scholarly journals FAC-Net: Feedback Attention Network Based on Context Encoder Network for Skin Lesion Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5172
Author(s):  
Yuying Dong ◽  
Liejun Wang ◽  
Shuli Cheng ◽  
Yongming Li
Keyword(s):  
Skin Lesion ◽  
Real Life ◽  
Good Deal ◽  
Experimental Tests ◽  
Skin Lesions ◽  
Lesion Segmentation ◽  
Attention Network ◽  
Effective Feedback ◽  
Public Datasets ◽  
Learning Architectures

Considerable research and surveys indicate that skin lesions are an early symptom of skin cancer. Segmentation of skin lesions is still a hot research topic. Dermatological datasets in skin lesion segmentation tasks generated a large number of parameters when data augmented, limiting the application of smart assisted medicine in real life. Hence, this paper proposes an effective feedback attention network (FAC-Net). The network is equipped with the feedback fusion block (FFB) and the attention mechanism block (AMB), through the combination of these two modules, we can obtain richer and more specific feature mapping without data enhancement. Numerous experimental tests were given by us on public datasets (ISIC2018, ISBI2017, ISBI2016), and a good deal of metrics like the Jaccard index (JA) and Dice coefficient (DC) were used to evaluate the results of segmentation. On the ISIC2018 dataset, we obtained results for DC equal to 91.19% and JA equal to 83.99%, compared with the based network. The results of these two main metrics were improved by more than 1%. In addition, the metrics were also improved in the other two datasets. It can be demonstrated through experiments that without any enhancements of the datasets, our lightweight model can achieve better segmentation performance than most deep learning architectures.

scholarly journals SELECTED APPLICATIONS OF DEEP NEURAL NETWORKS IN SKIN LESION DIAGNOSTIC

2021 ◽  
Vol 11 (4) ◽  
pp. 18-21
Author(s):  
Magdalena Michalska
Keyword(s):  
Neural Networks ◽  
Malignant Melanoma ◽  
Skin Lesion ◽  
Deep Neural Networks ◽  
Skin Lesions ◽  
Multiclass Classification ◽  
Features Selection ◽  
Lesion Segmentation ◽  
Dermatological Diseases ◽  
Segmentation Process

The article provides an overview of selected applications of deep neural networks in the diagnosis of skin lesions from human dermatoscopic images, including many dermatological diseases, including very dangerous malignant melanoma. The lesion segmentation process, features selection and classification was described. Application examples of binary and multiclass classification are given. The described algorithms have been widely used in the diagnosis of skin lesions. The effectiveness, specificity, and accuracy of classifiers were compared and analysed based on available datasets.

scholarly journals Skin Lesion Segmentation Using Stochastic Region-Merging and Pixel-Based Markov Random Field

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1224
Author(s):  
Omran Salih ◽  
Serestina Viriri
Keyword(s):  
Skin Lesion ◽  
Random Field ◽  
Markov Random Field ◽  
Likelihood Function ◽  
Skin Lesions ◽  
Great Success ◽  
Region Merging ◽  
Lesion Segmentation ◽  
Low Contrast ◽  
Markov Random

Markov random field (MRF) theory has achieved great success in image segmentation. Researchers have developed various methods based on MRF theory to solve skin lesions segmentation problems such as pixel-based MRF model, stochastic region-merging approach, symmetric MRF model, etc. In this paper, the proposed method seeks to provide a complement to the advantages of the pixel-based MRF model and stochastic region-merging approach. This is in order to overcome shortcomings of the pixel-based MRF model, because of various challenges that affect the skin lesion segmentation results such as irregular and fuzzy border, noisy and artifacts presence, and low contrast between lesions. The strength of the proposed method lies in the aspect of combining the benefits of the pixel-based MRF model and the stochastic region-merging by decomposing the likelihood function into the multiplication of stochastic region-merging likelihood function and the pixel likelihood function. The proposed method was evaluated on bench marked available datasets, PH2 and ISIC. The proposed method achieves Dice coefficients of 89.65 % on PH2 and 88.34 % on ISIC datasets respectively.

scholarly journals Residual refinement for interactive skin lesion segmentation

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Dalei Jiang ◽  
Yin Wang ◽  
Feng Zhou ◽  
Hongtao Ma ◽  
Wenting Zhang ◽  
...  
Keyword(s):  
Skin Lesion ◽  
User Interaction ◽  
Universal Method ◽  
Lesion Segmentation ◽  
Feature Maps ◽  
Classic Problem ◽  
Novel Approach ◽  
Wide Range ◽  
Public Datasets ◽  
Segmentation Task

