Detection Of Skin Diseases Research Articles

Detection and molecular characterization of lumpy skin disease and bovine papular stomatitis viruses in lumpy skin disease-suspected outbreaks in Tanzania.

Lumpy Skin Disease (LSD) is endemic in sub-Saharan countries and is currently a global threat to the cattle industry. Information on the circulating Capripoxvirus lumpyskinpox, formerly known as Lumpy Skin Disease Virus (LSDV), and other poxviruses infecting cattle is very scant in Tanzania. The current study aimed to confirm and characterize LSDV and other poxviruses infecting cattle, from LSD suspected outbreaks in Tanzania. A total of 24 samples were collected from four LSD suspected outbreaks reported in Tanzania between February and May 2023. Samples were screened for LSDV genome by real-time PCR and then subjected to a high-resolution multiplex melting (HRM) assay where 10 samples were positive for Capripoxvirus (CaPV) and one sample was Parapoxvirus (PPV) positive. Four LSDV genes; RPO30, GPCR, EEV glycoprotein and B22R and the partial B2L gene of PPVs were analyzed. All targeted LSDV genes from the Tanzanian isolates showed 100% similarity and isolates clustered with commonly circulating LSDV field isolates. Furthermore, the single nucleotide polymorphism (SNP) at position 240 (A-> G) of the EEV gene differentiates the Tanzanian LSDVs from the group of ancient Kenyan LSDV isolates while the B22R sequences of the Tanzanian LSDV isolates differed from the LSDV Neethling and LSDV KSGP-0240 derived vaccines. Sequence analysis of the partial B2L gene of the Tanzanian parapoxvirus bovinestomatitis, formerly known as Bovine papular stomatitis virus (BPSV) showed a different BPSV strain circulating compared to publicly available sequences. These findings confirm the presence of LSDV in Tanzania, which suggesting the need for establishing an effective control program and continuous monitoring. The presence of a typical profile for Tanzania BPSV is an indication that, although never reported before, BPSV is established in the country therefore this virus should be included in the differential diagnosis of LSDV.

Early Detection of Lumpy Skin Disease in Cattle Using Deep Learning-A Comparative Analysis of Pretrained Models.

Lumpy Skin Disease (LSD) poses a significant threat to agricultural economies, particularly in livestock-dependent countries like India, due to its high transmission rate leading to severe morbidity and mortality among cattle. This underscores the urgent need for early and accurate detection to effectively manage and mitigate outbreaks. Leveraging advancements in computer vision and artificial intelligence, our research develops an automated system for LSD detection in cattle using deep learning techniques. We utilized two publicly available datasets comprising images of healthy cattle and those with LSD, including additional images of cattle affected by other diseases to enhance specificity and ensure the model detects LSD specifically rather than general illness signs. Our methodology involved preprocessing the images, applying data augmentation, and balancing the datasets to improve model generalizability. We evaluated over ten pretrained deep learning models-Xception, VGG16, VGG19, ResNet152V2, InceptionV3, MobileNetV2, DenseNet201, NASNetMobile, NASNetLarge, and EfficientNetV2S-using transfer learning. The models were rigorously trained and tested under diverse conditions, with performance assessed using metrics such as accuracy, sensitivity, specificity, precision, F1-score, and AUC-ROC. Notably, VGG16 and MobileNetV2 emerged as the most effective, achieving accuracies of 96.07% and 96.39%, sensitivities of 93.75% and 98.57%, and specificities of 97.14% and 94.59%, respectively. Our study critically highlights the strengths and limitations of each model, demonstrating that while high accuracy is achievable, sensitivity and specificity are crucial for clinical applicability. By meticulously detailing the performance characteristics and including images of cattle with other diseases, we ensured the robustness and reliability of the models. This comprehensive comparative analysis enriches our understanding of deep learning applications in veterinary diagnostics and makes a substantial contribution to the field of automated disease recognition in livestock farming. Our findings suggest that adopting such AI-driven diagnostic tools can enhance the early detection and control of LSD, ultimately benefiting animal health and the agricultural economy.

