Diagnosis Of Skin Diseases Research Articles

Accuracy and confidence of Irish general practitioners in diagnosing skin disease in patients with darkly pigmented skin

Accuracy and confidence of Irish general practitioners in diagnosing skin disease in patients with darkly pigmented skin

SISTEM PAKAR DIAGNOSIS PENYAKIT KULIT MENGGUNAKAN METODE FORWARD CHAINING DENGAN PYTHON

Abstract. Skin is the outer layer of the body that functions to protect the inside. Like other parts of the body, the skin is also susceptible to disorders or diseases. Currently, the need for healthy skin is increasing due to pollution, free radicals and hot weather. Increased awareness about the importance of healthy skin encourages people to pay more attention to skincare. However, delays in identifying the type of disease and lack of knowledge about prevention can lead to more severe skin conditions, such as cancer. An expert system is an information system that utilizes knowledge and decision-making methods from experts in a particular field. Therefore, the author developed a system to help people with early diagnosis of skin diseases using the forward chaining method and the Python programming language. This implementation succeeded in creating an expert system capable of diagnosing skin diseases using the forward chaining method and Python. Based on testing each existing rule, the expert system succeeded in providing a diagnosis, description, solution or medicine, as well as a picture of the disease based on the symptoms reported by the user.

LS-Net: lightweight segmentation network for dermatological epidermal segmentation in optical coherence tomography imaging.

Optical coherence tomography (OCT) can be an important tool for non-invasive dermatological evaluation, providing useful data on epidermal integrity for diagnosing skin diseases. Despite its benefits, OCT's utility is limited by the challenges of accurate, fast epidermal segmentation due to the skin morphological diversity. To address this, we introduce a lightweight segmentation network (LS-Net), a novel deep learning model that combines the robust local feature extraction abilities of Convolution Neural Network and the long-term information processing capabilities of Vision Transformer. LS-Net has a depth-wise convolutional transformer for enhanced spatial contextualization and a squeeze-and-excitation block for feature recalibration, ensuring precise segmentation while maintaining computational efficiency. Our network outperforms existing methods, demonstrating high segmentation accuracy (mean Dice: 0.9624 and mean IoU: 0.9468) with significantly reduced computational demands (floating point operations: 1.131 G). We further validate LS-Net on our acquired dataset, showing its effectiveness in various skin sites (e.g., face, palm) under realistic clinical conditions. This model promises to enhance the diagnostic capabilities of OCT, making it a valuable tool for dermatological practice.

Enhancing Skin Disease Diagnosis with TFFNet: A Two-Stream Feature Fusion Network Integrating CNNs and Self Attention Block

The skin of an individual serves as the primary defense mechanism for safe guarding vital organs in the body. Although this barrier effectively protects internal organs from a variety of threats, it is still prone to damage from viral, fungal, or dust-related illnesses. Even minor skin injuries possess the potential to escalate into more severe and hazardous conditions. A prompt and precise skin disease diagnosis becomes crucial in expediting the healing process for individuals grappling with skin-related issues. The objective of this study is to develop a system based on Convolutional Neural Network (CNN) that can accurately identify various skin diseases. The proposed architecture, known as TFFNet (Two-Stream Feature Fusion Network), integrates two simultaneous modules featuring a Self-Attention (SA) block. We employ Self Attention-Convolutional Neural Networks (SACNNs) and Depthwise Separable Convolution (DWSC) to establish a diagnostic system for skin diseases. In this method, two separate CNN models are joined together, and two parallel modules (M1 and M2) are added. This greatly reduces the total number of trainable parameters. In comparison to other deep learning methods outlined in existing literature, the proposed CNN exhibits a notably lower number of learned parameters, specifically around 7 million for classification purposes. The skin disease classification was carried out on three datasets—ISIC2016, ISIC2017, and HAM10000. The model achieved testing accuracies of 89.70%, 90.52%, and 90.12% on each respective dataset.

Enhancing Skin Disease Diagnosis Through Fine-Tune Convolutional Neural Network: A Comparative Study with Multi-class Approach

Due to their similar appearance, skin disorders frequently disguise their early warning signs from our skin, which is the defense system of the body. Preventing serious disorders requires their early detection. This work investigated the use of fine-tune transfer learning as a fast and accurate way to diagnose skin diseases. To classify different skin issues, we used pre-trained models, i.e., InceptionV3, DenseNet201, and Xception. This work examined 17,500 photos from three sources. It was found that fine-tune Xception performed exceptionally well, with an accuracy rate of 99.14%. It was closely followed by DenseNet201 and InceptionV3, each with different processing speeds, 98.74% and 98.46%, respectively. We used transfer learning with data sets validated by medical experts, outperforming earlier research in precision. This more accurate detection of skin diseases could greatly improve patient outcomes and expedite medical procedures. This approach is new in that it fine-tunes transfer learning by utilizing a vast number of data to increase accuracy compared to other researcher works.

