Typical Skin Lesions Research Articles

AcneTyper: An automatic diagnosis method of dermoscopic acne image via self-ensemble and stacking.

Acne is a skin lesion type widely existing in adolescents, and poses computational challenges for automatic diagnosis. Computer vision algorithms are utilized to detect and determine different subtypes of acne. Most of the existing acne detection algorithms are based on the facial natural images, which carry noisy factors like illuminations. In order to tackle this issue, this study collected a dataset ACNEDer of dermoscopic acne images with annotations. Deep learning methods have demonstrated powerful capabilities in automatic acne diagnosis, and they usually release the training epoch with the best performance as the delivered model. This study proposes a novel self-ensemble and stacking-based framework AcneTyper for diagnosing the acne subtypes. Instead of delivering the best epoch, AcneTyper consolidates the prediction results of all training epochs as the latent features and stacks the best subset of these latent features for distinguishing different acne subtypes. The proposed AcneTyper framework achieves a promising detection performance of acne subtypes and even outperforms a clinical dermatologist with two-year experiences by 6.8% in accuracy. The method we proposed is used to determine different subtypes of acne and outperforms inexperienced dermatologists and contributes to reducing the probability of misdiagnosis.

Melanoma Skin Cancer Segmentation using Robust Multi-View Fuzzy C- Means Algorithm

Skin cancer has become a significant issue and the primary cause of death for people all over the world. Melanoma is one of the most serious types of skin cancer since it can spread to any area of the body. So, the prognosis of melanoma in its early stages is very important for figuring out how likely it is that the patient will get better. Because of this, clinical imaging science is a very important part of finding certain types of skin lesions quickly and correctly. In this study, we showed a new way to use digital image processing to separate skin lesions in dermoscopic images. First, the image is preprocessed with a median filter to eliminate extra hair, noise, and artefacts in order to create a better image for segmentation. To improve the accuracy of dermoscopic images, the preprocessed images are segmented based on an innovative method built on an improved robust Multiview Fuzzy C-Means clustering algorithm (RMV-FCM). Following segmentation, features are extracted using the ABCD rule to produce optimal features that may be used as input for classification. Lastly, the Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), and Nave Bayes classifiers use machine learning techniques to characterize skin lesions as malignant and benign (NB). The RMV-FCM algorithm exhibits more adaptability and higher clustering performance when compared to a variety of related clustering techniques. In terms of detection accuracy, as noted in the conclusion of this research, the novelty of the work shows that RMV-FCM is exceptional in comparison to numerous related clustering algorithms, and SVM is exceptional in comparison to other standard classifiers. The simulated outcome indicates that the suggested strategy accurately diagnoses skin tumors with a 97.5% accuracy rate. The results of the suggested segmentation are extremely accurate when compared to other algorithms in the same domain

Improving trust and confidence in medical skin lesion diagnosis through explainable deep learning

AbstractA key issue in critical contexts such as medical diagnosis is the interpretability of the deep learning models adopted in decision-making systems. Research in eXplainable Artificial Intelligence (XAI) is trying to solve this issue. However, often XAI approaches are only tested on generalist classifier and do not represent realistic problems such as those of medical diagnosis. In this paper, we aim at improving the trust and confidence of users towards automatic AI decision systems in the field of medical skin lesion diagnosis by customizing an existing XAI approach for explaining an AI model able to recognize different types of skin lesions. The explanation is generated through the use of synthetic exemplar and counter-exemplar images of skin lesions and our contribution offers the practitioner a way to highlight the crucial traits responsible for the classification decision. A validation survey with domain experts, beginners, and unskilled people shows that the use of explanations improves trust and confidence in the automatic decision system. Also, an analysis of the latent space adopted by the explainer unveils that some of the most frequent skin lesion classes are distinctly separated. This phenomenon may stem from the intrinsic characteristics of each class and may help resolve common misclassifications made by human experts.