Design and implementation of an IoT-based monitoring system for early detection of lumpy skin disease in cattle

Livestock farming serves a crucial role in global food security and sustainability, yet infections like Lumpy Skin Disease (LSD) present huge difficulties. LSD reduces the marketability and productivity of cattle, but it is not fatal. Current preventive measures that rely on routine veterinary testing can be time-consuming and miss early signs of infection, requiring a more effective management strategy first works This article presents an IoT-enabled intelligent system to prevent LSD in animals. The system uses a smart wearable sensor device to collect real-time health information including body temperature, heart rate and 3-axis motion data. Advanced analysis of data is considered to identify early diagnosis of potential LSD infection results based on deviations from normal parameters, this data prevents further infection within livestock by supporting immediate isolation. On a cloud-based online platform, and integrated into the mobile application provides a comprehensive experience and timely alerts to provide potential health issues. Furthermore, the IoT features of the system enable farmers and veterinarians to quickly guide and optimize disease control strategies, improving biosecurity protocols and overall livestock health management. This creative approach represents a significant improvement over traditional methods, providing a cost-effective, accurate and real-time solution for animal health monitoring and management.

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.

Skin cancer classification using non-local means denoising and sparse dictionary learning based CNN

Skin conditions are becoming increasingly prevalent across the world in current times. With the rise in dermatological disorders, there is a need for computerized techniques that are completely noninvasive to patients’ skin. As a result, deep learning models have become standard for the computerized detection of skin diseases. The performance efficiency of these models improves with access to more data with their primary aim being image classification. In this paper, we present a skin disease detection methodology using image processing techniques, non-local means denoising and convolutional neural network (CNN) backed by sparse dictionary learning. Here, the major benefit of using NLM denoising followed by sparse dictionary learning with CNNs in image classification lies in leveraging a multi-stage approach that enhances the quality of input data, extracts meaningful and discriminative features, and improves the overall performance of the classification model. This combined approach addresses challenges such as noise robustness, feature extraction, and classification accuracy, making it particularly effective in complex image analysis tasks. For denoising, the average Peak Signal to Noise Ratio (PSNR) obtained for images from HAM-10000 dataset is 33.59 dB. For the ISIC-2019 dataset, the average PSNR for the train folder is 34.37 dB, and for the test folder it is 34.39 dB. The deep learning network is trained for the analysis of skin cancer images using a CNN model and is achieving acceptable results in classifying skin cancer types. The datasets used contain high-resolution images. After all the tests, the accuracy obtained is 85.61% for the HAM-10000 dataset and 81.23% for the ISIC-2019 dataset, which is on par with existing approaches validated by benchmarking results.

Integrating Multi‐Scale Feature Boundary Module and Feature Fusion With CNN for Accurate Skin Cancer Segmentation and Classification

ABSTRACTThe skin, a crucial organ, plays a protective role in the human body, emphasizing the significance of early detection of skin diseases to prevent potential progression to skin cancer. The challenge lies in diagnosing these diseases at their early stages, where visual resemblance complicates differentiation, highlighting the need for an innovative automated method for precisely identifying skin lesions in biomedical images. This paper introduces a holistic methodology that combines DenseNet, multi‐scale feature boundary module (MFBM), and feature fusion and decoding engine (FFDE) to tackle challenges in existing deep‐learning image segmentation methods. Furthermore, a convolutional neural network model is designed for the classification of segmented images. The DenseNet encoder efficiently extracts features at four resolution levels, leveraging dense connectivity to capture intricate hierarchical features. The proposed MFBM plays a crucial role in extracting boundary information, employing parallel dilated convolutions with various dilation rates for effective multi‐scale information capture. To overcome potential disadvantages related to the conversion of features during segmentation, our approach ensures the preservation of context features. The proposed FFDE method adaptively fuses features from different levels, restoring skin lesion location information while preserving local details. The evaluation of the model is conducted on the HAM10000 dataset, which consists of 10 015 dermoscopy images, yielding promising results.

MLFF-Net: a multi-model late feature fusion network for skin disease classification

Early diagnosis is paramount to preventing skin diseases and reducing mortality, given their global prevalence. Visual detection by experts using dermoscopy images has become the gold standard for detecting skin cancer. However, a significant challenge in skin cancer detection and classification lies in the similarity of appearance among skin disease lesions and the complexity of dermoscopic images. In response, we developed multi-model late feature fusion network (MLFF-Net), a multi-model late feature fusion network tailored for skin disease detection. Our approach begins with image pre-processing techniques to enhance image quality. We then employ a two-stream network comprising an enhanced densely linked network (DenseNet-121) and a vision transformer (ViTb16). We leverage shallow and deep feature fusion, late fusion, and an attention module to enhance the model’s feature extraction efficiency. The subsequent feature fusion module constructs multi-receptive fields to capture disease information across various scales and uses generalized mean pooling (GeM) pooling to reduce the spatial dimensions of lesion characteristics. Finally, we implement and test our skin lesion categorization model, demonstrating its effectiveness. Despite the combination, convolutional neural network (CNN) outperforms ViT approaches, with our model enhancing the accuracy of the best model by 6.1%.

