Diagnosis Of Skin Diseases Research Articles

Artificial Intelligence in microbiomes analysis: A review of applications in dermatology.

Microorganisms are closely related to skin diseases, and microbiological imbalances or invasions of exogenous pathogens can be a source of various skin diseases. The development and prognosis of such skin diseases are also closely related to the type and composition ratio of microorganisms present. Therefore, through detection of the characteristics and changes in microorganisms, the possibility for diagnosis and prediction of skin diseases can be markedly improved. The abundance of microorganisms and an understanding of the vast amount of biological information associated with these microorganisms has been a formidable task. However, with advances in large-scale sequencing, artificial intelligence (AI)-related machine learning can serve as a means to analyze large-scales of data related to microorganisms along with determinations regarding the type and status of diseases. In this review, we describe some uses of this exciting, new emerging field. In specific, we described the recognition of fungi with convolutional neural networks (CNN), the combined application of microbial genome sequencing and machine learning and applications of AI in the diagnosis of skin diseases as related to the gut-skin axis.

Dermatitis Atopic and Psoriasis Skin Disease Classification by using Convolutional Neural Network

Skin is the one of the body parts that play a large role in human physical body. There are so many functions of the skin such as offering protection against fungal infection, bacteria, allergy, viruses and controls the temperature of the body. But, the reported shown that the skin disease is the most common disease in humans among all age groups and a significant root of infection. The diagnosis of skin diseases involves several tests. Due to this, the diagnosis process is seen to be intensely laborious, time-consuming and requires an extensive understanding aspecially for the skin disease that have similar symptoms. Two skin diseases that have similar symptoms and most misdiagnosed are atopic dermatitis and psoriasis. Convolutional Neural Network for image processing and classifying have been developed for more accurate classification of skin diseases with different architectures. However, the accuracy in determining skin lesions using CNNs is on the average level. The factors that affect the accuracy result of a CNN is the depth where gradients vanished as the network goes deeper. Another factor is the variance in the training set which means the need of the large size of training set. Hence, in this study we tried 10 CNN architecture to get the best result for classifying dermatitis atopic and psoriasis. These are VGG 16, VGG 19, ResNet 50, ResNet 101, MobileNet, MobileNet V2, DenseNet 121, DenseNet 201, Inception and Xception. Experimental result shown that the inception V3 architecture give the best result with accuracy for data testing 84%, accuracy for unseen data 82% and confusion matrix with True positive obtained is 248, True Negative is 61, False positive is 54 and False Negative 298.

An in-depth analysis of Convolutional Neural Network architectures with transfer learning for skin disease diagnosis

Low contrasts and visual similarity between different skin conditions make skin disease recognition a challenging task. Current techniques to detect and diagnose skin disease accurately require high-level professional expertise. Artificial intelligence paves the way for developing computer vision-based applications in medical imaging, like recognizing dermatological conditions. This research proposed an efficient solution for skin disease recognition by implementing Convolutional Neural Network (CNN) architectures. Computer vision-based applications using CNN architectures, MobileNet and Xception, are used to construct an expert system that can accurately and efficiently recognize different classes of skin diseases accurately and efficiently. The proposed CNN architectures used a transfer learning method in which models are pre-trained on the Imagenet dataset to discover more features. We also evaluated the performance of our proposed approach with some of the most popular CNN architectures: ResNet50, InceptionV3, Inception-ResNet, and DenseNet, thus establishing a comparison to set up a benchmark that will ratify the essence of transfer learning and augmentation. This study uses data from two separate data sources to collect five different types of skin disorders. Different performance evaluation indicators, including accuracy, precision, recall, and F1-score, are calculated to verify the success of our technique. The experimental results revealed the effectiveness of our proposed approach, where MobileNet achieved a classification accuracy of 96.00%, and the Xception model reached 97.00% classification accuracy with transfer learning and augmentation. Moreover, we proposed and implemented a web-based architecture for the real-time recognition of diseases.

Caring for transgender populations: A primer for the dermatopathologist.

