An image-based segmentation recommender using crowdsourcing and transfer learning for skin lesion extraction

Soudani, Amira; Barhoumi, Walid

doi:10.1016/j.eswa.2018.10.029

Cited by 70 publications

(29 citation statements)

References 13 publications

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“…Soudani and Barhoumi [25] used two deep learning classification models to recommend the most appropriate segmentation technique on ISIC-2017 dataset. Recently, Al-masni et al [26] proposed a fully resolution convolutional network (FrCN) to learn full resolution features of each pixel of dermoscopic skin lesion images for skin segmentation.…”

Section: Deep Learning For Skin Lesion Segmentationmentioning

confidence: 99%

Skin Lesion Segmentation in Dermoscopic Images With Ensemble Deep Learning Methods

et al. 2020

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Early detection of skin cancer, particularly melanoma, is crucial to enable advanced treatment. Due to the rapid growth in the number of skin cancers, there is a growing need of computerised analysis for skin lesions. The state-of-the-art public available datasets for skin lesions are often accompanied with a very limited amount of segmentation ground truth labeling. Also, the available segmentation datasets consist of noisy expert annotations reflecting the fact that precise annotations to represent the boundary of skin lesions are laborious and expensive. The lesion boundary segmentation is vital to locate the lesion accurately in dermoscopic images and lesion diagnosis of different skin lesion types. In this work, we propose the fully automated deep learning ensemble methods to achieve high sensitivity and high specificity in lesion boundary segmentation. We trained the ensemble methods based on Mask R-CNN and DeeplabV3+ methods on ISIC-2017 segmentation training set and evaluate the performance of the ensemble networks on ISIC-2017 testing set and PH2 dataset. Our results showed that the proposed ensemble methods segmented the skin lesions with Sensitivity of 89.93% and Specificity of 97.94% for the ISIC-2017 testing set. The proposed ensemble method Ensemble-A outperformed FrCN, FCNs, U-Net, and SegNet in Sensitivity by 4.4%, 8.8%, 22.7%, and 9.8% respectively. Furthermore, the proposed ensemble method EnsembleS achieved a specificity score of 97.98% for clinically benign cases, 97.30% for the melanoma cases, and 98.58% for the seborrhoeic keratosis cases on ISIC-2017 testing set, exhibiting better performance than FrCN, FCNs, U-Net, and SegNet.

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Section: Deep Learning For Skin Lesion Segmentationmentioning

confidence: 99%

Skin Lesion Segmentation in Dermoscopic Images With Ensemble Deep Learning Methods

et al. 2020

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“…TL has been widely applied in text classification and clustering, emotional classification, image classification and collaborative filtering. Soudani and Barhoumi [26] extracted features from the convolutional part to improve the segmentation performance using two pre-trained architectures (VGG16 and ResNet50).…”

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confidence: 99%

Learning Models for Semantic Classification of Insufficient Plantar Pressure Images

Wu¹,

Wu²,

Dey³

et al. 2020

IJIMAI

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Establishing a reliable and stable model to predict a target by using insufficient labeled samples is feasible and effective, particularly, for a sensor-generated data-set. This paper has been inspired with insufficient data-set learning algorithms, such as metric-based, prototype networks and meta-learning, and therefore we propose an insufficient data-set transfer model learning method. Firstly, two basic models for transfer learning are introduced. A classification system and calculation criteria are then subsequently introduced. Secondly, a dataset of plantar pressure for comfort shoe design is acquired and preprocessed through foot scan system; and by using a pre-trained convolution neural network employing AlexNet and convolution neural network (CNN)based transfer modeling, the classification accuracy of the plantar pressure images is over 93.5%. Finally, the proposed method has been compared to the current classifiers VGG, ResNet, AlexNet and pre-trained CNN. Also, our work is compared with known-scaling and shifting (SS) and unknown-plain slot (PS) partition methods on the public test databases: SUN, CUB, AWA1, AWA2, and aPY with indices of precision (tr, ts, H) and time (training and evaluation). The proposed method for the plantar pressure classification task shows high performance in most indices when comparing with other methods. The transfer learning-based method can be applied to other insufficient data-sets of sensor imaging fields.

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“…The system achieved a 78% classification accuracy on skin lesion images containing melanoma cancer. A segmentation recommender based on transfer learning and crowdsourcing algorithm was proposed by Soudani et al [55]. The system utilized two pre-trained CNN models based on VGG16 and ResNet50 for features extraction and classification of skin lesion images.…”

Section: ) Transfer 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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An image-based segmentation recommender using crowdsourcing and transfer learning for skin lesion extraction

Cited by 70 publications

References 13 publications

Skin Lesion Segmentation in Dermoscopic Images With Ensemble Deep Learning Methods

Skin Lesion Segmentation in Dermoscopic Images With Ensemble Deep Learning Methods

Learning Models for Semantic Classification of Insufficient Plantar Pressure Images

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

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