Bullous diabeticorum

Bullous Diabeticorum is a skin condition that occurs rarely in diabetic patients. It is a specific type of skin lesions characterized by non-inflammatory blisters that heals spontaneously. It is usually found in long standing diabetic patients with poor glycemic control, although the relationship between BD and blood glucose level in diabetic patients remains unclear. It frequently goes undiagnosed because it resembles other vesicobullous illnesses. Few published articles reported such cases. This case report describes a rare case of Saudi female patient presented to the clinic with a well demarcated erythematic fluid-filled lesion. Based on medical history and clinical examination diagnosis of diabetic bullae was made. We present this case to shed light on the rare occurrence of Bullous Diabeticorum in diabetic patients, to highlight the role of interprofessional team in its management and to improve the patients’ outcome.

Insulin Stimulates IL-23 Expression in Human Adipocytes: A Possible Explanation for the Higher Prevalence of Psoriasis in Obesity.

Psoriasis is a chronic systemic inflammatory disease involving the production of many pro-inflammatory cytokines derived from immune cells and interacting with different tissues leading to the typical skin lesions. Psoriasis shows a higher prevalence and a worse progression in obese than in lean subjects. The IL-23/IL-17 immune axis has a pivotal role in the pathogenesis of psoriasis and anti-IL-23 monoclonal antibodies are highly effective in its treatment. Since obesity in frequently associated with elevated insulin plasma levels, we have investigated the ability of in vitro differentiated human adipocytes to produce IL-23 at basal conditions and after insulin stimulation. In vitro differentiated human adipocytes were incubated in the absence and presence of different insulin concentrations and the expression of IL-23 was analyzed by real-time PCR and Western blotting. The results of this study show that in vitro differentiated human adipocytes spontaneously express IL-23 mRNA and protein being stimulated by insulin in a dose-dependent manner. The stimulatory effects of insulin on IL-23 expression were specific since it did not stimulate the expression of other well-known cytokines involved in psoriasis pathogenesis such as Il-22 nor LL-37. Furthermore, lipopolysaccharide did not stimulate IL-23 expression in human adipocytes, thus highlightening the specific effects of insulin in the stimulation of IL-23 expression in human adipocytes. Here we show that human adipocytes spontaneously express IL-23 and that insulin stimulates IL-23 production by these cells in a specific manner as other stimuli, known to be involved in psoriasis pathophysiology, are ineffective. These observations could explain the association between psoriasis and obesity, a condition frequently characterized by a state of insulin hypersecretion.

Skin Lesion Classification Using CNN-based Transfer Learning Model

The computer-aided diagnosis (CAD) and the analysis of skin lesions using deep learning models have become common in the last decade. The proposed CAD systems have considered various datasets and deep learning models. The transfer of knowledge from particular pre-trained models to others has also gained importance due to the efficient convergence and superior results. This study presents the design and implementation of a transfer learning model using Convolutional Neural Networks (CNN) with variable training epoch numbers to classify skin lesion images obtained by smartphones. The model is divided into the inner and external CNN models to train and transfer the knowledge, and the preprocessing and data augmentation are not applied. Several experiments are performed to classify cancerous and non-cancerous skin lesions and all skin lesion types provided in the dataset separately. The designed model increased the classification rates by 20% compared to the conventional CNN. The transfer learning model achieved 0.81, 0.88, and 0.86 mean recall, mean specificity, and mean accuracy in detecting cancerous lesions, and 0.83, 0.90, and 0.86 macro recall, macro precision, and macro F1 score in classifying six skin lesions. The obtained results show the efficacy of transfer learning in skin lesion diagnosis.

