scholarly journals Sympatric threatened Iberian leuciscids exhibit differences in Aeromonas diversity and skin lesions’ prevalence

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255850
Author(s):  
Miguel L. Grilo ◽  
Lélia Chambel ◽  
Tiago A. Marques ◽  
Carla Sousa-Santos ◽  
Joana I. Robalo ◽  
...  
Keyword(s):  
Skin Lesion ◽  
Fish Species ◽  
Skin Lesions ◽  
Lesion Score ◽  
Individual Level ◽  
Multiplex Pcr Assay ◽  
Information Gap ◽  
Similarity Relationships ◽  
Phenotypic Methods ◽  
Genomic Typing

Assessments regarding health aspects of Iberian leuciscids are limited. There is currently an information gap regarding effects of infectious diseases on these populations and their role as a possible conservation threat. Moreover, differences in susceptibility to particular agents, such as Aeromonas spp., by different species/populations is not clear. To understand potential differences in Aeromonas diversity and load, as well as in the prevalence and proportion of skin lesions, in fishes exposed to similar environmental conditions, an observational study was implemented. Using a set of 12 individuals belonging to two sympatric Iberian leuciscid species (Squalius pyrenaicus and Iberochondrostoma lusitanicum), the skin lesion score in each individual was analyzed. Furthermore, a bacterial collection of Aeromonas spp. isolated from each individual was created and isolates’ load was quantified by plate counting, identified at species level using a multiplex-PCR assay and virulence profiles established using classical phenotypic methods. The similarity relationships of the isolates were evaluated using a RAPD analysis. The skin lesion score was significantly higher in S. pyrenaicus, while the Aeromonas spp. load did not differ between species. When analyzing Aeromonas species diversity between fishes, different patterns were observed. A predominance of A. hydrophila was detected in S. pyrenaicus individuals, while I. lusitanicum individuals displayed a more diverse structure. Similarly, the virulence index of isolates from S. pyrenaicus was higher, mostly due to the isolated Aeromonas species. Genomic typing clustered the isolates mainly by fish species and skin lesion score. Specific Aeromonas clusters were associated with higher virulence indexes. Current results suggest potential differences in susceptibility to Aeromonas spp. at the fish species/individual level, and constitute important knowledge for proper wildlife management through the signalization of at-risk fish populations and hierarchization of conservation measures.

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 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 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.

Proposed Threshold Algorithm for Accurate Segmentation for Skin Lesion

2015 ◽  
Vol 4 (2) ◽  
pp. 40-47
Author(s):  
T. Y. Satheesha ◽  
D. Sathyanarayana ◽  
M. N. Giri Prasad
Keyword(s):  
Skin Cancer ◽  
Skin Lesion ◽  
Skin Lesions ◽  
Large Set ◽  
Threshold Values ◽  
Diagnostic Systems ◽  
Automated Diagnosis ◽  
Algorithm Efficiency ◽  
Threshold Algorithms ◽  
Threshold Technique

Automated diagnosis of skin cancer can be easily achieved only by effective segmentation of skin lesion. But this is a highly challenging task due to the presence of intensity variations in the images of skin lesions. The authors here, have presented a histogram analysis based fuzzy C mean threshold technique to overcome the drawbacks. This not only reduces the computational complexity but also unifies advantages of soft and hard threshold algorithms. Calculation of threshold values even the presence of abrupt intensity variations is simplified. Segmentation of skin lesions is easily achieved, in a more efficient way in the following algorithm. The experimental verification here is done on a large set of skin lesion images containing every possible artifacts which highly contributes to reversed segmentation outputs. This algorithm efficiency was measured based on a comparison with other prominent threshold methods. This approach has performed reasonably well and can be implemented in the expert skin cancer diagnostic systems

Comparing the capabilities of transfer learning models to detect skin lesion in humans

2020 ◽  
Vol 234 (10) ◽  
pp. 1083-1093 ◽  
Author(s):  
Aditi Singhal ◽  
Ramesht Shukla ◽  
Pavan Kumar Kankar ◽  
Saurabh Dubey ◽  
Sukhjeet Singh ◽  
...  
Keyword(s):  
Skin Lesion ◽  
Transfer Learning ◽  
Wiener Filter ◽  
Skin Lesions ◽  
Automated Classification ◽  
Convolutional Networks ◽  
Image Quantization ◽  
Malignant Skin ◽  
Effective Diagnosis

