Skin lesion classification using ensembles of multi-resolution EfficientNets with meta data

Gessert, Nils; Nielsen, Maximilian; Shaikh, Mohsin; Werner, René; Schlaefer, Alexander

doi:10.1016/j.mex.2020.100864

Cited by 242 publications

(178 citation statements)

References 17 publications

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“…The following ensembles architectures stand out in terms of their performance after systematic evaluation: 1) EfficientNet-B3+Inception-V3, 2) EfficientNet-B3+ResNet-50, 3) ResNext101+ResNet-50, 4) EfficientNet-B3+ResNext-101+Xception, 5) EfficientNet-B3+ResNext-101. Similar ensemble networks have shown state-of-theart performance for related domain classification tasks [40], [41], [42].…”

Section: Ensemble Methodsmentioning

confidence: 99%

Ensemble Convolutional Neural Networks With Knowledge Transfer for Leather Defect Classification in Industrial Settings

et al. 2020

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Leather defect analysis is important for leather quality grading which directly effects the leather exports. Automated leather sample classification is vital due to slow and subjective nature of the manual process. The major challenges that exist in visual inspection of leather samples for categorization are: the morphology of defects significantly differs and their close examples are not available for transfer learning, unavailability of publicly available data and a benchmark. In this paper, we discuss three important aspects in the identification of industrial leather defects, i.e. the creation of an annotated wet-blue leather image dataset, the transfer of information from different domains to the leather image domain and the design of ensemble networks tailored to the task. We are therefore introducing a new database of wet-blue leather images (Wet-blue Leather Image Dataset (WBLID)) for the classification of defects along with expert annotation data. We proposed a new network EfficientNet-B3+ResNext-101. The proposed EfficientNet-B3+ResNext-101 ensemble architecture significantly outperforms all other state-of-the-art methods in terms of AUC and F1-score.

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Section: Ensemble Methodsmentioning

confidence: 99%

Ensemble Convolutional Neural Networks With Knowledge Transfer for Leather Defect Classification in Industrial Settings

et al. 2020

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“…Compared with [45], VWB-1 is close in most evaluation criteria, but VWB-1 has obvious improvement in AUC and SP. On the whole, [46] performs better than VWB-1. However, VWB-1 is 0.002 higher in SP than [46].…”

Section: E Comparison Of Classification Effects Of Different Frameworkmentioning

confidence: 96%

“…On the whole, [46] performs better than VWB-1. However, VWB-1 is 0.002 higher in SP than [46]. In addition, the calculation cost of [46] is relatively high and the realization process is complex.…”

Section: E Comparison Of Classification Effects Of Different Frameworkmentioning

confidence: 96%

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Classification for Dermoscopy Images Using Convolutional Neural Networks Based on the Ensemble of Individual Advantage and Group Decision

2020

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Skin cancer is a common and deadly cancer. Dermoscopy is an effective tool for the observation of abnormal skin pigmentation. However, dermoscopy images are extremely complex and present great challenges for diagnosis. Therefore, we proposed a classification method based on the ensemble of individual advantage and group decision in dermoscopy images, including the ensemble strategy of group decision, the ensemble strategy of maximizing individual advantage, and the ensemble strategy of block-integrated voting. We used generative adversarial networks (GANs) to create a balanced sample space to better train convolutional neural networks (CNNs). Through transfer learning, the pretraining CNNs were used for fine-tuning, then the effects of different CNNs on the classification of different categories of dermoscopy images were compared, and the CNNs with better classification effect were selected for the ensemble of different strategies. This study is based on the ISIC 2018 dataset and ISIC 2019 dataset. Compared with the different individual CNNs and the frameworks, the proposed ensemble strategies achieve a better improvement in the evaluation criteria. INDEX TERMS Dermoscopy images, Ensemble strategies, Convolutional neural networks, Transfer learning.

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“…They performed two binary classifications with keratinocytecarcinomas versus benign seborrheic-keratosis, and malignant melanomas versus benign nevi. Gessert et al [61] utilized a multi-resolution ensemble of CNNs comprising of EfficientNets, SENet, and ResNeXt WSL for the detection of skin lesions. They achieved satisfactory performance on a much smaller dataset of HAM 10000 and ISIC 2018.…”

Section: ) Supervised Learningmentioning

confidence: 99%

FCN-Based DenseNet Framework for Automated Detection and Classification of Skin Lesions in Dermoscopy Images

2020

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Skin Lesion detection and classification are very critical in diagnosing skin malignancy. Existing Deep learning-based Computer-aided diagnosis (CAD) methods still perform poorly on challenging skin lesions with complex features such as fuzzy boundaries, artifacts presence, low contrast with the background and, limited training datasets. They also rely heavily on a suitable turning of millions of parameters which often leads to over-fitting, poor generalization, and heavy consumption of computing resources. This study proposes a new framework that performs both segmentation and classification of skin lesions for automated detection of skin cancer. The proposed framework consists of two stages: the first stage leverages on an encoder-decoder Fully Convolutional Network (FCN) to learn the complex and inhomogeneous skin lesion features with the encoder stage learning the coarse appearance and the decoder learning the lesion borders details. Our FCN is designed with the sub-networks connected through a series of skip pathways that incorporate long skip and shortcut connections unlike, the only long skip connections commonly used in the traditional FCN, for residual learning strategy and effective training. The network also integrates the Conditional Random Field (CRF) module which employs a linear combination of Gaussian kernels for its pairwise edge potentials for contour refinement and lesion boundaries localization. The second stage proposes a novel FCN-based DenseNet framework that is composed of dense blocks that are merged and connected via the concatenation strategy and transition layer. The system also employs hyperparameters optimization techniques to reduce network complexity and improve computing efficiency. This approach encourages feature reuse and thus requires a small number of parameters and effective with limited data. The proposed model was evaluated on publicly available HAM10000 dataset of over 10000 images consisting of 7 different categories of diseases with 98% accuracy, 98.5% recall, and 99% of AUC score respectively.

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Skin lesion classification using ensembles of multi-resolution EfficientNets with meta data

Cited by 242 publications

References 17 publications

Ensemble Convolutional Neural Networks With Knowledge Transfer for Leather Defect Classification in Industrial Settings

Ensemble Convolutional Neural Networks With Knowledge Transfer for Leather Defect Classification in Industrial Settings

Classification for Dermoscopy Images Using Convolutional Neural Networks Based on the Ensemble of Individual Advantage and Group Decision

FCN-Based DenseNet Framework for Automated Detection and Classification of Skin Lesions in Dermoscopy Images

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