With increasing access to electronic health records, patients may encounter dermatopathology reports more readily. Dermatopathologists should consider their impact and interactions with transgender patients, who may face specific health and healthcare inequities. Rendering accurate diagnosis for skin diseases requires accurate information about patient's sex assigned at birth and gender identity. Understanding how sex and gender identity data flow between electronic health records, laboratory information systems, insurance billing systems, and patients will be important to avoid patient misgendering, to render accurate diagnoses, to maintain consistency in dermatopathology reports, and to avoid insurance billing denials. Dermatopathologists have important roles to build patient trust in the healthcare system and to help dermatologists diagnose, treat, and characterize skin diseases in transgender populations.

Android-Based Herpes Disease Detection Application using Image Processing

Herpes is a viral infection that causes a skin disease that is widespread throughout the world. Herpes virus is a DNA virus transmitted via infected skin, saliva, and other body fluids. Herpes is characterized by chickenpox-like nodules in one area of the skin, swollen tissue surrounding the nodule, and blister formation on the nodule. Digital image processing that can detect herpes disease is anticipated to reduce physical contact between physicians and patients during skin disease diagnosis. This study's methodology includes collecting data on herpes disease, developing machine-learning models using the CNN algorithm, and deploying the model as an Android application. This study makes use of actual data collected via smartphones, Pocket Cameras, and internet-sourced photographs. The data include 12,645 images of skin affected by herpes and normal skin. Using 100 epochs and the Adadelta optimizer, the accuracy of this study is 85 percent.

Self-reported clinical history of misdiagnosed leprosy cases in the State of Mato Grosso, Brzil, 2016-2019.

This study aimed to analyze the self-reported clinical history of patients misdiagnosed with leprosy in the State of Mato Grosso, Brazil. This is a cross-sectional study of new leprosy cases diagnosed in the State of Mato Grosso from 2016 to 2019, with individuals who were released from multidrug therapy due to misdiagnosis after starting treatment. Data were collected via telephone interviews. Over the study period, 354 leprosy cases were released from treatment due to misdiagnosis, of which 162 (45.8%) could be interviewed. All interviewees expressed dissatisfaction with their treatment, which prompted them to seek a reevaluation of their diagnosis before they were released due to "misdiagnosis". Among them, 35.8% received a final diagnosis of a musculoskeletal or connective tissue disease - mainly fibromyalgia and degenerative changes in the spine - followed by 13.6% with diagnoses of skin and subcutaneous tissue diseases. For 23.5% of the respondents, no alternative diagnosis was established, whereas 7.4% were later re-diagnosed with leprosy. Fibromyalgia and spinal problems were the most common alternative diagnoses for erroneous leprosy. Although the diagnosis of leprosy is usually clinical and does not require access to technical infrastructure in most cases, some more complex situations require diagnostic support via complementary tests, as well as close collaboration between primary care and reference services.

Nonlinear optical microscopy for skinin vivo: Basics, development and applications

Multi-photon microscopy (MPM) and coherent anti-Stokes Raman scattering (CARS) are two advanced nonlinear optical imaging techniques, which provide complementary information and have great potential in combination for noninvasive in vivo biomedical applications. This paper provides a detailed discussion of the basics, development and applications of these technologies for in vivo skin research, covering the following topics: The principle and advantage of MPM and CARS, instrumentation development for in vivo applications, MPM and CARS of normal skin, application of MPM and CARS in skin cancer and disease diagnosis; application of MPM in skin disease intervention, i.e., imaging guided two-photon photothermolysis.

A Scheme for Effective Skin Disease Detection using Optimized Region Growing Segmentation and Autoencoder based Classification

Detecting skin disorders just through visual inspection is difficult due to the complicated and overlapping nature of sick lesions, background skin textures, skin hair, low illumination, etc. Computer Vision and Machine Learning are playing a great role in identifying the correct type of diseased lesion. However, in the presence of the aforementioned artefacts, current computational approaches are equally limited in their ability to detect complicated lesion structures. We provide a novel detection framework to enhance skin disease diagnosis. Beginning with segmentation and feature extraction from diseased lesions, this framework uses autoencoder based classification model. Diseased lesions are segmented using Optimized Region Growing using Grey Wolf Optimization (GWO). Texture features are extracted from the segmented lesion using Gray Level Co-occurrence Matrix (GLCM), and Weber Local Descriptor (WLD). Finally, a reduced feature set is generated through latent representation using the autoencoder. An integrated convolutional neural network is used to classify the diseased lesions from latent representation. The proposed framework significantly outperformed the traditional deep classification strategies, according to experimental findings.