Effective diagnosis of skin tumours mainly relies on the analysis of the characteristics of the lesion. Automatic detection of malignant skin lesion has become a mandatory task to reduce the risk of human deaths and increase their survival. This article proposes a study of skin lesion classification using transfer learning approach. The transfer learning model uses four different state-of-the-art architectures, namely Inception v3, Residual Networks (ResNet 50), Dense Convolutional Networks (DenseNet 201) and Inception Residual Networks (Inception ResNet v2). These models are trained under the dataset comprising seven different classes of skin lesions. The skin lesion images are pre-processed using image quantization, grayscaling and the Wiener filter before final training step. These models are compared for performance evaluation on different metrics. The present study shows the efficacy of the methodology for automated classification of lesion images.

scholarly journals Behaviour and Skin Injuries of Piglets Originating from a Novel Group Farrowing System Before and After Weaning

Agriculture ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 93 ◽  
Author(s):  
Lilith Schrey ◽  
Nicole Kemper ◽  
Michaela Fels
Keyword(s):  
Body Weight ◽  
Weight Gain ◽  
Body Weight Gain ◽  
Skin Lesions ◽  
Lesion Score ◽  
Skin Injuries ◽  
Individual Housing ◽  
Common Area ◽  
Before And After ◽  
The Common

The aim of this study was to analyse a novel group farrowing system (GH) concerning piglets’ behaviour, skin injuries and body weight gain, to test its animal friendliness. Skin injuries and weight gain were compared to piglets originating from conventional individual housing (IH) before and after weaning. The GH system had five farrowing pens without crates, a common area and an area only available for piglets. In total, 34 litters were studied. Four days after the GH-piglets had left the pens during lactation, the lesion score of piglets in GH was higher than in IH. However, piglets from the GH sustained fewer injuries after mixing at weaning, compared to the piglets from IH and had higher daily weight gains, during the early nursery phase. The common area in GH was intensively used for active behaviour, since standing/walking and playing were observed there, most frequently, whereas lying occurred most frequently inside the pens. Immediately after the piglets had left the pens in the GH, the piglets preferred proximity to the sow, compared to the pens where they were born. The GH system enabled social enrichment, offered increased space for activity and led to fewer skin lesions, after weaning; thus, potentially increasing animal welfare.

scholarly journals Significance of Histopathology in Leprosy Patients with 1–5 Skin Lesions with Relevance to Therapy

2011 ◽  
Vol 3 (01) ◽  
pp. 021-024 ◽  
Author(s):  
S Veena ◽  
Prakash Kumar ◽  
Shashikala P. ◽  
Gurubasavaraj H. ◽  
H R Chandrasekhar ◽  
...  
Keyword(s):  
Drug Therapy ◽  
Skin Lesion ◽  
Skin Lesions ◽  
Clinicopathological Correlation ◽  
Adequate Treatment ◽  
Duration Of Therapy ◽  
Tuberculoid Leprosy ◽  
Leprosy Patients ◽  
Skin Biopsies ◽  
Severity Of The Disease

ABSTRACT Background: Patients with 1-5 skin lesions are clinically categorized as paucibacillary for treatment purposes. For betterment and adequate treatment of patients, this grouping needs further study. Aim: To study a group of leprosy patients with 1-5 skin lesions, compare clinical details with histopathological findings and bacteriological status of the skin to evaluate the relevance of this grouping. Materials and Methods: Two-year study involving 31 patients of leprosy with 1-5 skin lesions was included in this study. A number of skin lesions were recorded. Skin biopsies were taken in all patients. The biopsies were evaluated for the type of pathology and acid fast bacilli (AFB) status. Results: Of 31 patients, 19 (61.2%) had single skin lesion, 7 (22.5%) had two lesions, 4 (12.9%) had three lesions, and only one (3.22%) had four lesions, there were no patients with five lesions. Of the 31 patients, 30 (96.7%) were clinically diagnosed as borderline tuberculoid and one patient (3.22%) has tuberculoid leprosy. Skin smears were negative for AFB in all patients. The histological diagnoses were: TT 1 (3.22%), BT 24 (77.41%), and IL 6 (19.2%). AFB were found in 2 (6.45%) out of 31 skin biopsies. Clinicopathological correlation was 76.6% in the BT group. Conclusion: Tissue biopsy findings in 1-5 skin lesions which were not considered relevant for treatment purposes until now should be given a status in the categorization and assessment of severity of the disease. The significance of finding of AFB and histopathology of multibacillary (MB) type of leprosy in tissue biopsies, in patients grouped as PB should be resolved so that patients could be given the drug therapy and duration of therapy they warrant.

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