scholarly journals Quantitative ABCD parameters measured by a multispectral digital skin lesion analysis device for evaluation of suspicious pigmented skin lesions strongly correlate with clinical ABCD observations

2017 ◽  
Vol 23 (8) ◽  
Author(s):  
Alex M Glazer ◽  
Aaron S Farberg ◽  
Richard R Winkelmann ◽  
Natalie Tucker ◽  
Darrell S Rigel
Keyword(s):  
Skin Lesion ◽  
Skin Lesions ◽  
Lesion Analysis ◽  
Pigmented Skin Lesions

scholarly journals A Mobile-Based Prototype for Skin Lesion Analysis: Towards a Patient-Oriented Design Approach

2013 ◽  
Vol 9 (S8) ◽  
pp. 27 ◽  
Author(s):  
Luís Rosado ◽  
Maria João Vasconcelos ◽  
Márcia Ferreira
Keyword(s):  
Skin Lesion ◽  
Active Role ◽  
Skin Lesions ◽  
Self Monitoring ◽  
Promising Tool ◽  
Processing Power ◽  
Skin Cancers ◽  
Lesion Analysis ◽  
New Generation

The wide spreading of the new generation of smartphones, with significant improvements in terms of image acquisition and processing power, is opening up the possibility of new approaches for skin lesion monitoring. Mobile Teledermatology appears nowadays as a promising tool with the potential to empower patients to adopt an active role in managing their own skin health status, while facilitates the early diagnosis of skin cancers. The main objective of this work is to create a mobile-based prototype for skin lesions analysis with patient-oriented features and functionalities. The presented self-monitoring system collects, processes and storages information of skin lesions through the automatic extraction and classification of specific visual features. The algorithms used to extract and classify these features are briefly described, as well as the overall system architecture and functionalities.

Detection and Classification of Skin Lesions using Probability Map-based Region Growing with BA-KNN Classifier (Preprint)

2021 ◽  
Author(s):  
Sivaraj S ◽  
Dr.R. Malmathanraj
Keyword(s):  
Skin Lesion ◽  
Region Growing ◽  
Skin Lesions ◽  
Classification Approach ◽  
Probability Map ◽  
Knn Classifier ◽  
Knn Classification ◽  
Pigmented Skin Lesions ◽  
Thresholding Algorithm

BACKGROUND Melanoma is one of the most hazardous existing diseases, and is a kind of threatening pigmented skin lesion. Appropriate automated diagnosis of skin lesions and the categorization of melanoma may be exceptionally enhancing premature identification of melanomas. OBJECTIVE However, Models of categorization based on deterministic skin lesion may influence multi-dimensional nonlinear problem provokes inaccurate and ineffective categorization. This research presents a novel hybrid BA-KNN classification approach for pigmented skin lesions in dermoscopy images. METHODS In the first step, the skin lesion is preprocessed via automatic preprocessing algorithm together with a fusion hair detection and removal strategy. Also, a new probability map based region growing and optimal thresholding algorithm is integrated in this system to enhance the rate of accuracy. RESULTS Moreover, to attain better efficacy, an estimate of ABCD as well as geometric features are considered during the feature extraction to describe the malignancy of the lesion. CONCLUSIONS The evaluation of the experiment reveals the efficiency of the proposed approach on dermoscopy images with better accuracy

scholarly journals The Study of Usefulness of a Set of Fractal Parameters to Build Classes of Disease Units Based on Images of Pigmented Skin Lesions

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1773
Author(s):  
Monika Styła ◽  
Tomasz Giżewski
Keyword(s):  
Skin Lesion ◽  
Fractal Analysis ◽  
Skin Lesions ◽  
Diagnostic Systems ◽  
Kohonen Network ◽  
Network Learning ◽  
Fractal Parameters ◽  
Fully Automatic ◽  
Pigmented Skin Lesions

Dermatoscopic images are also increasingly used to train artificial neural networks for the future to provide fully automatic diagnostic systems capable of determining the type of pigmented skin lesion. Therefore, fractal analysis was used in this study to measure the irregularity of pigmented skin lesion surfaces. This paper presents selected results from individual stages of preliminary processing of the dermatoscopic image on pigmented skin lesion, in which fractal analysis was used and referred to the effectiveness of classification by fuzzy or statistical methods. Classification of the first unsupervised stage was performed using the method of analysis of scatter graphs and the fuzzy method using the Kohonen network. The results of the Kohonen network learning process with an input vector consisting of eight elements prove that neuronal activation requires a larger learning set with greater differentiation. For the same training conditions, the final results are at a higher level and can be classified as weaker. Statistics of factor analysis were proposed, allowing for the reduction in variables, and the directions of further studies were indicated.

scholarly journals Robustness of convolutional neural networks in recognition of pigmented skin lesions

2021 ◽  
Vol 145 ◽  
pp. 81-91
Author(s):  
Roman C. Maron ◽  
Sarah Haggenmüller ◽  
Christof von Kalle ◽  
Jochen S. Utikal ◽  
Friedegund Meier ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Skin Lesions ◽  
Pigmented Skin Lesions

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.

