Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network

Zafar, Kashan; Gilani, Syed Omer; Waris, Asim; Ahmed, Ali; Jamil, Mohsin; Khan, Muhammad Nasir; Kashif, Amer Sohail

doi:10.3390/s20061601

Cited by 107 publications

(45 citation statements)

References 39 publications

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“…The proposed model gives accurate segmentation results and requires a small dataset because our model is not sensitive to parameter tuning. Our experimental results revealed that integrating [44] 0.802 0.881 Proposed method 0:826 ± 0:008 0:912 ± 0:07 0:833 ± 0:09 0:914 ± 0:05 17 BioMed Research International hierarchical K-means clustering and DRLSE had high clinical applicability even in the presence of various artifacts and small datasets. The proposed model may facilitate the combination of machine learning and level set models in skin lesion images.…”

Section: Discussionmentioning

confidence: 87%

A Segmentation of Melanocytic Skin Lesions in Dermoscopic and Standard Images Using a Hybrid Two-Stage Approach

Hwang

Seo

Kim

2021

BioMed Research International

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The segmentation of a skin lesion is regarded as very challenging because of the low contrast between the lesion and the surrounding skin, the existence of various artifacts, and different imaging acquisition conditions. The purpose of this study is to segment melanocytic skin lesions in dermoscopic and standard images by using a hybrid model combining a new hierarchical K -means and level set approach, called HK-LS. Although the level set method is usually sensitive to initial estimation, it is widely used in biomedical image segmentation because it can segment more complex images and does not require a large number of manually labelled images. The preprocessing step is used for the proposed model to be less sensitive to intensity inhomogeneity. The proposed method was evaluated on medical skin images from two publicly available datasets including the PH2 database and the Dermofit database. All skin lesions were segmented with high accuracies (>94%) and Dice coefficients (>0.91) of the ground truth on two databases. The quantitative experimental results reveal that the proposed method yielded significantly better results compared to other traditional level set models and has a certain advantage over the segmentation results of U-net in standard images. The proposed method had high clinical applicability for the segmentation of melanocytic skin lesions in dermoscopic and standard images.

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Section: Discussionmentioning

confidence: 87%

A Segmentation of Melanocytic Skin Lesions in Dermoscopic and Standard Images Using a Hybrid Two-Stage Approach

Hwang

Seo

Kim

2021

BioMed Research International

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“…Feng et al (2020) proposed Context Pyramid Fusion Network (named CPFNet) to solve context information extraction capability of a single stage that is insufficient in the deep learning, due to the problems such as imbalanced class and blurred boundary. Zafar et al (2020) adapted an automated technique for segmenting lesion boundaries that combines two architectures, the U-net and the ResNet, collectively called Res-Unet. It also used image inpainting for hair removal to improve the segmentation results significantly.…”

Section: Related Workmentioning

confidence: 99%

Skin Lesion Segmentation Using Local Binary Convolution-Deconvolution Architecture

Salih

Viriri

2020

Image Anal Stereol

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Deep learning techniques such as Deep Convolutional Networks have achieved great success in skin lesion segmentation towards melanoma detection. The performance is however restrained by distinctive and challenging features of skin lesions such as irregular and fuzzy border, noise and artefacts presence and low contrast between lesions. The methods are also restricted with scarcity of annotated lesion images training dataset and limited computing resources. Recent research in convolutional neural network (CNN) has provided a variety of new architectures for deep learning. One interesting new architecture is the local binary convolutional neural network (LBCNN), which can reduce the workload of CNNs and improve the classification accuracy. The proposed framework employs the local binary convolution on U-net architecture instead of the standard convolution in order to reduced-size deep convolutional encoder-decoder network that adopts loss function for robust segmentation. The proposed framework replaced the encoder part in U-net by LBCNN layers. The approach automatically learns and segments complex features of skin lesion images. The encoder stage learns the contextual information by extracting discriminative features while the decoder stage captures the lesion boundaries of the skin images. This addresses the issues with encoder-decoder network producing coarse segmented output with challenging skin lesions appearances such as low contrast between healthy and unhealthy tissues and fine grained variability. It also addresses issues with multi-size, multi-scale and multi-resolution skin lesion images. The deep convolutional network also adopts a reduced-size network with just five levels of encoding-decoding network. This reduces greatly the consumption of computational processing resources. The system was evaluated on publicly available dataset of ISIC and PH2. The proposed system outperforms most of the existing state-of-art.

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“…Lyu et al [10] classified different types of lung nodule malignancies through a multilevel cross-residual convolutional neural network (CNN). These studies [4][5][6][7][8][9][10] were conducted by applying a basic augmentation method or GAN for lesions such as lung, skin, and brain lesions but not gastric lesions. Asperti et al [11] increased the amount of data by randomly applying rotation, width shift, height shift, shear, and zoom methods within a certain range to classify gastroscopy images as normal or abnormal.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Computer-Aided Diagnosis System for Gastroscopy Image Classification Using Synthetic Data

Kim

Cho

2021

Applied Sciences

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Gastric cancer has a high mortality rate worldwide, but it can be prevented with early detection through regular gastroscopy. Herein, we propose a deep learning-based computer-aided diagnosis (CADx) system applying data augmentation to help doctors classify gastroscopy images as normal or abnormal. To improve the performance of deep learning, a large amount of training data are required. However, the collection of medical data, owing to their nature, is highly expensive and time consuming. Therefore, data were generated through deep convolutional generative adversarial networks (DCGAN), and 25 augmentation policies optimized for the CIFAR-10 dataset were implemented through AutoAugment to augment the data. Accordingly, a gastroscopy image was augmented, only high-quality images were selected through an image quality-measurement method, and gastroscopy images were classified as normal or abnormal through the Xception network. We compared the performances of the original training dataset, which did not improve, the dataset generated through the DCGAN, the dataset augmented through the augmentation policies of CIFAR-10, and the dataset combining the two methods. The dataset combining the two methods delivered the best performance in terms of accuracy (0.851) and achieved an improvement of 0.06 over the original training dataset. We confirmed that augmenting data through the DCGAN and CIFAR-10 augmentation policies is most suitable for the classification model for normal and abnormal gastric endoscopy images. The proposed method not only solves the medical-data problem but also improves the accuracy of gastric disease diagnosis.

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Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network

Cited by 107 publications

References 39 publications

A Segmentation of Melanocytic Skin Lesions in Dermoscopic and Standard Images Using a Hybrid Two-Stage Approach

A Segmentation of Melanocytic Skin Lesions in Dermoscopic and Standard Images Using a Hybrid Two-Stage Approach

Skin Lesion Segmentation Using Local Binary Convolution-Deconvolution Architecture

Deep Learning-Based Computer-Aided Diagnosis System for Gastroscopy Image Classification Using Synthetic Data

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