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.

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 Label Rectification Learning through Kernel Extreme Learning Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Qiang Cai ◽  
Fenghai Li ◽  
Yifan Chen ◽  
Haisheng Li ◽  
Jian Cao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Extreme Learning Machine ◽  
Classification Performance ◽  
Considerable Progress ◽  
Strong Representation ◽  
Kernel Extreme Learning Machine ◽  
Classification Tasks ◽  
Learning Machine

Along with the strong representation of the convolutional neural network (CNN), image classification tasks have achieved considerable progress. However, majority of works focus on designing complicated and redundant architectures for extracting informative features to improve classification performance. In this study, we concentrate on rectifying the incomplete outputs of CNN. To be concrete, we propose an innovative image classification method based on Label Rectification Learning (LRL) through kernel extreme learning machine (KELM). It mainly consists of two steps: (1) preclassification, extracting incomplete labels through a pretrained CNN, and (2) label rectification, rectifying the generated incomplete labels by the KELM to obtain the rectified labels. Experiments conducted on publicly available datasets demonstrate the effectiveness of our method. Notably, our method is extensible which can be easily integrated with off-the-shelf networks for improving performance.

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.

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 An Optimal Feature Selection with Wavelet Kernel Extreme Learning Machine for Big Data Analysis of Product Reviews

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
R. Sathya , Et. al.
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Extreme Learning Machine ◽  
Selection Process ◽  
Classification Performance ◽  
Product Reviews ◽  
Social Spider ◽  
Social Spider Optimization ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

In recent times, generation of big data takes place in an exponential way from diverse textual data sources like review sites, media, blogs, etc. Sentiment analysis (SA) finds it useful to classify the opinions of the big data to different kinds ofsentiments. Therefore, SA on big data helps a business to take beneficial commercial understandings from text based content. Though several SA approaches have been presented, yet, there is a need to improve the performance of SA to interpret the customer’s feedback and increase the product quality.This paper introduces a novel social spider optimization based feature selection based wavelet kernel extreme learning machine (SSO-WKELM) model. The proposed model initially undergoes pre-processing to remove the unwanted word removal. Then, Term Frequency-Inverse Document Frequency (TF-IDF) is utilized as a feature extraction technique to extract the set of feature vectors. Besides, a social spider optimization (SSO) algorithm is utilized for feature selection process and thereby achieves improved classification performance. Subsequently, WKELM is employed as a classifier to classify the incidence of positive or negative user reviews. For experimental validation, a Product review dataset derived from Amazon along with synthetic data is used. The experimental results stated the superior classification performance of the SSO-WKELM model.   

scholarly journals Skin Lesion Classification Via Combining Deep Learning Features and Clinical Criteria Representations

2018 ◽  
Author(s):  
Xiaoxiao Li ◽  
Junyan Wu ◽  
Hongda Jiang ◽  
Eric Z. Chen ◽  
Xu Dong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Skin Lesion ◽  
Severe Disease ◽  
Texture Features ◽  
Skin Lesions ◽  
Model Parameters ◽  
Deep Convolutional Neural Networks ◽  
Global Features ◽  
Clinical Criteria ◽  
Deep Learning Features

AbstractSkin lesion is a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of skin lesion is extremely challenging manually visualization. Hence, reliable automatic classification of skin lesions is meaningful to improve pathologists’ accuracy and efficiency. In this paper, we proposed a two-stage method to combine deep learning features and clinical criteria representations to address skin lesion automated diagnosis task. Stage 1 - feature encoding: Modified deep convolutional neural networks (CNNs, in this paper, we used Dense201 and Res50) were fine-tuned to extract rich image global features. To avoid hair noisy, we developed a lesion segmentation U-Net to mask out the decisive regions and used the masked image as CNNs inputs. In addition, color features, texture features and morphological features were exacted based on clinical criteria; Stage 2 - features fusion: LightGBM was used to select the salient features and model parameters, predicting diagnosis confidence for each category. The proposed deep learning frameworks were evaluated on the ISIC 2018 dataset. Experimental results show the promising accuracies of our frameworks were achieved.

scholarly journals Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Mi Li ◽  
Lei Cao ◽  
Qian Zhai ◽  
Peng Li ◽  
Sa Liu ◽  
...  
Keyword(s):  
Eye Movement ◽  
Principal Component ◽  
Classification Performance ◽  
Physiological Signals ◽  
Movement Behavior ◽  
Emotional Stimuli ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
The One ◽  
Better Than

This paper presents a method of depression recognition based on direct measurement of affective disorder. Firstly, visual emotional stimuli are used to obtain eye movement behavior signals and physiological signals directly related to mood. Then, in order to eliminate noise and redundant information and obtain better classification features, statistical methods (FDR corrected t-test) and principal component analysis (PCA) are used to select features of eye movement behavior and physiological signals. Finally, based on feature extraction, we use kernel extreme learning machine (KELM) to recognize depression based on PCA features. The results show that, on the one hand, the classification performance based on the fusion features of eye movement behavior and physiological signals is better than using a single behavior feature and a single physiological feature; on the other hand, compared with previous methods, the proposed method for depression recognition achieves better classification results. This study is of great value for the establishment of an automatic depression diagnosis system for clinical use.

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 An Efficient multi-level Pre-processing Algorithm for the Enhancement of Dermoscopy images in Melanoma detection

2021 ◽  
Author(s):  
JEBA DERWIN D ◽  
JEBA SINGH O ◽  
PRIESTLY SHAN B
Keyword(s):  
Skin Lesion ◽  
Contrast Enhancement ◽  
Extreme Learning Machine ◽  
Positive Impact ◽  
Lesion Detection ◽  
Phase Congruency ◽  
Lesion Segmentation ◽  
Multi Level ◽  
Learning Machine ◽  
Structural Preservation

Abstract In this paper, a multi-level algorithm for Pre-processing of dermoscopy images is proposed, which helps in improving the quality of the raw images, making it suitable for skin lesion detection. This multi-level pre-processing method has a positive impact on automated skin lesion segmentation using Regularized Extreme Learning Machine. Raw images are subjected to de-noising, illumination correction, contrast enhancement, sharpening, reflection removal and virtual shaving before the skin lesion segmentation. The NLM filter with lowest BRISQUE score exhibits better de-noising of dermoscopy images. To suppress uneven illumination, gamma correction is subjected to the de-noised image. RICE algorithm is used for contrast enhancement, produces enhanced images with better structural preservation and negligible loss of information. Unsharp Masking for sharpening exhibits low BRISQUE scores for better sharpening of fine details in an image. Output images produced by the phase-congruency based method in virtual shaving shows high similarity with groundtruth images as the hair is removed completely from the input images. Obtained scores at each stage of pre-processing framework shows that, the performance is superior compared to all the existing methods, both qualitatively and quantitatively, in terms of uniform contrast, preservation of information content, removal of undesired information and elimination of artifacts in melanoma images. Output of proposed system is assessed qualitatively and quantitatively with and without pre-processing of dermoscopy images. From the overall evaluation results it is found that, the segmentation of skin lesion is more efficient using Regularized Extreme Learning Machine if the multi-level pre-processing steps are used in proper sequence.

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