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 Simple and Accurate Border Detection Algorithm for Melanoma Computer Aided Diagnosis

Diagnostics ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 423
Author(s):  
Cataldo Guaragnella ◽  
Maria Rizzi
Keyword(s):  
Skin Lesion ◽  
Early Stage ◽  
Relative Survival ◽  
Skin Lesions ◽  
Detection Algorithm ◽  
Human Vision ◽  
Smooth Transition ◽  
Border Detection ◽  
Wide Range ◽  
Computer Aided

The interest of the scientific community for computer aided skin lesion analysis and characterization has been increased during the last years for the growing incidence of melanoma among cancerous pathologies. The detection of melanoma in its early stage is essential for prognosis improvement and for guaranteeing a high five-year relative survival rate of patients. The clinical diagnosis of skin lesions is challenging and not trivial since it depends on human vision and physician experience and expertise. Therefore, a computer method that makes an accurate extraction of important details of skin lesion image can assist dermatologists in cancer detection. In particular, the border detection is a critical computer vision issue owing to the wide range of lesion shapes, sizes, colours and skin texture types. In this paper, an automatic and effective pigmented skin lesion segmentation method in dermoscopic image is presented. The proposed procedure is adopted to extract a mask of the lesion region without the adoption of other signal processing procedures for image improvement. A quantitative experimental evaluation has been performed on a publicly available database. The achieved results show the method validity and its high robustness towards irregular boundaries, smooth transition between lesion and skin, noise and artifact presence.

scholarly journals A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 348
Author(s):  
Choongsang Cho ◽  
Young Han Lee ◽  
Jongyoul Park ◽  
Sangkeun Lee
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Feature Maps ◽  
Data Set ◽  
Feature Map ◽  
Adaptive Weighting ◽  
Spatially Adaptive ◽  
Wide Range ◽  
Decoder Architecture

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.

scholarly journals A Generalization Performance Study Using Deep Learning Networks in Embedded Systems

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1031
Author(s):  
Joseba Gorospe ◽  
Rubén Mulero ◽  
Olatz Arbelaitz ◽  
Javier Muguerza ◽  
Miguel Ángel Antón
Keyword(s):  
Deep Learning ◽  
Embedded Systems ◽  
Embedded System ◽  
General Purpose ◽  
Learning Networks ◽  
Performance Study ◽  
Learning Techniques ◽  
Wide Range ◽  
Learning Architectures

Deep learning techniques are being increasingly used in the scientific community as a consequence of the high computational capacity of current systems and the increase in the amount of data available as a result of the digitalisation of society in general and the industrial world in particular. In addition, the immersion of the field of edge computing, which focuses on integrating artificial intelligence as close as possible to the client, makes it possible to implement systems that act in real time without the need to transfer all of the data to centralised servers. The combination of these two concepts can lead to systems with the capacity to make correct decisions and act based on them immediately and in situ. Despite this, the low capacity of embedded systems greatly hinders this integration, so the possibility of being able to integrate them into a wide range of micro-controllers can be a great advantage. This paper contributes with the generation of an environment based on Mbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowing the introduction of deep learning architectures. The experiments herein prove that the proposed system is competitive if compared to other commercial systems.

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 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.

AI-Enabled Support System for Melanoma Detection and Classification

2021 ◽  
Vol 10 (4) ◽  
pp. 58-75
Author(s):  
Vivek Sen Saxena ◽  
Prashant Johri ◽  
Avneesh Kumar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Skin Lesion ◽  
Performance Metrics ◽  
Similarity Index ◽  
Skin Lesions ◽  
Machine Learning Algorithms ◽  
Lesion Area ◽  
Melanoma Detection ◽  
Grabcut Algorithm

Skin lesion melanoma is the deadliest type of cancer. Artificial intelligence provides the power to classify skin lesions as melanoma and non-melanoma. The proposed system for melanoma detection and classification involves four steps: pre-processing, resizing all the images, removing noise and hair from dermoscopic images; image segmentation, identifying the lesion area; feature extraction, extracting features from segmented lesion and classification; and categorizing lesion as malignant (melanoma) and benign (non-melanoma). Modified GrabCut algorithm is employed to generate skin lesion. Segmented lesions are classified using machine learning algorithms such as SVM, k-NN, ANN, and logistic regression and evaluated on performance metrics like accuracy, sensitivity, and specificity. Results are compared with existing systems and achieved higher similarity index and accuracy.