Anxiety, depression and quality of life in dermatology patients at a tertiary care hospital.

To compare the anxiety, depression and health-related quality of life of dermatology patients with healthy individuals in a tertiary care setting. The case-control study was conducted at the Pak-Emirates Military Hospital, Rawalpindi, Pakistan, from April to September 2022, and comprised adult outpatients and inpatients with diagnosed skin diseases. They were designated as group A, while healthy adults who did not suffer from any dermatological condition formed control group B. Data was collected using a questionnaire which in addition to demographic characteristics, comprised the Dermatology Life Quality Index and the Hospital Anxiety and Depression Scale. Data was analysed using SPSS 21. Of the 200 subjects with mean age 30.21±12.09 years, 100(50%) were in group A; 75(75%) males and 25(25%) females with mean age 32.56±13.49 years. The remaining 100(50%) subjects were in group B; 75(75%) males and 25(25%) females with mean age 27.86±10.02. In group A, 71(71%) were outpatients and 29(29%) were inpatients. Quality of life and depression scores were significantly different between inpatients in group A and group B (p<0.05). The difference was significant between outpatients in group A and group B in all categories (p<0.05). Dermatological diseases had a significant impact on the daily life and psychological health of patients.

High-Precision Skin Disease Diagnosis through Deep Learning on Dermoscopic Images.

Dermatological conditions are primarily prevalent in humans and are primarily caused by environmental and climatic fluctuations, as well as various other reasons. Timely identification is the most effective remedy to avert minor ailments from escalating into severe conditions. Diagnosing skin illnesses is consistently challenging for health practitioners. Presently, they rely on conventional methods, such as examining the condition of the skin. State-of-the-art technologies can enhance the accuracy of skin disease diagnosis by utilizing data-driven approaches. This paper presents a Computer Assisted Diagnosis (CAD) framework that has been developed to detect skin illnesses at an early stage. We suggest a computationally efficient and lightweight deep learning model that utilizes a CNN architecture. We then do thorough experiments to compare the performance of shallow and deep learning models. The CNN model under consideration consists of seven convolutional layers and has obtained an accuracy of 87.64% when applied to three distinct disease categories. The studies were conducted using the International Skin Imaging Collaboration (ISIC) dataset, which exclusively consists of dermoscopic images. This study enhances the field of skin disease diagnostics by utilizing state-of-the-art technology, attaining exceptional levels of accuracy, and striving for efficiency improvements. The unique features and future considerations of this technology create opportunities for additional advancements in the automated diagnosis of skin diseases and tailored treatment.

The Usefulness of Line-Field Confocal Optical Coherence Tomography in Monitoring Epidermal Changes in Atopic Dermatitis in Response to Treatment: A Pilot Study.

Atopic dermatitis (AD) is the most common chronic inflammatory skin disease. Due to its high prevalence, considerable morbidity, and chronicity, there is a need for the accurate in vivo evaluation of treatment efficacy. Line-field confocal optical coherence tomography (LC-OCT) is a new emerging imaging technique able to perform a non-invasive, real-time examination of the epidermis and the upper dermis. LC-OCT may represent a promising tool in the diagnosis and treatment follow-up of chronic eczematous skin diseases with barrier defects. We aimed to investigate the role of LC-OCT in the non-invasive monitoring of the treatment effect on five patients with severe atopic dermatitis during dupilumab treatment. LC-OCT imaging was performed on five patients (three women and two men) aged between 14 and 85 years old at the baseline and at 2, 4, and 6 weeks of treatment with dupilumab. The LC-OCT scans were performed at two sites, the lesional skin in the antecubital fossa and the extensor part of the arm, considered a control site on each patient for comparison. The captured images were later evaluated. Descriptive statistics and a t-test were used to compare the analyzed parameters over time and between involved atopic skin and clinically healthy skin. The LC-OCT imaging was able to detect the difference in stratum corneum (SC) thickness and quality and epidermal thickness (ET) and the changes before and after treatment with high accuracy. The main findings include a significant reduction in the epidermal and stratum corneum thickness and decreased epidermal spongiosis and inflammation, with better quality of the stratum corneum indicating restoration of its tightness at both lesional and control sites. This study demonstrates that clinical improvement of affected and unaffected atopic skin under dupilumab treatment correlates with the LC-OCT findings. LC-OCT represents a novel, non-invasive tool examining the in vivo skin barrier and inflammation and can help to monitor the treatment efficacy among patients with atopic dermatitis in daily practice.

