Detection Of Skin Diseases Research Articles

OESV-KRF: Optimal ensemble support vector kernel random forest based early detection and classification of skin diseases

The most widespread disease that often puts the patient’s life in danger is skin disease. Avoiding this situation, it necessitates the development of efficient and reliable skin cancer prediction techniques. Besides, regular screening of suspicious lesions in the skin helps the dermatologist to detect skin cancer in the early phase. Although there exist numerous Machine Learning methods to detect skin cancer in its early stage, the accurate and timely prediction and classification of skin lesions continued to be demanding. To fulfill this aim, in this paper, we proposed an ensemble support vector kernel random forest-based hybrid equilibrium Aquila optimization (ESVMKRF-HEAO) approach. The proposed prediction model is examined using the HAM10000 dataset which contains numerous varieties of skin lesion images in it. Initially, the intrusions and noises in the dataset are removed, and image qualities are enhanced using preprocessing pipelines. Then, the thresholding-based segmentation technique is utilized to segment the cancerous lesion regions from the healthy background. Finally, the proposed classifier accurately predicts and classifies the segmented images based on their feature characteristics into five distinct categories namely melanocytic nevus, basal cell carcinoma, melanoma, actinic keratosis and dermatofibroma. The simulation of the proposed model is performed using MATLAB 2019a software. The evaluation of the proposed ESVMKRF-HEAO approach performance is done concerning metrics such as sensitivity, f-1 score, accuracy, precision and specificity. The proposed ESVMKRF-HEAO approach attained a greater performance rate with respect to all metrics especially, the prediction accuracy of about 97.4% in the experimental results.

Skin Disease Identification using Image Processing

Skin diseases are very dangerous and contagious. Dermatosis is the most common among other conditions caused by things like fungal infections, bacteria, allergies, or viruses. May cause. Dermatosis is diseases that affect the skin. Skin diseases are diseases that only a dermatologist can cure, while some skin diseases cannot be cured by proper medications. Skin diseases can also cause rashes, inflammation, itching, or other skin changes. The objective of this research is to detect skin diseases through image processing. In this process, we take an image of the infected part of the image and after analyzing the image using image processing techniques, we get an output of what type of disease it is and we use CNN for real-time detection of skin diseases.

Skin Disease Identification using Image Processing

Skin diseases are very dangerous and infectious. Skin disease is most common than other diseases that can be caused by fungal infections, bacteria, allergies or viruses, etc. Skin diseases are conditions that affect your skin. skin diseases are the diseases that can be cured Only by dermatologist and some this skin diseases cannot be a cured by proper medication. Also skin diseases can cause rashes inflammation itching or any other skin changes. the purpose of this research to detect skin disease using image processing. In this process we take the image of the infected part of image and after image analysis using image processing techniques we get the output of which type of disease it is in this we use CNN for real time skin disease detection.

Deep-learning-based super-resolution and classification framework for skin disease detection applications

Deep-learning-based super-resolution and classification framework for skin disease detection applications

Detection and Classification of Dog Skin Disease using Deep Learning

Dogs are beloved pets and loyal companions to millions of people worldwide. Unfortunately, dogs can also suffer from a variety of skin diseases that can cause discomfort, pain, and even life-threatening complications. Some dog skin diseases can be transmitted to humans through direct contact. Early detection and treatment of these skin diseases are crucial for the health and well-being of dogs and humans. Our project aims to provide a quick and precise approach to identifying various types of skin diseases in dogs. To expedite the process of identifying and diagnosing infections related to canine interaction, we plan to utilize a machine-learning model. This approach aims to reduce the time and expertise required for accurate and consistent diagnosis, which can otherwise be challenging and time-consuming. Two models, InceptionV3 and MobileNetV2, were utilized and compared in our implementation. In the case of InceptionV3, a training accuracy of 0.99 and a validation accuracy of 0.98 were achieved. For MobileNetV2, we attained a validation accuracy of 96 and a categorical accuracy of 97. Key Words: Dermatophytosis, zoonosis, image classification, deep learning, Transfer learning, InceptionV3, MobileNetV2, CNN, DNN.

Skin Disease Detection Using Machine Learning

Abstract: Particularly dangerous and frequently contagious include melanoma, eczema, and impetigo. When found early enough, some skin conditions are curable. The main issue with it is that only a skilled dermatologist can identify and categorise such diseases. In several cases, medical professionals appropriately categorise the illness and consequently give the patient the wrong drugs. Navies Bias Classifier has been contrasted. Real-time testing findings are displayed. Since our solution is mobilebased, it can be used anywhere. In order for the programme to function, the patient must supply an image of the affected area. It is processed using deep learning techniques and produces the most accurate results. In this essay, we compare two distinct real-time skin disease detection algorithms based on accuracy in this presentation.