Symmetrical acral keratoderma: A waxing and waning scaly pigmented skin lesions on the acral extremities

2020 ◽  
Author(s):  
Toshifumi Nomura ◽  
Masae Takeda ◽  
Jin Teng Peh ◽  
Akihiro Orita ◽  
Emi Inamura ◽  
...  
Keyword(s):  
Skin Lesions ◽  
Pigmented Skin Lesions

scholarly journals ASCU-Net: Attention Gate, Spatial and Channel Attention U-Net for Skin Lesion Segmentation

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 501
Author(s):  
Xiaozhong Tong ◽  
Junyu Wei ◽  
Bei Sun ◽  
Shaojing Su ◽  
Zhen Zuo ◽  
...  
Keyword(s):  
Skin Lesion ◽  
Contextual Information ◽  
Skin Lesions ◽  
Medical Image Segmentation ◽  
Learning Networks ◽  
Wide Range ◽  
Accurate Performance ◽  
Relevant Field ◽  
Skin Smooth ◽  
Smooth Transitions

Segmentation of skin lesions is a challenging task because of the wide range of skin lesion shapes, sizes, colors, and texture types. In the past few years, deep learning networks such as U-Net have been successfully applied to medical image segmentation and exhibited faster and more accurate performance. In this paper, we propose an extended version of U-Net for the segmentation of skin lesions using the concept of the triple attention mechanism. We first selected regions using attention coefficients computed by the attention gate and contextual information. Second, a dual attention decoding module consisting of spatial attention and channel attention was used to capture the spatial correlation between features and improve segmentation performance. The combination of the three attentional mechanisms helped the network to focus on a more relevant field of view of the target. The proposed model was evaluated using three datasets, ISIC-2016, ISIC-2017, and PH2. The experimental results demonstrated the effectiveness of our method with strong robustness to the presence of irregular borders, lesion and skin smooth transitions, noise, and artifacts.

scholarly journals From colour to tissue histology: Physics-based interpretation of images of pigmented skin lesions

2003 ◽  
Vol 7 (4) ◽  
pp. 489-502 ◽  
Author(s):  
Ela Claridge ◽  
Symon Cotton ◽  
Per Hall ◽  
Marc Moncrieff
Keyword(s):  
Skin Lesions ◽  
Pigmented Skin Lesions ◽  
Tissue Histology

scholarly journals Prevalence and risk of appearence of skin lesions considered to be cutaneous markers of diabetes in a population group in bihor county

2012 ◽  
Vol 19 (3) ◽  
pp. 285-290
Author(s):  
Denisa Kovacs ◽  
Luiza Demian ◽  
Aurel Babeş
Keyword(s):  
Type 2 Diabetes ◽  
Type 1 Diabetes ◽  
Skin Lesion ◽  
Skin Diseases ◽  
Population Group ◽  
Skin Lesions ◽  
Diabetic Patients ◽  
Cutaneous Lesions

Abstract Objectives: The aim of the study was to calculate the prevalence rates and risk ofappearance of cutaneous lesions in diabetic patients with both type-1 and type-2diabetes. Material and Method: 384 patients were analysed, of which 47 had type-1diabetes (T1DM), 140 had type-2 diabetes (T2DM) and 197 were non-diabeticcontrols. Results: The prevalence of the skin lesions considered markers of diabeteswas 57.75% in diabetics, in comparison to 8.12% in non-diabetics (p<0.01). The riskof skin lesion appearance is over 7 times higher in diabetic patients than in nondiabetics.In type-1 diabetes the prevalence of skin lesions was significantly higherthan in type-2 diabetes, and the risk of skin lesion appearance is almost 1.5 timeshigher in type-1 diabetes than type-2 diabetes compared to non-diabetic controls.Conclusions: The diabetic patients are more susceptible than non-diabetics todevelop specific skin diseases. Patients with type-1 diabetes are more affected.

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