Congenital Cutaneous Candidiasis: A Case Presentation

2002 ◽  
Vol 21 (6) ◽  
pp. 9-12 ◽  
Author(s):  
Robin Knobel
Keyword(s):  
Skin Lesion ◽  
Nurse Practitioner ◽  
Skin Infection ◽  
Skin Lesions ◽  
Delivery Room ◽  
Differential Diagnoses ◽  
Case Presentation ◽  
Cutaneous Candidiasis ◽  
Unnecessary Testing

When an infant presents in the delivery room with macular and papular skin lesions covering the trunk, extremities, and/or skin folds, the neonatal nurse practitioner covering deliveries must be aware of possible skin lesion differential diagnoses. Among these is congenital cutaneous candidiasis, a rare, usually benign skin infection. If this condition is recognized early, unnecessary testing and treatment of newborns who present with these symptoms might be avoided.

scholarly journals A Brief Study on Primary Melanoma over Breast Region

2021 ◽  
pp. 86-90
Author(s):  
K. M. Hiwale ◽  
Avadh Kishor Tyagi
Keyword(s):  
Lymph Node ◽  
Survival Rate ◽  
Malignant Melanoma ◽  
Skin Lesion ◽  
Adjuvant Therapy ◽  
Primary Melanoma ◽  
Skin Lesions ◽  
Axillary Lymph ◽  
Case Presentation ◽  
Mucous Membranes

Background: Malignant melanoma is mostly found in mucous membranes and skin. So it’s occurrence on the breast skin is very rare. Case Presentation: In our study, 50-year-old female came to OPD with complaint of skin lesions on the breast since 2 months. On diagnosis, skin lesion was found to be malignant melanoma and the disease had metastasized in right axillary lymph node with discoloration over breast. Conclusion: The prognosis for patients with this disease is very poor. Important procedures which may increase the survival rate include, Early diagnosis and surgical resection with adjuvant therapy.

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 Complicated cutaneous leishmaniasis caused by an imported case of Leishmania tropica in Japan: a case report

2021 ◽  
Vol 49 (1) ◽  
Author(s):  
Hiroyuki Kitano ◽  
Chizu Sanjoba ◽  
Yasuyuki Goto ◽  
Kazumasa Iwamoto ◽  
Hiroki Kitagawa ◽  
...  
Keyword(s):  
Cutaneous Leishmaniasis ◽  
Skin Lesions ◽  
Leishmania Tropica ◽  
Case Presentation ◽  
Imported Case ◽  
Imported Cases ◽  
History Of ◽  
Hematoxylin And Eosin ◽  
Polymerase Chain ◽  
Positive Results

Abstract Background Leishmaniasis is not endemic in Japan, and imported cases are rare. However, there are increasing concerns regarding imported cases of cutaneous leishmaniasis from endemic countries to Japan. This report describes a case of imported cutaneous leishmaniasis that was diagnosed and treated in Japan. Case presentation A 53-year-old Pakistani man presented with skin lesions on both malleoli of his right ankle and the dorsum of the left foot. The skin lesions manifested as erythematous nodules surrounding an ulcer in the center of the lesion. The lesions of the malleoli of his right ankle each measured 3 × 3 cm, and the lesion on the top of his left foot measured 5 × 4 cm. He had been living and working in Japan but had a history of a visit to Pakistan for about 2 months in 2018. The skin lesions were biopsied. Giemsa and hematoxylin and eosin staining of biopsy samples showed amastigotes of Leishmania in macrophages, and the presence of Leishmania was confirmed by skin tissue culture. Polymerase chain reaction using biopsy specimens identified Leishmania parasites, and DNA sequence analysis revealed that the species was Leishmania tropica. The patient was treated with intravenous liposomal amphotericin B for 6 days. The erythema disappeared, and the erythematous nodules resolved within 3 weeks. Conclusion This is the first report of imported cutaneous leishmaniasis caused by L. tropica from Pakistan, and it is interesting that all three testing modalities showed positive results in this case.

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

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