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.

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.

Interpretable machine learning for dermatological disease detection: Bridging the gap between accuracy and explainability

Research on disease detection by leveraging machine learning techniques has been under significant focus. The use of machine learning techniques is important to detect critical diseases promptly and provide the appropriate treatment. Disease detection is a vital and sensitive task and while machine learning models may provide a robust solution, they can come across as complex and unintuitive. Therefore, it is important to gauge a better understanding of the predictions and trust the results. This paper takes up the crucial task of skin disease detection and introduces a hybrid machine learning model combining SVM and XGBoost for the detection task. The proposed model outperformed the existing machine learning models — Support Vector Machine (SVM), decision tree, and XGBoost with an accuracy of 99.26%. The increased accuracy is essential for detecting skin disease due to the similarity in the symptoms which make it challenging to differentiate between the different conditions. In order to foster trust and gain insights into the results we turn to the promising field of Explainable Artificial Intelligence (XAI). We explore two such frameworks for local as well as global explanations for these machine learning models namely, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME).

Lumpy Skin Disease Detection in Cattle by a Robust Approach using Advanced Convolutional Neural Networks

Background: Lumpy skin disease (LSD) is a significant health concern for cattle globally and poses economic threats by affecting various aspects of cattle health. Integrating artificial intelligence (AI) and machine learning (ML) with visual inspections and biosensor data has shown promise in enhancing disease detection and diagnosis. The present study harnesses the potential of Convolutional Neural Networks (CNN) and image processing for detecting LSD. Methods: Using images from the agricultural landscape, this study highlights the significance of convolutional neural networks that identify the lumpy skin disease (LSD) in animals. Images are categorized into two groups: LSD (infected skin) and non-LSD (normal skin). This is achieved by applying a deeply designed deep learning model carefully built to fulfill this particular need. Evaluation metrics assess the model’s performance, including accuracy, loss and a confusion matrix. Result: A CNN-based model trained for 50 epochs to classify skin conditions, achieved an 86.54% accuracy. The study underscores the potential of CNN in early LSD detection, paving the way for practical applications in veterinary medicine. Future work involves addressing dataset limitations, refining model parameters, reducing image noise, exploring different feature extraction methods and investigating additional animal skin conditions.

Detection of Disease in Calves using Artificial Intelligence

Background: Livestock farming is experiencing a digital transformation and is becoming more information-driven. However, this type of data is often kept in separate storage towers, making it incapable of practicing its potential to boost animal welfare. Lumpy Skin Disease (LSD) is a serious threat to the health of cattle worldwide and has caused financial problems for many cattle farming enterprises. It has been shown that combining machine learning (ML) and artificial intelligence (AI) with biosensor data and conventional visual inspections can improve the identification and diagnosis of this serious condition. Methods: This study presents an extremely precise livestock farming framework that combines data streams from a wide array of disciplines of dairy cattle to see if ever wider/vast data sources enhance the overall projections for diseases and if using the more complicated prediction models can reimburse for less diverse data to some extent. Using images from the farming landscape, this study highlights the utility of convolutional neural networks (CNNs) in the identification of the Lumpy Skin Disease Virus (LSDV) in animals. Result: An analysis of the relative weight given to individual factors in predicting accuracy shows that disease in dairy cattle results from the intricate interactions between many life domains/parameters, such as housing, nutrition and climate; that prediction performance is enhanced by incorporating a wider range of data sources; and that current information can be repurposed to produce useful information for vaccine development. The study highlights the potential of data-driven dairy interventions, focusing on artificial intelligence for disease prediction in cattle, to improve animal welfare and reduce the risk of disease. A Convolutional Neural Network (CNN) based model effectively classified skin conditions with an overall accuracy of 73.89% after 27 training epochs. This study demonstrates CNN’s useful applications in the field of veterinary medicine by highlighting its potential for early detection of Lumpy Skin Disease (LSD).

Integration of Localized, Contextual, and Hierarchical Features in Deep Learning for Improved Skin Lesion Classification.