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 SPOROTRIKOSIS LIMFOKUTAN DIAGNOSIS HISTOPATOLOGI DAN SITOLOGI

2013 ◽  
Vol 2 (1) ◽  
Author(s):  
Poppy M. Lintong ◽  
Inneke V. Sumolang
Keyword(s):  
Skin Lesion ◽  
Fine Needle Aspiration ◽  
Fine Needle Aspiration Biopsy ◽  
Histopathological Examination ◽  
Sporothrix Schenckii ◽  
Giant Cells ◽  
Skin Lesions ◽  
Needle Aspiration ◽  
Fine Needle ◽  
Left Hand

Abstract: Diagnosis of sporotrichosis associated with lymphocutaneous nodules was made based on the histopathological examination of skin lesions and the cytology of fine needle aspiration biopsy (FNAB). A case of sporotrichosis in a 63-year-old man was reported with papules and nodules spread along the back of the left hand, forearm, and arm. The histopatho-logical examination showed infiltration of PMNs, granulomas, and giant cells in the dermis and epidermis, along with hyperplasia and microabscesses. Sporothrix schenckii was not found in the skin lesion tissues. However, in the FNAB cytology examination of lymphocutaneus nodules we found spores of Sporothrix schenckii in the cytoplasma of histiocytes besides granuloma and infiltration of PMNs. Key words: sporothrix schenckii, histopathology, FNAB cytology.  Abstrak: Diagnosis sporotrikosis kulit dengan nodul limfokutan ditegakkan melalui pemerik-saan histopatologi pada lesi kulit dan sitologi biopsi aspirasi jarum halus pada nodul limfo-kutan. Kami melaporkan kasus sporotrikosis pada laki-laki berusia 63 tahun dengan papul-papul dan nodul-nodul eritematosa pada dorsum manus, antebrakium, dan brakium sinistra. Pemeriksaan histopatologi jaringan biopsi dari lesi kulit menunjukkan reaksi radang, gambaran granuloma, dan sel datia dalam dermis dan epidermis, dengan mikroabses disertai hiperplasia. Tidak ditemukan jamur Sporothrix schenckii dalam potongan jaringan histopatologi. Hasil pemeriksaan sitologi biopsi aspirasi jarum halus pada nodul limfokutan memperlihatkan adanya spora-spora jamur Sporothrix schenckii dalam sitoplasma sel-sel histiosit disamping  terdapatnya bentuk granuloma dalam infiltrat radang. Kata kunci: sporothrix schenckii, histopatologi, sitologi biopsi aspirasi jarum halus.

scholarly journals Performance Evaluation of Cloud Data Centers with Batch Task Arrivals

2013 ◽  
pp. 199-223 ◽  
Author(s):  
Hamzeh Khazaei ◽  
Jelena Mišić ◽  
Vojislav B. Mišić
Keyword(s):  
Performance Evaluation ◽  
Queue Length ◽  
Blocking Probability ◽  
General Distribution ◽  
Cloud Data ◽  
Wide Range ◽  
Accurate Performance ◽  
Cloud Data Centers ◽  
Poisson Arrivals

Accurate performance evaluation of cloud computing resources is a necessary prerequisite for ensuring that Quality of Service (QoS) parameters remain within agreed limits. In this chapter, the authors consider cloud centers with Poisson arrivals of batch task requests under total rejection policy; task service times are assumed to follow a general distribution. They describe a new approximate analytical model for performance evaluation of such systems and show that important performance indicators such as mean request response time, waiting time in the queue, queue length, blocking probability, probability of immediate service, and probability distribution of the number of tasks in the system can be obtained in a wide range of input parameters.

Visualizing ICT Change in the Academy

2008 ◽  
pp. 3695-3715
Author(s):  
Gale Parchoma
Keyword(s):  
Complexity Theory ◽  
Knowledge Economy ◽  
Distributed Learning ◽  
Learning Opportunities ◽  
Mediated Learning ◽  
Global Learning ◽  
Learning Networks ◽  
Strategic Adaptation ◽  
Wide Range ◽  
The World

This chapter introduces complexity theory as a theoretical framework for analyzing the influences of information and computer technologies (ICTs) on the structures, cultures, economies (reward systems), and pedagogical praxes within the Academy. An argument is made that the strategic adaptation of the academy’s structures, cultures, economies, and pedagogical praxes to the knowledge economy can help build a future where Academy-based distributed learning networks will transmit ICT-mediated learning opportunities around the world, thus providing flexible access for a wide range of learners to fully participate in the global learning society. The author posits attunements to policies and practices to support institution-wide involvement in ICT initiatives.

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