An Internet of Things (IoT)-based Veterinary Support System for Livestock Skin Disease Health Care using MobileNetV2

In this work, we present a novel approach for the early detection and diagnosis of skin diseases in farm animals, a major concern that can lead to reduced productivity, decreased animal welfare, and economic losses. Using Internet of Things (IoT) and MobileNetV2, we have developed a system that is built using Raspberry Pi for the gateway and low-power ESP 32 microcontrollers for sensor attachment. This system consists of sensors placed on the animals

Skin Diseases Diagnosis System Based on Machine Learning

Skin diseases are very common across the globe and especially in underdeveloped countries. Increasing climate changes and global warming are among the major causes of cancerous skin diseases. Skin diseases can be deadly if not diagnosed and treated at their initial stages. Clinical skin disease diagnosis methods are very rarely available for a common person and these methods are very expensive as well. The advancement in the field of image processing and machine learning made it possible to use machine learning methods for feature extraction and diagnosis of medical illnesses. Deep learning on the emerging subfields of machine learning methods that are more advance and intelligent in the classification of images based on different features. In this work, different deep learning models are used which are based on Convolution Neural Networks (CNN) architecture like ResNet-50, the DenseNet-121, and sequential CNN model for feature extraction and the classification of skin disease categories using HAM10000 dataset from ISIC archive 2018. The HAM10000 dataset consists of 10015 images from seven different skin disease categories. The dataset is further subdivided into testing and training data with a ratio of 20% and 80% correspondingly. The skin disease diagnosis is a three-step process that includes dataset images preprocessing in the first stage, feature extraction using CNN models in the second stage, and classification of skin disease category based on the extracted features using different classifiers in the third stage. Image augmentation techniques were used to reduce the imbalance in different skin disease classes. The sequential CNN-based model using 7 convolution layers gives an accuracy of 98% and 99% Area Under the Curve (AUC) for all seven skin disease categories. ResNet-50 and DenseNet-121 give an accuracy of 84% and 89% respectively. The performance of different CNN models on the given dataset is compared and evaluated using different performance matrixes.

Skin lesion classification based on two-modal images using a multi-scale fully-shared fusion network

Background and objectiveDue to the complexity of skin lesion features, computer-aided diagnosis of skin diseases based on multi-modal images is considered a challenging task. Dermoscopic images and clinical images are commonly used to diagnose skin diseases in clinical scenarios, and the complementarity of their features promotes the research of multi-modality classification in the computer-aided diagnosis field. Most current methods focus on the fusion between modalities and ignore the complementary information within each of them, which leads to the loss of the intra-modality relation. Multi-modality models for integrating features both within single modalities and across multiple modalities are limited in the literature. Therefore, a multi-modality model based on dermoscopic and clinical images is proposed to address this issue. MethodsWe propose a Multi-scale Fully-shared Fusion Network (MFF-Net) that gathers features of dermoscopic images and clinical images for skin lesion classification. In MFF-Net, the multi-scale fusion structure combines deep and shallow features within individual modalities to reduce the loss of spatial information in high-level feature maps. Then Dermo-Clinical Block (DCB) integrates the feature maps from dermoscopic images and clinical images through channel-wise concatenation and using a fully-shared fusion strategy that explores complementary information at different stages. ResultsWe validated our model on a four-class two-modal skin diseases dataset, and proved that the proposed multi-scale structure, the fusion module DCBs, and the fully-shared fusion strategy improve the performance of MFF-Net independently. Our method achieved the highest average accuracy of 72.9% on the 7-point checklist dataset, outperforming the state-of-the-art single-modality and multi-modality methods with an accuracy boost of 7.1% and 3.4%, respectively. ConclusionsThe multi-scale fusion structure demonstrates the significance of intra-modality relations between clinical images and dermoscopic images. The proposed network combined with the multi-scale structure, DCBs, and the fully-shared fusion strategy, can effectively integrate the features of the skin lesions across the two modalities and achieved a promising accuracy among different skin diseases.

