged as a possibility. CNNs can automatically learn and extract intricate patterns and features from medical images, enabling automated liver lesion classification with exceptional accuracy.
Aug 15, 2024
Purpose: Our aim is to develop an automatic method which can detect diverse focal liver lesions (FLLs) in 3D CT volumes. Method: A hybrid ...
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Jan 29, 2021 · In this paper, we propose a framework based on hierarchical convolutional neural networks (CNNs) for automatic detection and classification of ...
Dec 15, 2021 · The computer-aided diagnosis of focal liver lesions (FLLs) can help improve workflow and enable correct diagnoses; FLL detection is the ...
Mar 31, 2013 · Purpose. Our aim is to develop an automatic method which can detect diverse focal liver lesions (FLLs) in 3D CT volumes.
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Jun 23, 2021 · The identified imaging markers and CAD system can be used as a non-invasive diagnostic tool for early and accurate detection and grading of liver cancer.
Oct 25, 2017 · Computer-aided diagnosis systems accurately classified 11 different focal liver lesion types as benign or malignant by analyzing 3-minute ...
Computer-aided diagnosis of liver lesions using CT images
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It takes abdominal CT images as input and processes them to detect/diagnose the liver lesion. In this review, the CAD systems are grouped into two categories: ...
An automatic method which can detect diverse focal liver lesions in 3D CT volumes and use a discriminative approach to suppress false positives with the ...
Purpose: Our aim is to develop an automatic method which can detect diverse focal liver lesions (FLLs) in 3D CT volumes.