Abstract Background Image segmentation is a difficult and classic problem. It has a wide range of applications, one of which is skin lesion segmentation. Numerous researchers have made great efforts to tackle the problem, yet there is still no universal method in various application domains. Results We propose a novel approach that combines a deep convolutional neural network with a grabcut-like user interaction to tackle the interactive skin lesion segmentation problem. Slightly deviating from grabcut user interaction, our method uses boxes and clicks. In addition, contrary to existing interactive segmentation algorithms that combine the initial segmentation task with the following refinement task, we explicitly separate these tasks by designing individual sub-networks. One network is SBox-Net, and the other is Click-Net. SBox-Net is a full-fledged segmentation network that is built upon a pre-trained, state-of-the-art segmentation model, while Click-Net is a simple yet powerful network that combines feature maps extracted from SBox-Net and user clicks to residually refine the mistakes made by SBox-Net. Extensive experiments on two public datasets, PH2 and ISIC, confirm the effectiveness of our approach. Conclusions We present an interactive two-stage pipeline method for skin lesion segmentation, which was demonstrated to be effective in comprehensive experiments.

Efficient Unet with depth-aware gated fusion for automatic skin lesion segmentation

2021 ◽  
pp. 1-13
Author(s):  
Xiangwen Ding ◽  
Shengsheng Wang
Keyword(s):  
Skin Lesion ◽  
Skin Lesions ◽  
Background Information ◽  
Lesion Segmentation ◽  
Feature Maps ◽  
Irregular Shapes ◽  
Large Size ◽  
Application Potential ◽  
Different Depths ◽  
Serious Disease

Melanoma is a very serious disease. The segmentation of skin lesions is a critical step for diagnosing melanoma. However, skin lesions possess the characteristics of large size variations, irregular shapes, blurring borders, and complex background information, thus making the segmentation of skin lesions remain a challenging problem. Though deep learning models usually achieve good segmentation performance for skin lesion segmentation, they have a large number of parameters and FLOPs, which limits their application scenarios. These models also do not make good use of low-level feature maps, which are essential for predicting detailed information. The Proposed EUnet-DGF uses MBconv to implement its lightweight encoder and maintains a strong encoding ability. Moreover, the depth-aware gated fusion block designed by us can fuse feature maps of different depths and help predict pixels on small patterns. The experiments conducted on the ISIC 2017 dataset and PH2 dataset show the superiority of our model. In particular, EUnet-DGF only accounts for 19% and 6.8% of the original Unet in terms of the number of parameters and FLOPs. It possesses a great application potential in practical computer-aided diagnosis systems.

scholarly journals Skin Lesion Segmentation Using Local Binary Convolution-Deconvolution Architecture

2020 ◽  
Vol 39 (3) ◽  
pp. 169-185
Author(s):  
Omran Salih ◽  
Serestina Viriri
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Skin Lesions ◽  
Training Dataset ◽  
Great Success ◽  
Lesion Segmentation ◽  
Convolutional Network ◽  
Low Contrast

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.

scholarly journals Skin Lesion Segmentation and Multiclass Classification Using Deep Learning Features and Improved Moth Flame Optimization

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 811
Author(s):  
Muhammad Attique Khan ◽  
Muhammad Sharif ◽  
Tallha Akram ◽  
Robertas Damaševičius ◽  
Rytis Maskeliūnas
Keyword(s):  
Skin Lesion ◽  
Skin Lesions ◽  
Classification Performance ◽  
Lesion Segmentation ◽  
Local Color ◽  
Heat Map ◽  
Kernel Extreme Learning Machine ◽  
Deep Learning Features ◽  
Learning Machine ◽  
Thresholding Function