Issues in Melanoma Detection: Semisupervised Deep Learning Algorithm Development via a Combination of Human and Artificial Intelligence.

Automatic skin lesion recognition has shown to be effective in increasing access to reliable dermatology evaluation; however, most existing algorithms rely solely on images. Many diagnostic rules, including the 3-point checklist, are not considered by artificial intelligence algorithms, which comprise human knowledge and reflect the diagnosis process of human experts. In this paper, we aimed to develop a semisupervised model that can not only integrate the dermoscopic features and scoring rule from the 3-point checklist but also automate the feature-annotation process. We first trained the semisupervised model on a small, annotated data set with disease and dermoscopic feature labels and tried to improve the classification accuracy by integrating the 3-point checklist using ranking loss function. We then used a large, unlabeled data set with only disease label to learn from the trained algorithm to automatically classify skin lesions and features. After adding the 3-point checklist to our model, its performance for melanoma classification improved from a mean of 0.8867 (SD 0.0191) to 0.8943 (SD 0.0115) under 5-fold cross-validation. The trained semisupervised model can automatically detect 3 dermoscopic features from the 3-point checklist, with best performances of 0.80 (area under the curve [AUC] 0.8380), 0.89 (AUC 0.9036), and 0.76 (AUC 0.8444), in some cases outperforming human annotators. Our proposed semisupervised learning framework can help with the automatic diagnosis of skin disease based on its ability to detect dermoscopic features and automate the label-annotation process. The framework can also help combine semantic knowledge with a computer algorithm to arrive at a more accurate and more interpretable diagnostic result, which can be applied to broader use cases.

Molecular Detection and Seroprevalence of Lumpy Skin Disease in Cattle

Background: To investigate seroprevalence, clinical manifestations, molecular diagnosis of Lumpy Skin Disease in cattle in an around Parbhani during outbreak of LSD. Methods: The cattle irrespective of their age, breed and gender were screened for detection of LSDV. Duration of study period was from December, 2020 to August, 2021. The cattle showed high fever, presence of nodules on body, nasal discharge, ocular discharge, brisket oedema and lameness were screened for Lumpy Skin Disease and were further confirmed using molecular tests. Total 478 cattle were screened for the seroprevalence of Lumpy Skin Disease by ELISA and PCR tests. The cattle were categorized into different age groups as calves (less than 0-2 years), young (between 2-4 years) and adults (4 years and above). The various breeds such as Khillar, Red Kandhari, Gir, Holstein-Friesen, cross-bred and non-descript cattle were screened. The seroprevalence of Lumpy Skin Disease in female and male was also recorded. Result: The molecular assay of LSD revealed that, out of 478 cattle screened, 267 (55.85%) serum samples were found positive by ELISA test. Whereas, among 267 blood samples and 8 scab samples tested by PCR, 82 blood samples (30.7%) and 5 scab samples (62.5%) were found positive for LSDV in cattle. LSD is an emerging, transboundary and economically important viral disease of cattle. The overall seroprevalence of LSD in cattle was 55.85%. Prominent clinical manifestations in LSD affected cattle was Prominent clinical manifestations in LSD affected cattle were fever followed by generalized appearance of skin nodules on body. In addition, LSD affected cattle showed oculo-nasal discharge, enlarged lymph nodes, brisket oedema, lameness, corneal opacity and anorexia. The scab samples were more reliable material for LSDV confirmation by PCR.

Detection and diagnosis of skin diseases by using snake algorithm and neural networks

This research presents a medical application to diagnose types of skin diseases based on the axis of dermatology, where a computer intelligent system was built based on identifying and diagnosing the type of skin inflammation, and this system called (SANN) (Snake Algorithm Neural Network). The system consists of two stages, the first stage is the axis of locating the presence of skin inflammation and distinguish it from uninfected skin by performing the initial processing of the image using the snake algorithm. while the second stage using neural networks to diagnose types of skin disease that were identified in the first stage by adding some improvements to the neural networks to work more efficiently for diagnosis. The suggested SANN system was applied to 250 images, and the accuracy and execution time were calculated. The results showed that using the system based on the snake algorithm and neural networks in the process of identifying and diagnosing types of dermatitis (psoriasis or Spider birthmark) achieves high performance and accuracy, and gave a diagnosis rate of 88.9%

Perceptual learning modules in undergraduate dermatology teaching.

