Skin Disease Identification Research Articles

Automated detection and classification of skin diseases using diverse features and improved gray wolf‐based multiple‐layer perceptron neural network

AbstractOne of the largest organs of the human body is the skin and its pigmentation differs among the population. During skin disease identification, the dermatologist requires a high level of expertise and accuracy. This study proposes different kinds of skin image feature extraction and classification methods. In this work, we have chosen six kinds of skin diseases such as melanoma, seborrheic keratosis, eczema, actinic keratoses, nevus, and lupus erythematosus. In the preprocessing stage, the color standardization is performed by gray world color constancy (GWCC) algorithm. The thick and thin hairs from the disease images are removed in the preprocessing stage. The circular kernel with morphological operation clearly segments the skin lesion region. Moreover, the combination of novel cumulative‐based level difference mean (NCLDM) and improved Asymmetry, Border Irregularity, Color Variation and Diameter (ABCD) features vector (ABCD‐fv) methods is more helpful to extract the shape, texture, and color feature of skin lesion. However, the more features never offer an accurate classification result, so we go for the ranking and selection of features. Finally, the improved gray wolf‐based multiple‐layer perceptron (IGWO‐MLP) technique is used to produce the relevant skin disease class. For the experimentation, there are six datasets such as DermNet, Xiangya, Medicine Net, PH2, Kaggle, and HAM‐10000, which are chosen for effective skin disease identification. The proposed method demonstrates better segmentation, feature extraction, and classification result in terms of accuracy, specificity, sensitivity, Jaccard similarity index, and Dice similarity index. As a result, the skin disease identification of the proposed IGWO‐MLP method yields 98% accuracy, 99% sensitivity, 98% specificity, 98% Jaccard Similarity Index, and 99% Dice similarity index when compared with state‐of‐the‐art methods. This work will certainly help dermatologists to make their work more efficient and help them to provide the correct treatment for the skin disease detected.

Dermatological expert system implementing the ABCD rule of dermoscopy for skin disease identification

Doctors and radiologists generally follow the standard ABCD rule of dermoscopy for differentiating the malignant and benign skin lesions. The estimation of the dermoscopic score by visual inspection only, may lead to the inaccurate diagnosis of the disease at an early stage. In this work, the ABCD attributes have been improvised and quantified in a dermatological expert system (DermESy) for the differentiation of malignant and benign lesions. DermESy, a rule based expert system has been developed by implementing dermatologist’s knowledge with proper quantification of the dermoscopic findings. Using DermESy, the dermoscopic images have been categorized as malignant, benign and suspicious lesions based on the estimated total dermoscopic score (TDS), similar to the findings of an expert. To estimate the TDS, shape, brightness and color variations are considered to modify the ‘A’ score. The color information extraction algorithm is introduced to extract significant color regions to quantify the ‘C’ score. To find the appropriate ‘D’ score of a skin lesion, dermoscopic structures segmentation algorithms have been introduced. In this work, the ABCD rule of dermoscopy has been improvised by considering the spatial properties of dermoscopic structures for improved identification of malignant lesions. An explanatory subsystem is implemented in DermESy to assist the dermatologist with proper in-detail visualization. DermESy has differentiated the benign and malignant skin lesions with 97.69% sensitivity, 97.97% specificity and 97.86% accuracy. The TDS evaluated by DermESy is verified and compared against expert dermatologist’s TDS scores of same dermoscopy images to establish the reliability and robustness of the proposed system.

Identification of Skin Disease Using Deep Learning

Globally, skin diseases are among the most common health problems in all humans irrespective of age. Prevention and early detection of these diseases can improve the chance of surviving. This model illustrates the identification of skin diseases providing more objective and reliable solutions using deep learning technology and convolutional neural network approach. In this model, the system design, implementation and identification of common skin diseases such as acne, blister, eczema, warts etc. are explained. The system applies deep learning technology to train itself with various images of skin diseases from the Kaggle platform. The accuracy obtained by using deep learning is 83.23%. The main objective of this system is to achieve maximum accuracy of skin disease prediction. Moreover, if the disease is identified the system provides detailed information about the diseases along with home remedies.

