Convolutional descriptors aggregation via cross-net for skin lesion recognition

Yu, Zhen; Jiang, Feng; Zhou, Feng; He, Xinzi; Ni, Dong; Chen, Siping; Wang, Tianfu; Lei, Baiying

doi:10.1016/j.asoc.2020.106281

Cited by 46 publications

(28 citation statements)

References 55 publications

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“…Zhang et al Designed a multi-CNN collaborative training dermoscopy image lesion recognition model, improves the robustness of lesion identification and verified the effectiveness of the proposed method on related data sets [38]. In order for the model to learn more powerful and more distinguishing feature representation capabilities, Zheng et al Proposed a framework for automatic skin lesion recognition using crossnet based aggregation of multiple convolutional networks, and verified the proposed method through extensive experiments Superiority [39].…”

Section: ⅱ Related Workmentioning

confidence: 92%

“…We have made a wide comparison with the advanced melanoma recognition methods, these methods include: based on feature fusion [31], segmentation first and then recognition [34], combining adaptive sample learning strategy with multi-CNN [36], combining deep residual network with Fisher coding [37], multi-CNN collaborative training model [38], combining Fisher Vector and multi-CNN fusion [39]. As seen in Table VII, our method was superior the rank one method [34] in the ISBI 2016 classification task, and the methods [39] 6.3% and 1.3% respectively in AP index score, After the fusion (weighted average) of the prediction results of two different classification networks, the put forward approach exceeds the state-of-the-art method [39] 3.7% in AP index. It is should be noted that some methods contain more intermediate steps or higher calculation amounts.…”

Section: ) Performance Comparison With Other Methodsmentioning

confidence: 99%

“…For instance, using multiple CNN and network fine-tuning [36]. The method [39] include three steps: image processing, CNN training, fisher vector coding and SVM training. which cannot achieve end-to-end model training.…”

Section: ) Performance Comparison With Other Methodsmentioning

confidence: 99%

“…which cannot achieve end-to-end model training. Our framework has very few parameters, the final fusion model size is only with 42M (The model size of method [37], [39] are 97.6M, 179M, respectively). Furthermore, because our model also can be trained end-to-end and effectively, which can be easily used in the analysis of other medical tasks.…”

Section: ) Performance Comparison With Other Methodsmentioning

confidence: 99%

“…At present, some methods based on CNN have made positive progress in the task of automatic detection of melanoma [33][34][35][36][37][38][39][40][41]. Meanwhile, some researchers have focused on the automatic detection task of melanoma on lightweight deep learning models [50][51][52], which significantly reduced the number of model parameters and achieved better detection results.…”

Section: A Basis Of Lightweight Convolutional Neural Networkmentioning

confidence: 99%

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Automatic Skin Cancer Detection in Dermoscopy Images Based on Ensemble Lightweight Deep Learning Network

2020

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The complex detection background and lesion features make the automatic detection of dermoscopy image lesions face many challenges. The previous solutions mainly focus on using larger and more complex models to improve the accuracy of detection, there is a lack of research on significant intraclass differences and inter-class similarity of lesion features. At the same time, the larger model size also brings challenges to further algorithm application; In this paper, we proposed a lightweight skin cancer recognition model with feature discrimination based on fine-grained classification principle. The propose model includes two common feature extraction modules of lesion classification network and a feature discrimination network. Firstly, two sets of training samples (positive and negative sample pairs) are input into the feature extraction module (Lightweight CNN) of the recognition model. Then, two sets of feature vectors output from the feature extraction module are used to train the two classification networks and feature discrimination networks of the recognition model at the same time, and the model fusion strategy is applied to further improve the performance of the model, the proposed recognition method can extract more discriminative lesion features and improve the recognition performance of the model in a small amount of model parameters; In addition, based on the feature extraction module of the proposed recognition model, U-Net architecture, and migration training strategy, we build a lightweight semantic segmentation model of lesion area of dermoscopy image, which can achieve high precision lesion area segmentation end-to-end without complicated image preprocessing operation; The performance of our approach was appraised through widespread experiments comparative and feature visualization analysis, the outcome indicates that the proposed method has better performance than the start-of-the-art deep learning-based approach on the ISBI 2016 skin lesion analysis towards melanoma detection challenge dataset. INDEX TERMS Dermoscopy Images, Skin cancer detection, Lightweight deep learning network, Finegrained feature.

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Section: ⅱ Related Workmentioning

confidence: 92%