Dermatological diagnosis depends highly on visual skills, and implicit nonanalytical proficiency plays a key role. To correctly diagnose skin diseases, the clinician needs visual skills, and intuitive recognition plays a key role. To investigate the effectiveness of digital perceptual learning modules (PLMs) in undergraduate teaching, and how these affect medical students' learning about skin diseases. This was a study performed in Finland, which enrolled 39 students of an undergraduate dermatology course. Online PLMs designed for dermatology, using different pictures of skin diseases were performed three times: before, during and at the end of the course. The modules provided four outcome measures: diagnostic accuracy (percentage of correct responses), a rating of confidence about the decision, fluency (response/decision time) and a list of features on which the decision was based. As the number of PLMs and the course duration increased, there were also improvements in the four measures, with a significant increase in diagnostic accuracy [from 66% to 94%; P < 0.001; partial η2 (η2 p ) = 0.92], fluency (as measured by a decrease in response time (from 10 to 6 s; P < 0 0.001; η2 p = 0.69) and self-perceived confidence (2.5 to 4.3; P < 0 0.001, η2 p = 0.86) with subsequent PLMs and course duration. There was a diversification of recognized features, an increase in pattern recognition, and better attention to localization and contextual association. Based on student feedback, the PLMs functioned well online, and enhanced motivation and learning. PLMs increased diagnostic accuracy, had a positive effect on learning outcomes and were easily integrated alongside clinical teaching. Considering the current era of digital technologies, we believe that there is potential for wider use of PLMs to improve visual skills and strengthen implicit learning in dermatology.

A Novel Deep Convolutional Neural Network for Diagnosis of Skin Disease

Due to its intricacy, dermatology presents the most challenging and uncertain terrain for diagnosis. Skin conditions like Carcinoma and Melanoma are frequently very challenging to identify in the early stages and are much more challenging to define independently. The use of pattern recognition models to automate detection has been studied by a number of writers. This research describes a novel Deep Convolutional Neural Network (DCNN) for Skin Disease Detection. The photographs of skin would undergo processing to remove unwanted noise as well as to improve the photos. The performance of classification will be greatly impacted by the pixel values of a picture. The picture is classified using the softmax classifier method by feature extraction utilising DCCN, and a diagnosis report is produced as the result. In comparison to more classic approaches like KNN (K-Nearest Neighbour) and CNN, this methodology will provide results faster and with improved accuracy, precision, and recall. With a detection time of 10,000 milliseconds, DCNN achieved accuracy, precision, and recall percentages of 98.4%, 96.3%, and 97.2%, respectively.

Skin disease detection using deep learning

A complex topic such as dermatology makes it one of the most unexpected and challenging professions to diagnose due to the complexities of the subject matter involved. According to dermatology, it is a regular practice to do extensive tests on patients to ascertain the kind of skin illness they have been afflicted duration of time varies from one practitioner to the next, depending on their experience. It is also influenced by the individual's personal experience with the subject matter. Using a technique not restricted by these limits is essential to diagnose skin diseases without these limitations. This work provides an automated image-based method for diagnosing and categorizing skin problems that use machine learning classification. Computational approaches will be used to analyze, process, and relegate picture data to consider the many different characteristics of the photos that are being processed. Skin photographs are first filtered to remove undesirable noise from the image and then processed to enhance the picture's overall quality. It is possible to extract features from an image using advanced techniques such as Convolutional Neural Network (CNN), classify the picture using the softmax classifier's algorithm, and provide a diagnostic report as an output. With more accuracy and faster delivery of results than the previous technique, this application will be a more efficient and reliable system for dermatological illness diagnosis than the conventional method. Furthermore, this may be a reliable real-time teaching tool for medical students enrolled in the dermatology stream at a university studying dermatology.