Skin lesion classification is vital for the early detection and diagnosis of skin diseases, facilitating timely intervention and treatment. However, existing classification methods face challenges in managing complex information and long-range dependencies in dermoscopic images. Therefore, this research aims to enhance the feature representation by incorporating local, global, and hierarchical features to improve the performance of skin lesion classification. We introduce a novel dual-track deep learning (DL) model in this research for skin lesion classification. The first track utilizes a modified Densenet-169 architecture that incorporates a Coordinate Attention Module (CoAM). The second track employs a customized convolutional neural network (CNN) comprising a Feature Pyramid Network (FPN) and Global Context Network (GCN) to capture multiscale features and global contextual information. The local features from the first track and the global features from second track are used for precise localization and modeling of the long-range dependencies. By leveraging these architectural advancements within the DenseNet framework, the proposed neural network achieved better performance compared to previous approaches. The network was trained and validated using the HAM10000 dataset, achieving a classification accuracy of 93.2%.

Two-step hierarchical binary classification of cancerous skin lesions using transfer learning and the random forest algorithm

Skin lesion classification plays a crucial role in the early detection and diagnosis of various skin conditions. Recent advances in computer-aided diagnostic techniques have been instrumental in timely intervention, thereby improving patient outcomes, particularly in rural communities lacking specialized expertise. Despite the widespread adoption of convolutional neural networks (CNNs) in skin disease detection, their effectiveness has been hindered by the limited size and data imbalance of publicly accessible skin lesion datasets. In this context, a two-step hierarchical binary classification approach is proposed utilizing hybrid machine and deep learning (DL) techniques. Experiments conducted on the International Skin Imaging Collaboration (ISIC 2017) dataset demonstrate the effectiveness of the hierarchical approach in handling large class imbalances. Specifically, employing DenseNet121 (DNET) as a feature extractor and random forest (RF) as a classifier yielded the most promising results, achieving a balanced multiclass accuracy (BMA) of 91.07% compared to the pure deep-learning model (end-to-end DNET) with a BMA of 88.66%. The RF ensemble exhibited significantly greater efficiency than other machine-learning classifiers in aiding DL to address the challenge of learning with limited data. Furthermore, the implemented predictive hybrid hierarchical model demonstrated enhanced performance while significantly reducing computational time, indicating its potential efficiency in real-world applications for the classification of skin lesions.

Skin Disease Detection Model

Abstract: The human skin is a remarkable organ susceptible to a myriad of know and unknown diseases. Many of these ailment are widespread, with some ranking among common worldwide. The complexity of diagnosing these diseases is compounded by challenges such as variations in skin texture, the presence of hair, and diverse skin colors. In some areas have limited access to medical facilities, individuals often neglect early symptoms, leading to exacerbated conditions over time. Furthermore, traditional diagnostic methods for skin diseases are time consuming. To address these challenges, there is a critical need to develop advanced diagnostic methods utilizing machine learning techniques to enhance accuracy cross various skin diseases. Machine learning algorithms have proven valuable in medical applications, leveraging image feature values to facilitate decision making. The diagnostic process involves three key stages: feature extraction, training, and testing. By employing machine learning technology, these algorithms learn from a diverse set of skin images o enhance their diagnostic capabilities. The primary goal is to significantly improved the accuracy of skin disease detection. This study focuses on utilizing color and texture features for the classification of skin diseases. The distinctive color of healthy skin differs from that affected by disease, while texture features effectively discern smoothness, coarseness, and regularity in images. Key features such as texture, color, and shape phyla pivotal role in image classification. The incorporation of convolution neural networks (CNN) further augments the capabilities of image classification in the realm of skin disease diagnosis

Skin Disease Detection and Remedial System

With the advent of smart processing systems in computers it has driven the emergence of inventive solutions within health-care. One notable instance is the Skin Disease Detection and Recommendation System, utilizing AI and machine learning methods to elevate dermatological diagnosis and treatment guidance. This summary offers a comprehensive overview of the Skin Disease Detection System, outlining its core elements, methodologies, advantages, and potential healthcare impact. The System for Skin Disease Detection aims to transform dermatology by automating the skin disease identification process and delivering customized treatment suggestions. This also aims to detect the skin types and suggest remedial medication and other things for the same. Consulting with a dermatologist is also easy by this. Employing image processing, pattern recognition, and deep learning algorithms, this system accurately evaluates skin condition images. The solution's application was developed using Streamlit, Python, PHP, Bootstrap, and MySQL.