Manual diagnosis of skin cancer is time-consuming and expensive; therefore, it is essential to develop automated diagnostics methods with the ability to classify multiclass skin lesions with greater accuracy. We propose a fully automated approach for multiclass skin lesion segmentation and classification by using the most discriminant deep features. First, the input images are initially enhanced using local color-controlled histogram intensity values (LCcHIV). Next, saliency is estimated using a novel Deep Saliency Segmentation method, which uses a custom convolutional neural network (CNN) of ten layers. The generated heat map is converted into a binary image using a thresholding function. Next, the segmented color lesion images are used for feature extraction by a deep pre-trained CNN model. To avoid the curse of dimensionality, we implement an improved moth flame optimization (IMFO) algorithm to select the most discriminant features. The resultant features are fused using a multiset maximum correlation analysis (MMCA) and classified using the Kernel Extreme Learning Machine (KELM) classifier. The segmentation performance of the proposed methodology is analyzed on ISBI 2016, ISBI 2017, ISIC 2018, and PH2 datasets, achieving an accuracy of 95.38%, 95.79%, 92.69%, and 98.70%, respectively. The classification performance is evaluated on the HAM10000 dataset and achieved an accuracy of 90.67%. To prove the effectiveness of the proposed methods, we present a comparison with the state-of-the-art techniques.

Serial attention network for skin lesion segmentation

2021 ◽  
Author(s):  
Yuan Ren ◽  
Long Yu ◽  
Shengwei Tian ◽  
Junlong Cheng ◽  
Zhiqi Guo ◽  
...  
Keyword(s):  
Skin Lesion ◽  
Lesion Segmentation ◽  
Attention Network

scholarly journals Attention-Guided Network with Densely Connected Convolution for Skin Lesion Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3462
Author(s):  
Shengxin Tao ◽  
Yun Jiang ◽  
Simin Cao ◽  
Chao Wu ◽  
Zeqi Ma
Keyword(s):  
Survival Rate ◽  
Skin Lesion ◽  
Automatic Segmentation ◽  
Skin Lesions ◽  
Diagnosis And Treatment ◽  
Guided Filter ◽  
Lesion Segmentation ◽  
Data Set ◽  
Low Contrast

The automatic segmentation of skin lesions is considered to be a key step in the diagnosis and treatment of skin lesions, which is essential to improve the survival rate of patients. However, due to the low contrast, the texture and boundary are difficult to distinguish, which makes the accurate segmentation of skin lesions challenging. To cope with these challenges, this paper proposes an attention-guided network with densely connected convolution for skin lesion segmentation, called CSAG and DCCNet. In the last step of the encoding path, the model uses densely connected convolution to replace the ordinary convolutional layer. A novel attention-oriented filter module called Channel Spatial Fast Attention-guided Filter (CSFAG for short) was designed and embedded in the skip connection of the CSAG and DCCNet. On the ISIC-2017 data set, a large number of ablation experiments have verified the superiority and robustness of the CSFAG module and Densely Connected Convolution. The segmentation performance of CSAG and DCCNet is compared with other latest algorithms, and very competitive results have been achieved in all indicators. The robustness and cross-data set performance of our method was tested on another publicly available data set PH2, further verifying the effectiveness of the model.

scholarly journals Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm

Diagnostics ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 72 ◽  
Author(s):  
Halil Murat Ünver ◽  
Enes Ayan
Keyword(s):  
Skin Lesion ◽  
Automatic Segmentation ◽  
Critical Role ◽  
Skin Lesions ◽  
Lesion Segmentation ◽  
Low Contrast ◽  
Melanoma Detection ◽  
Sensitivity Rate ◽  
Dermoscopic Image ◽  
Grabcut Algorithm

Skin lesion segmentation has a critical role in the early and accurate diagnosis of skin cancer by computerized systems. However, automatic segmentation of skin lesions in dermoscopic images is a challenging task owing to difficulties including artifacts (hairs, gel bubbles, ruler markers), indistinct boundaries, low contrast and varying sizes and shapes of the lesion images. This paper proposes a novel and effective pipeline for skin lesion segmentation in dermoscopic images combining a deep convolutional neural network named as You Only Look Once (YOLO) and the GrabCut algorithm. This method performs lesion segmentation using a dermoscopic image in four steps: 1. Removal of hairs on the lesion, 2. Detection of the lesion location, 3. Segmentation of the lesion area from the background, 4. Post-processing with morphological operators. The method was evaluated on two publicly well-known datasets, that is the PH2 and the ISBI 2017 (Skin Lesion Analysis Towards Melanoma Detection Challenge Dataset). The proposed pipeline model has achieved a 90% sensitivity rate on the ISBI 2017 dataset, outperforming other deep learning-based methods. The method also obtained close results according to the results obtained from other methods in the literature in terms of metrics of accuracy, specificity, Dice coefficient, and Jaccard index.

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