Skin line and subcutaneous fat in radiology: Opinion

The aim of this paper is to shed light on a neglected topic in radiology which is imaging the skin and subcutaneous fat. A few studies showed that imaging of skin line and subcutaneous fat have clinical benefits for health, cosmetic reasons, and overall wellbeing. This paper will list many issues that can be diagnosed when evaluating a skin line, fold, and subcutaneous fat on different imaging modalities like; X-ray, ultrasound, CT, MRI, etc. Furthermore, this paper will show a comparison of those imaging modalities in terms of measurement accuracy, diagnosis of skin and subcutaneous fat diseases, comparison of males and females in terms of fat buildup, and so on. According to the available literature, men have more fat than women in the inner deep layer, but women have more body fat than men. Furthermore, as previously hypothesized and proven wrong, recent paper shows that there is no water in the adipose tissue. In addition, a hierarchy in evaluating skin folds and subcutaneous fat by using imaging modalities is seen in the following order from low accuracy to high accuracy; caliper, X-ray, ultrasound, CT, etc.

Learning from dermoscopic images in association with clinical metadata for skin lesion segmentation and classification

Automatic segmentation and classification of lesions are two clinically significant tasks in the computer-aided diagnosis of skin diseases. Both tasks are challenging due to the nonnegligible lesion differences in dermoscopic images from different patients. In this paper, we propose a novel pipeline to efficiently implement skin lesions’ segmentation and classification tasks, which consists of a segmentation network and a classification network. To improve the performance of the segmentation network, we propose a novel module of Multi-Scale Holistic Feature Exploration (MSH) to thoroughly exploit perceptual clues latent among multi-scale feature maps as synthesized by the decoder. The MSH module enables holistic exploration of features across multiple scales to more effectively support downstream image analysis tasks. To boost the performance of the classification network, we propose a novel module of Cross-Modality Collaborative Feature Exploration (CMC) to discover latent discriminative features by collaboratively exploiting potential relationships between cross-modal features of dermoscopic images and clinical metadata. The CMC module enables dynamically capturing versatile interaction effects among cross-modal features during the model’s representation learning procedure by discriminatively and adaptively learning the interaction weight associated with each crossmodality feature pair. In addition, to effectively reduce background noise and boost the lesion discrimination ability of the classification network, we crop the images based on lesion masks generated by the best segmentation model. We evaluate the proposed pipeline on the four public skin lesion datasets, where the ISIC 2018 and PH2 are for segmentation, and the ISIC 2019 and ISIC 2020 are combined into a new dataset, ISIC 2019&2020, for classification. It achieves a Jaccard index of 83.31% and 90.14% in skin lesion segmentation, an AUC of 97.98% and an Accuracy of 92.63% in skin lesion classification, which is superior to the performance of representative state-of-the-art skin lesion segmentation and classification methods. Last but not least, the new model for segmentation utilizes much fewer model parameters (3.3 M) than its peer approaches, leading to a greatly reduced number of labeled samples required for model training, which obtains substantially stronger robustness than its peers.

Cytokine/Chemokine assessment as a complementary diagnostic tool for inflammatory skin diseases.

Inflammatory skin conditions are the 4th leading cause of non-fatal health burden in the general population worldwide. The diagnosis of skin lesions due to systemic drug reactions, viral or bacterial exanthems, or in patients with psoriasis, atopic dermatitis or contact dermatitis is often difficult and relies heavily upon conventional histopathologic examination. Conversely, it is widely accepted that the cutaneous profile of inflammatory markers, or 'inflammatory signature', is differentially expressed in various skin conditions. In this pilot study, we investigated the possibility of inflammatory skin disease diagnosis from an immunological perspective in small punch biopsies. We collected lesional and perilesional punch biopsies from 139 patients suffering from a variety of inflammatory skin conditions and attending the Dermatology Department at the Princess Alexandra Hospital in Brisbane, Australia. Using bead-based immunoassays we were able to measure 13 out of 17 inflammatory markers from a pre-selected multi-analyte panel and to detect significant differences between lesional and perilesional biopsies from each individual patient. Hierarchical and unbiased clustering methods based on inflammatory signatures grouped psoriasis and atopic dermatitis lesions into individual clusters in contrast to other skin conditions, highlighting the potential of inflammatory signatures to be used as diagnostic differentiators and to inform alternative targets in anti-inflammatory treatment strategies.