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. Author manuscript; available in PMC: 2021 Jan 1.

Published in final edited form as: Oncology. 2018 Nov 23;98(6):344–362. doi: 10.1159/000493575

Fig. 6. — Lesion classification pipeline based on diagnostic images. Two types of features are extracted from a medical image: (a) CNN features with pretrained CNN and (b) handcrafted features with conventional CADx. High and low-level features extracted by pretrained CNN are evaluated in terms of their classification performance and preprocessing requirements. Furthermore, the classifier outputs from the pooled CNN features and the handcrafted features are fused in the evaluation of a combination of the two types of features. [permissions required!!]

Fig. 6. — Lesion classification pipeline based on diagnostic images. Two types of features are extracted from a medical image: (a) CNN features with pretrained CNN and (b) handcrafted features with conventional CADx. High and low-level features extracted by pretrained CNN are evaluated in terms of their classification performance and preprocessing requirements. Furthermore, the classifier outputs from the pooled CNN features and the handcrafted features are fused in the evaluation of a combination of the two types of features. [permissions required!!]