Facial Skin Disease Detection using Image Processing

Busy lifestyle, modernization, increasing pollution and unhealthy diet have led to problems which people are neglecting. Not drinking enough water, stress and hormonal changes are causing problems to skin. Causes may be situational or genetic. Few skin conditions are minor while others can be life-threatening. The skin is the largest organ of the body and is composed of water, proteins, fats and minerals. Problems appear on outer layer of the skin that is epidermis. Skin diseases are considered to be the fourth most common cause of human illness. Skin diseases are observed to increase with age and were seen frequently in both men and women. Skin disorders can be temporary or permanent. Skin diseases have an impact on individual, family and social life caused by inadequate self-treatment which may also induce psychological problems. In recent years, use of computer technologies is becoming practically universal for both personal and professional issues. Facial skin problem identification and recognition has evolved to a great extent over the years. Detection of skin diseases is done using Convolution Neural Network (CNN) and image processing methods. CNN yields better performance in terms of accuracy, precision and results than the existing conventional methods. Image processing uses digital computer to process the images through an algorithm. We focus on features like skin tone, skin texture and color. We present a brief review about various facial skin problems providing more insight about the effective models and algorithms used.

Federated Machine Learning for Detection of Skin Diseases and Enhancement of Internet of Medical Things (IoMT) Security.

Human skin disease, the most infectious dermatological ailment globally, is initially diagnosed by sight. Some clinical screening and dermoscopic analysis of skin biopsies and scrapings for accurate classification are medically compulsory. Classification of skin diseases using medical images is more challenging because of the complex formation and variant colors of the disease and data security concerns. Both the Convolution Neural Network (CNN) for classification and a federated learning approach for data privacy preservation show significant performance in the realm of medical imaging fields. In this paper, a custom image dataset was prepared with four classes of skin disease, a CNN model was suggested and compared with several benchmark CNN algorithms, and an experiment was carried out to ensure data privacy using a federated learning approach. An image augmentation strategy was followed to enlarge the dataset and make the model more general. The proposed model achieved a precision of 86%, 43%, and 60%, and a recall of 67%, 60%, and 60% for acne, eczema, and psoriasis. In the federated learning approach, after distributing the dataset among 1000, 1500, 2000, and 2500 clients, the model showed an average accuracy of 81.21%, 86.57%, 91.15%, and 94.15%. The CNN-based skin disease classification merged with the federated learning approach is a breathtaking concept to classify human skin diseases while ensuring data security.

Attitudes Towards Artificial Intelligence Among Dermatologists Working in Saudi Arabia.

Artificial intelligence (AI) and its applications are among the most discussed modern technologies today. Despite the rapidly expanding use of AI in medicine, and specifically in dermatology, only a few studies have studied the attitude of physicians toward AI. To recognize the attitudes towards AI among dermatologists in the Kingdom of Saudi Arabia. A cross-sectional survey was done among dermatologists in Saudi Arabia. Questionnaires were distributed through several online channels. Overall, 103 dermatologists filled out the survey. The majority saw very strong or strong potential for AI in the automated detection of skin diseases based on dermatological clinical images (50.9%), dermoscopic images (66.6%) and within dermatopathology (66.6%). In regard to results of attitudes towards AI, 56.6% and 52. 8% agreed that AI will revolutionize medicine and dermatology, respectively. However, many of the respondents disagreed that AI will replace physicians (41.5%) and human dermatologists (39.6%) in the future. Age did not impact the overall attitude of dermatologists. Dermatologists in Saudi Arabia showed an optimistic attitude towards AI in dermatology and medicine. However, dermatologists believe that AI will not replace humans in the future.

Implementasi Sistem Pakar Mendiagnosa Penyakit Kulit Akibat Jamur Pada Manusia Berbasis Web Menggunakan Metode Forward Chaining Dan Naive Bayes

In general, fungal skin disease is not a deadly disease. Most people who have been infected often leave it alone and think that the disease will heal on its own. However, if left untreated or without prompt treatment, the impact can worsen the patient's condition, one of which is permanent baldness if the fungal skin disease infects the scalp. The importance of early detection and treatment of fungal skin diseases is essential. While the current process requires a person to go to a hospital or a skin and genital specialist doctor, so they have to spend time and money to do early detection and treatment, the availability of expert doctors or experts who know a particular field is quite limited. At the same time, many patients must be diagnosed with the disease immediately and treated immediately. In a clinic, medical officers often experience difficulties when receiving patients who complain of skin diseases caused by fungi.

FACES: A Deep-Learning-Based Parametric Model to Improve Rosacea Diagnoses.

