Abstract
This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features.
Highlights
Computer-aided diagnosis (CADx) is a computerized procedure to provide a second objective opinion for the assistance of medical image interpretation and diagnosis[1,2,3,4,5,6,7,8,9,10]
We compare the performances of the stacked denoising autoencoder (SDAE)-based CADx and the two compared algorithms with the six assessment metrics: 1) area under receiver operating characteristic curve (AUC), 2) accuracy (ACC), 3) sensitivity (SENS), 4) specificity (SPEC), 5) positive predictive value (PPV), and 6) negative predictive value (NPV)
The experimental results show that the SDAE algorithm outperforms the conventional CADx algorithms on both applications
Summary
Computer-aided diagnosis (CADx) is a computerized procedure to provide a second objective opinion for the assistance of medical image interpretation and diagnosis[1,2,3,4,5,6,7,8,9,10]. The conventional design of CADx is often composed of three main steps: feature extraction[4,6,7,12,13,14,15,18], feature selection[18,19,20,21], and classification These three steps need to be well-addressed separately and integrated together for the overall CADx performance tuning. The engineering of effective features is problem-oriented and still needs assistance from the latter steps of feature selection and feature integration by classifier, to achieve accurate lesion/ nodule differentiation. We further exploit the deep learning model of the stacked denoising autoencoder (SDAE)[41] for the differentiation of distinctive types of lesions and nodules depicted with different imaging modalities
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