Skin Disease Detection

The human skin is a remarkable organ susceptible to a myriad of know and unknown diseases. Many of these ailment are widespread, with some ranking among common worldwide. The complexity of diagnosing these diseases is compounded by challenges such as variations in skin texture, the presence of hair, and diverse skin colors. In some areas have limited access to medical facilities, individuals often neglect early symptoms, leading to exacerbated conditions over time. Furthermore, traditional diagnostic methods for skin diseases are time-consuming. To address these challenges, there is a critical need to develop advanced diagnostic methods utilizing machine learning techniques to enhance accuracy cross various skin diseases. Machine learning algorithms have proven valuable in medical applications, leveraging image feature values to facilitate decision-making. The diagnostic process involves three key stages: feature extraction, training, and testing. By employing machine learning technology, these algorithms learn from a diverse set of skin images o enhance their diagnostic capabilities. The primary goal is to significantly improved the accuracy of skin disease detection. This study focuses on utilizing color and texture features for the classification of skin diseases. The distinctive color of healthy skin differs from that affected by disease, while texture features effectively discern smoothness, coarseness, and regularity in images. Key features such as texture, color, and shape phyla pivotal role in image classification. The incorporation of convolution neural networks (CNN) further augments the capabilities of image classification in the realm of skin disease diagnosis

A Method of Skin Disease Detection Using Image Processing and Machine Learning

Skin conditions are prevalent health issues globally. The dangers posed by these infections are unseen, leading to physical discomfort and mental anguish. In severe cases, skin diseases can even progress to skin cancer. Consequently, the identification of skin diseases from clinical images remains a significant challenge in medical image analysis. Furthermore, manual diagnosis by medical professionals is time-consuming and subjective. Therefore, there is a need for automated skin disease prediction to expedite treatment planning for both patients and dermatologists. These studies presents a digital hair removal method and de-blurs or denoise the images. To extract essential patterns from the skin imagesand statistical features techniques are utilized. Two efficient machine learning algorithms, namely Support Vector Machine (SVM), and Convolutional Neural Network (CNN) classifiers, are employed with the extracted features to accurately classify skin images as melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis (AK), benign keratosis (BKL), dermatofibroma (DF), vascular lesion (VASC), and Squamous cell carcinoma (SCC). The models are validated using the ISIC 2019 challenge and HAM10000 datasets, with SVM demonstrating slightly superior performance compared to the other classifiers. Furthermore, a comparison with state-of-the-art methods is conducted to evaluate the effectiveness of the proposed approach.

Deep Long and Short Term Memory with Tunicate Swarm Algorithm for Skin Disease Detection and Classification

The development and implementation of cost-effective and efficient screening technologies is important. To address these concerns, we have introduced a unique method to detect skin diseases. Each photo is first pre-processed and cropped to pixel size. Six square fields are used to split these pictures into pixels. Techniques for enlarging images, such as rotation, mirroring, and enhancement, are employed to minimize the quantity of parameters needed for further processes. An kernel-weighted fuzzy local information or the C-means clustering model (K-FCM) is used to properly segment cancer-affected regions. Texture and colour features are then extracted. Finally, a deep long-term and short-term memory (DLTM)-based tunicate group algorithm (TSA) is used to detect skin diseases and classify both normal and abnormal classes. The experiment was carried out using MATLAB, and photos were gathered from the Helllev University Hospital in Denmark. According to the comparative analysis results, the proposed DLSTM-TSA outperforms competing products in terms of F-score, sensitivity, and precision.

Detection of Skin Disease by using Image Processing

There are many types and forms of skin diseases and have many causes, including internal ones related to hormones and body glands such as acne, or external related to air pollution or sensitivity to sunlight such as rashes. The skin disease may be contagious, such as scabies and lice, or it may not be contagious, such as drug allergy and rosacea, or it may be chronic, such as psoriasis and atopic eczema, or it may be rare, such as sweet syndrome and ofuji disease. The outlook of society, in general, reduces the importance of skin disease when it appears and it is preferable not to visit the doctor. Skin diseases are a major and worrying problem in societies due to their physical and psychological effects on patients. Detecting skin diseases at an early stage has an important role in treatment. The process of diagnosing and treating skin injury is related to the skill and experience of the specialist doctor. skin disease prediction by using a neural network based on image analysis. Diagnosis and prediction of skin disease take a very long process because it requires a patient’s history, physical examination, and proper laboratory diagnostic tests. st of such diagnosis is still limited and very expensive. So, image processing techniques help to build automated screening system for dermatology at an initial stage. The extraction of features plays a key role in helping to classify skin disease. proach works on the inputs of a color image. Then resize the of the image to extract features using pre trained convolutional neural network. After that classified feature using Multiclass SVM. Finally, the results are shown to the user, including the type.