Effects of a skin type diversity seminar on undergraduate medical students’ self-assessed competence in managing skin diseases in patients with skin of color

BackgroundSkin diseases in patients with skin of colour (Fitzpatrick skin types IV to VI) are underrepresented in dermatology training, which may lead to lower quality of care for these patients. To address this underrepresentation in medical education, a newly developed seminar on skin type diversity using an interactive teaching method was implemented in an undergraduate medical curriculum. This study examined the effects of a seminar on the self-assessed competence of medical students in managing skin conditions in patients with skin of colour.MethodsA questionnaire survey was conducted among fourth-year undergraduate medical students at the University of Hamburg (Germany) between October 2023 and February 2024. Students’ self-assessed competence was compared before and after the obligatory seminar (pre- and post-design).ResultsIn total, 158 students participated in the survey. After the seminar, knowledge of the presentation of skin diseases in patients with skin of colour and the associated psychological burden, differences in the incidence of skin diseases in different skin types, and the ability to diagnose skin diseases in darker skin types increased. Most participants stated that they wanted to attend more courses on this topic.DiscussionAppropriate courses for medical students can improve their competence in managing different skin diseases in patients with skin of colour. In the future, more attention should be paid to teaching the diversity of skin types in dermatology education.

Lumpy skin disease diagnosis in cattle: A deep learning approach optimized with RMSProp and MobileNetV2.

Lumpy skin disease (LSD) is a critical problem for cattle populations, affecting both individual cows and the entire herd. Given cattle's critical role in meeting human needs, effective management of this disease is essential to prevent significant losses. The study proposes a deep learning approach using the MobileNetV2 model and the RMSprop optimizer to address this challenge. Tests on a dataset of healthy and lumpy cattle images show an impressive accuracy of 95%, outperforming existing benchmarks by 4-10%. These results underline the potential of the proposed methodology to revolutionize the diagnosis and management of skin diseases in cattle farming. Researchers and graduate students are the audience for our paper.

Advanced skin disease detection: Image processing and modified genetic optimization with supervised k-nearest neighbors

The use of modern technology to improve skin disease diagnosis includes things like artificial intelligence (AL), deep learning (DL) and computer vision. Medical images of skin lesions, dermatoscopic images, or thermal imaging can be analyzed by automated algorithms and image processing techniques to diagnose skin diseases. Diseases can cause physical and emotional suffering, making them silent killers. Extreme instances might cause skin cancer. skin disease diagnosis from clinical photographs is a key medical image processing challenge. Manual skin diagnosis takes time and it is subjective for doctors. Computerized skin disease prediction can simplify patient and dermatologist treatment planning. To tackle these issues, we proposed a genetic optimization and supervised K-Nearest Neighbor (MGO-SKNN) technique for detecting advanced skin diseases. The research gathers clinical data for detecting advanced skin diseases. Three methods were used to preprocess our data: noise reduction, hair removal and image resizing. The Grey Level Co-Occurrence Matrix (GLCM) approach was used in this work to extract features. The integration of image processing into MGO-SKNN represents a significant step forward in the quest for accurate, efficient and cutting-edge skin disease diagnosis. The metrics like accuracy (97.53%), precision (95.20%), recall (96.74%) and f1-score (98.06%) of our suggested technique, MGO-SKNN, exceed the traditional approaches for skin detection.

Skin colour and disease diagnosis: A cross-sectional study of medical students in Kuwait.

Skin colour and disease diagnosis: A cross-sectional study of medical students in Kuwait.

Simple tape-stripping method for highly reliable and quantitative analysis of skin microbiome.