Rosacea is a chronic inflammatory skin disorder that causes visible blood vessels and redness on the nose, chin, cheeks, and forehead. However, visual assessment, the current standard method used to identify rosacea, is often subjective among clinicians and results in high variation. Recent advances in artificial intelligence have allowed for the effective detection of various skin diseases with high accuracy and consistency. In this study, we develop a new methodology, coined "five accurate CNNs-based evaluation system (FACES)", to identify and classify rosacea more efficiently. First, 19 CNN-based models that have been widely used for image classification were trained and tested via training and validation data sets. Next, the five best performing models were selected based on accuracy, which served as a weight value for FACES. At the same time, we also applied a majority rule to five selected models to detect rosacea. The results exhibited that the performance of FACES was superior to that of the five individual CNN-based models and the majority rule in terms of accuracy, sensitivity, specificity, and precision. In particular, the accuracy and sensitivity of FACES were the highest, and the specificity and precision were higher than most of the individual models. To improve the performance of our system, future studies must consider patient details, such as age, gender, and race, and perform comparison tests between our model system and clinicians.

Skin Disease Detection Based on Deep Learning

Among the most prevalent disorders is skin infection. Clinical manifestations are difficult to classify because to various diverse classifications, the similarity of various clinical signs, and indeed the higher than anticipated of problem tissues. Additionally, in the context of sparse data, a singular trustworthy CNN Algorithm has poor adaptation, inadequate semantic segmentation, and poor challenges stem. To classify skin diseases via prototype merging, we are using a learning algorithm throughout this research. The algorithm ability to retrieve data was improved though modelling synthesis, superficial and convolutional blending, and indeed the addition of an awareness component. Additionally, a number of tasks are carried out to improve the classifying ability of the algorithm, including prototype which was before, subsampling, and parameters wonderful. The subject's ability to retrieve data was improved through modelling synthesis, superficial and feature space blending, and the addition of such an attentiveness module. Additionally, a number of tasks performed carried out to improve the categorization ability of the algorithm, including prototype which was before, feature extraction, and parameters exquisite. The research outcomes demonstrated that their suggested model beat the existing Kaiser normalization of DenseNet201 and Convent L by 4.42% and 3.66%, correspondingly, whilst operating on our proprietary datasets occupied by pimple skin disorders. The suggested approach performed well when comparing to other cutting-edge approaches, exhibiting reliability and f1-scores of 90.85% and 89.99%, correspondingly.

An XNOR-ResNet and spatial pyramid pooling-based YOLO v3-tiny algorithm for Monkeypox and similar skin disease detection

ABSTRACT This work focus on the detection of Monkeypox disease which has been declared a medical emergency by the World Health Organization. To carry out this work, a richly annotated dataset in YOLO and PASCAL VOC format has been created covering similar skin diseases such as, Monkeypox, Chickenpox, and Measles and further training and tests have been performed using deep learning-based algorithms. As an effort to propose a high-performing algorithm, an improved variant of the YOLO v3-tiny algorithm has been developed. The improvements in YOLO v3-tiny algorithm have been incorporated by employing the residual network (ResNet) and Spatial Pyramid Pooling (SPP) network. Furthermore, to reduce the computation cost of the developed algorithm, XNOR layers have been applied to the residual network. The proposed algorithm achieved a Mean Average Precision value of 76.51% which was 4.54–48.88% higher compared to other tested baseline algorithms.

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

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%

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.

Attention Cost-Sensitive Deep Learning-Based Approach for Skin Cancer Detection and Classification

Deep learning-based models have been employed for the detection and classification of skin diseases through medical imaging. However, deep learning-based models are not effective for rare skin disease detection and classification. This is mainly due to the reason that rare skin disease has very a smaller number of data samples. Thus, the dataset will be highly imbalanced, and due to the bias in learning, most of the models give better performances. The deep learning models are not effective in detecting the affected tiny portions of skin disease in the overall regions of the image. This paper presents an attention-cost-sensitive deep learning-based feature fusion ensemble meta-classifier approach for skin cancer detection and classification. Cost weights are included in the deep learning models to handle the data imbalance during training. To effectively learn the optimal features from the affected tiny portions of skin image samples, attention is integrated into the deep learning models. The features from the finetuned models are extracted and the dimensionality of the features was further reduced by using a kernel-based principal component (KPCA) analysis. The reduced features of the deep learning-based finetuned models are fused and passed into ensemble meta-classifiers for skin disease detection and classification. The ensemble meta-classifier is a two-stage model. The first stage performs the prediction of skin disease and the second stage performs the classification by considering the prediction of the first stage as features. Detailed analysis of the proposed approach is demonstrated for both skin disease detection and skin disease classification. The proposed approach demonstrated an accuracy of 99% on skin disease detection and 99% on skin disease classification. In all the experimental settings, the proposed approach outperformed the existing methods and demonstrated a performance improvement of 4% accuracy for skin disease detection and 9% accuracy for skin disease classification. The proposed approach can be used as a computer-aided diagnosis (CAD) tool for the early diagnosis of skin cancer detection and classification in healthcare and medical environments. The tool can accurately detect skin diseases and classify the skin disease into their skin disease family.

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.