Decision Support Techniques for Dermatology Using Case-Based Reasoning

Identification of skin disease has become a challenging task with the origination of various skin diseases. This paper presents a case-based reasoning (CBR) decision support system to enhance dermatological diagnosis for rural and remote communities. In this proposed work, an automated way is introduced to deal with the inconsistency problem in CBRs. This new hybrid architecture is to support the diagnosis in multiple skin diseases. The architecture used case-based reasoning terminology facilitates the medical diagnosis. Case based reasoning system retrieves the data which contains symptoms and treatment plan of the disease from the data repository by the way of matching visual contents of the image, such as shape, texture, and color descriptors. The extracted feature vector is fed into a framework to retrieve the data. The results proved using ROC curve that the proposed architecture yields high contribution to the computer-aided diagnosis of skin lesions. In experimental analysis, the system yields a specificity of 95.25% and a sensitivity of 86.77%. Our empirical evaluation has a superior retrieval and diagnosis performance when compared to the performance of other works.

Deep Learning in Skin Disease Image Recognition: A Review

The application of deep learning methods to diagnose diseases has become a new research topic in the medical field. In the field of medicine, skin disease is one of the most common diseases, and its visual representation is more prominent compared with the other types of diseases. Accordingly, the use of deep learning methods for skin disease image recognition is of great significance and has attracted the attention of researchers. In this study, we review 45 research efforts on the identification of skin disease by using deep learning technology since 2016. We analyze these studies from the aspects of disease type, data set, data processing technology, data augmentation technology, model for skin disease image recognition, deep learning framework, evaluation indicators, and model performance. Moreover, we summarize the traditional and machine learning-based skin disease diagnosis and treatment methods. We also analyze the current progress in this field and predict four directions that may become the research topic in the future. Our results show that the skin disease image recognition method based on deep learning is better than those of dermatologists and other computer-aided treatment methods in skin disease diagnosis, especially the multi deep learning model fusion method has the best recognition effect.

Integration of morphological preprocessing and fractal based feature extraction with recursive feature elimination for skin lesion types classification

Background and objectiveSkin cancer is the commonest form of cancer in the worldwide population. Non-invasive and non-contact imaging modalities are being used for the screening of melanoma and other cutaneous malignancies to endorse early detection and prevention of the disease. Traditionally it has been a problem for medical personnel to differentiate melanoma, dysplastic nevi and basal cell carcinoma (BCC) diseases from one another due to the confusing appearance and similarity in the characteristics of the pigmented lesions. The paper reports an integrated method developed for identifying these skin diseases from the dermoscopic images. MethodsThe proposed integrated computer-aided method has been employed for the identification of each of these diseases using recursive feature elimination (RFE) based layered structured multiclass image classification technique. Prior to the classification, different quantitative features have been extracted by analyzing the shape, the border irregularity, the texture and the color of the skin lesions, using different image processing tools. Primarily, a combination of gray level co-occurrence matrix (GLCM) and a proposed fractal-based regional texture analysis (FRTA) algorithm has been used for the quantification of textural information. The performance of the framework has been evaluated using a layered structure classification model using support vector machine (SVM) classifier with radial basis function (RBF). ResultsThe performance of the morphological skin lesion segmentation algorithm has been evaluated by estimating the pixel level sensitivity (Sen) of 0.9172, 0.9788 specificity (Spec), 0.9521 accuracy (ACU), along with the image similarity measuring indices as Jaccard similarity index (JSI) of 0.8562 and Dice similarity coefficient (DSC) of 0.9142 with respect to the corresponding ground truth (GT) images. The quantitative features extracted from the proposed feature extraction algorithms have been employed for the proposed multi-class skin disease identification. The proposed layered structure identifies all the three classes of skin diseases with a highly acceptable classification accuracy of 98.99%, 97.54% and 99.65% for melanoma, dysplastic nevi and BCC respectively. ConclusionTo overcome the difficulties of proper diagnosis of diseases based on visual evaluation, the proposed integrated system plays an important role by quantifying the effective features and identifying the diseases with higher degree of accuracy. This combined approach of quantitative and qualitative analysis not only increases the diagnostic accuracy, but also provides some important information not obtainable from qualitative assessment alone.

Retrieval in Dermatology using Intelligent Techniques

Identification of skin disease has become a challenging task with the origination of various skin diseases. The architecture used in this work is content-based image retrieval to facilitate medical diagnosis. In this work, analysis of feature vectors based on fusion of colour and texture and shape features are carried out. The feature vector data are fed into an optimization framework. The results proved that using precision recall curve the shape feature vectors and Mahalanobis distance measure have high contribution to computer-aided diagnosis of skin lesions. Experiments on a set of 2420 images yielded a specificity of 97.04% and a sensitivity of 80.81%. Our empirical evaluation has a superior retrieval and diagnosis performance when compared to the performance of other works. The work is tested with 40 skin diseases.