The composition of human skin microbiome profoundly impacts host skin health and disease. However, the relationship between skin homeostasis or the development of skin diseases and daily changes in skin microbial composition is poorly understood. Longitudinal samplings at more frequent intervals would address this issue, while conventional sampling methods have technical difficulties, leading to limitations in sampling opportunities. Here, we developed a simple and stable tape-stripping method regardless of the operator's skill. Our method enables skin microbial sampling within 30 seconds and taking multiple skin microbial samples from the same body site. The amount of microbial DNA among multiple sampling sites could be measured within 13.5%. The sequencing results of multiple sampling showed high consistency, Pearson's correlation coefficient between multiple samples of 0.98. Furthermore, these results were comparable to those collected by the conventional swabbing method. These results demonstrate that our tape-stripping method enables simple microbiome collection and highly reliable quantitative skin microbiome analysis. These features of our method would lead to a further understanding of skin disease development or diagnosis of skin conditions in clinical research by increasing the opportunities for microbial sampling.

Result and Analysis of Soft Computing Techniques for Diagnosis of Skin Diseases

Abstract: A prevalent bodily issue that has a significant impact on people's lives is skin disease. An early and precise identification of the condition can assist patients in receiving proper care, hastening their recovery. Convolutional neural networks (CNN) powered by deep learning have recently made major advancements that have greatly increased the accuracy of illness categorization. This led to the motivation for this work, which used deep CNN architectures to identify two different skin conditions—eczema and psoriasis. Ten fold cross validation has been utilized to examine the performance of five distinct cutting-edge CNN architectures. With the Adam optimizer, the Inception ResNet v2 architecture achieves a maximum validation accuracy of 97.1%. The results of the performance matrices suggest that the model performs remarkably well in terms of diagnosing skin disorders. Additionally, the study demonstrates two approaches for the practical application of the implemented model. (i) CNN Approach ii) PCA Approach.

LW-XNet for segmentation and classification of skin lesions from dermoscopy images

The skin, one of the most crucial organs of the human body, serves as a barrier between the body and the external environment. Early detection of skin diseases is imperative to reduce mortality rates, as some untreated conditions may progress to skin cancer. Segmentation and classification of lesions represent pivotal and interrelated endeavors within the realm of skin disease diagnosis. For this purpose, this paper presents a comprehensive diagnostic framework for segmentation and classification of skin lesions, which integrates a Legendre multiwavelet transform-based fusion XNet (LW-XNet) with an improved soft attention dense connection convolutional network (ISA-DenseNet). LW-XNet combines the strengths of XNet in fusing different frequency components of images, along with the strong feature representation capability of LW bases with various regularities for overall contextual information and detailed information of dermoscopy images. Furthermore, its encoder devises a LWT channel concatenate (LCC) block to subdivide the image into eight wavelet coefficient feature images and perform concatenated processing on them, enabling it to better differentiate and comprehend the intricate features within dermoscopy images. Finally, ISA-DenseNet is utilized for multi-class classification of the segmented images. Experimental results demonstrate the superiority of the proposed framework over existing segmentation and classification methods.

Clinical study of common bacterial, fungal and parasitic skin diseases in cats

Background: The skin is a big, metabolically active organ that has a significant need for other nutrients and protein in its body. The study pointed high significant infection with fungal, scabies, alopecia, bacterial and wounds in the white color cats more than the others because it is represented the better color choice for the families then the orange and grey colors while the black color is the less in number of domestic cats. The aim of present study was investigated of some skin disease in cats in Basrah, Iraq.Methods: The present study includes two hindered animals of both sexes with different ages, colors, weights and sizes were studied clinically in Basrah veterinary hospital and the private veterinary clinics in Basrah. The study extended from October 2022 to March 2023 during the winter season. Clinical signs were reported carefully, and the initial diagnosis of skin diseases was confirmed by apparent lesions on the skin, the skin samples of the cases were collected for laboratory diagnosis in veterinary medicine college ̸ Basrah University and Basrah veterinary hospital laboratories to confirmed diagnosis.Results: Clinical signs were reported carefully and the initial diagnosis of skin diseases. The two hindered sample cases were divided according to cases in Basrah veterinary hospital and Basrah private clinic: 60 Cases fungal infection, 40 cases scabies, 10 case alopecia, 50 cases of bacterial infection and 40 cases of wound. Conclusion: The present study shows the fungal diseases were common causes for skin diseases in Cats follow by Bacterial, Parasitic and Alopecia respectively.Keywords: Cat infection; Skin diseases in cat; Scabies; Fungal disease

Effect of Using GLCM and LBP+HOG Feature Extraction on SVM Method in Classification of Human Skin Disease Type

Skin diseases have been ranked third out of ten diseases suffered by outpatients in many hospitals in Indonesia. The public often underestimates these diseases because they are considered not to cause death. In general, dermatologists diagnose skin diseases using the biopsy process, but the biopsy process is quite expensive and can cause injury to the skin. Each skin disease has different texture and shape characteristics, so classification can be used to distinguish the type of skin disease. This study compares LBP+HOG and GLCM feature extraction with the SVM classification method to determine the best feature extraction in skin disease classification. This research compares GLCM and LBP+HOG feature extraction using the SVM method. GLCM is used to measure the relationship between pixel intensities in the image, while LBP+HOG combines information about the texture and shape of the image. The test results show that the GLCM feature extraction method with SVM classification achieves an accuracy of 74%. In this test, the parameters C=100 and the features used are homogeneity, contrast, energy, correlation, ASM, and dissimilarity. Meanwhile, extraction with LBP+HOG resulted in an accuracy of 68%.

Machine Learning Approaches for Skin Neoplasm Diagnosis.

Approaches for skin neoplasm diagnosis include physical exam, skin biopsy, lab tests of biopsy samples, and image analyses. These approaches often involve error-prone and time-consuming processes. Recent studies show that machine learning shows promise in effectively classifying skin images into different categories such as melanoma and melanocytic nevi. In this work, we investigate machine learning approaches to enhance the performance of computer-aided diagnosis (CADx) systems to diagnose skin diseases. In the proposed CADx system, generative adversarial network (GAN) discriminator is used to identify (and remove) fake images. Exploratory data analysis (EDA) is applied to normalize the original data set for preventing model overfitting. Synthetic minority oversampling technique (SMOTE) is employed to rectify class imbalances in the original data set. To accurately classify skin images, the following machine learning models are utilized: linear discriminant analysis (LDA), support vector machine (SVM), convolutional neural network (CNN), and an ensemble CNN-SVM. Experimental results using the HAM10000 data set demonstrate the ability of the machine learning models to improve CADx performance in treating skin neoplasm. Initially, the LDA, SVM, CNN, and ensemble CNN-SVM show 49%, 72%, 77%, and 79% accuracy, respectively. After applying GAN (discriminator) and SMOTE, the LDA, SVM, CNN, and ensemble CNN-SVM show 76%, 83%, 87%, and 94% accuracy, respectively. We plan to explore other machine learning models and data sets in our next endeavor.

Web-based skin disease diagnosis expert system

. In tropical nations, such as Indonesia, skin diseases are a common ailment. Skin conditions are typically not lethal illnesses, thus patients frequently disregard them and don't take them seriously. Skin conditions can, however, become uncomfortable and threaten the sufferer's survival if they are left untreated for an extended period of time. The result of this research is the creation of a web-based expert system for identifying skin disorders, which can aid in more accurate and timely diagnosis of skin diseases as well as offer treatment advice. The development of a web-based expert system for diagnosing skin diseases uses the forward chaining method. While the development methodology uses ESDLC (Expert System Development Life Cycle), with the phases of Assessment, Knowledge Acquisition, Design, Test, Documentation, and Maintenance

An extensive investigation of convolutional neural network designs for the diagnosis of lumpy skin disease in dairy cows

Cow diseases are a major source of concern for people. Some diseases in animals that are discovered in their early stages can be treated while they are still treatable. If lumpy skin disease (LSD) is not properly treated, it can result in significant financial losses for the farm animal industry. Animals like cows that sign this disease have their skin seriously affected. A reduction in milk production, reduced fertility, growth retardation, miscarriage, and occasionally death are all detrimental effects of this disease in cows. Over the past three months, LSD has affected thousands of cattle in nearly fifty districts across Bangladesh, causing cattle farmers to worry about their livelihood. Although the virus is very contagious, after receiving the right care for a few months, the affected cattle can be cured. The goal of this study was to use various deep learning and machine learning models to determine whether or not cows had lumpy disease. To accomplish this work, a Convolution neural network (CNN) based novel architecture is proposed for detecting the illness. The lumpy disease-affected area has been identified using image preprocessing and segmentation techniques. After the extraction of numerous features, our proposed model has been evaluated to classify LSD. Four CNN models, DenseNet, MobileNetV2, Xception, and InceptionResNetV2 were used to classify the framework, and evaluation metrics were computed to determine how well the classifiers worked. MobileNetV2 has been able to achieve 96% classification accuracy and an AUC score of 98% by comparing results with recently published relevant works, which seems both good and promising.