Study on skin infection model of Staphylococcus aureus based on analytic hierarchy process and Delphi method

PurposeInfectious skin diseases are a type of inflammatory skin lesions caused by pathogenic microorganisms. Because of the uncertainty of methodology, the skin infection model usually have low replication rate and lack of good evaluation system. We aimed to establish multi-index and comprehensive evaluation method for Staphylococcus aureus (S.aureus) skin-infection models through Analytic hierarchy process (AHP) and Delphi method, and screen high quality animal models through it. Materials and methodsFirstly, the evaluation indicators of skin infection were collected basing on literature research. The weight of the evaluation indicators were decided according to AHP and Delphi method. Then different ulcer models (mouse or rat) infected by S. aureus were selected as the research objects. ResultsThe evaluation indicators were classified into four groups of criteria (including ten sub-indicators) and given different weights, physical sign changes (0.0518), skin lesion appearance (0.2934), morphological observation (0.3184), etiological examination (0.3364). Through the evaluation system, we screened and found that the mouse ulcer model which caused by a round wound and 1.0 × 1010 CFU/mL (0.1 mL) bacterial concentration got the highest comprehensive score, and also found that the model which caused by a 1.5 cm-round wound and 1.0 × 1010 CFU/mL (0.2 mL) maybe the best rat ulcer model. ConclusionsThis study has established an evaluation system based on AHP and Delphi method, also provided the best skin ulcer models selected by this system, the models are suitable for disease research and drug development research of skin ulcer.

Skin Melanoma Cancer Detection and Classification using Machine Learning

Skin cancer is a common and potentially life-threatening disease that affects millions of people worldwide. Early detection and accurate classification of skin lesions are critical for effective therapy and improved patient outcomes. In recent years, advances in machine learning and computer vision techniques have shown promising results in automating skin cancer detection and classification. The goal of this project is to develop an automated system for skin cancer detection and classification using machine learning algorithms. The proposed system uses a dataset of dermatoscopic images collected from different sources covering different types of skin lesions, including malignant melanoma, basal cell carcinoma, and squamous cell carcinoma. The project includes several main stages. First, preprocessing techniques including noise reduction, normalization, and feature extraction are used to improve image quality. Next, a comprehensive set of features such as color, texture, and portrait features are extracted from the preprocessed images. These features are input to various machine learning models, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs) and Random Forests.

Micro-relief characterization of benign and malignant skin lesions by polarization speckle analysis in vivo.

A recent direction in skin disease classification is to develop quantitative diagnostic techniques. Skin relief, colloquially known as roughness, is an important clinical feature. The aim of this study is to demonstrate a novel polarization speckle technique to quantitatively measure roughness on skin lesions in vivo. We then calculate the average roughness of different types of skin lesions to determine the extent to which polarization speckle roughness measurements can be used to identify skin cancer. The experimental conditions were set to target the fine relief structure on the order of ten microns within a small field of view of 3 mm. The device was tested in a clinical study on patients with malignant and benign skin lesions that resemble cancer. The cancer group includes 37 malignant melanomas (MM), 43 basal cell carcinomas (BCC), and 26 squamous cell carcinomas (SCC), all categories confirmed by gold standard biopsy. The benign group includes 109 seborrheic keratoses (SK), 79 nevi, and 11 actinic keratoses (AK). Normal skin roughness was obtained for the same patients (301 different body sites proximal to the lesion). The average root mean squared (rms) roughness ± standard error of the mean for MM and nevus was equal to 19 ± 5 μm and 21 ± 3 μm, respectively. Normal skin has rms roughness of 31 ± 3 μm, other lesions have roughness of 35 ± 10 μm (AK), 35 ± 7 μm (SCC), 31 ± 4 μm (SK), and 30 ± 5 μm (BCC). An independent-samples Kruskal-Wallis test indicates that MM and nevus can be separated from each of the tested types of lesions, except each other. These results quantify clinical knowledge of lesion roughness and could be useful for optical cancer detection.

Plasmacytoid dendritic cell neoplasms.

Plasmacytoid dendritic cells (pDCs) are type I interferon-producing cells that modulate immune responses. There are two types of pDC neoplasms: 1) mature pDC proliferation (MPDCP) associated with myeloid neoplasm and 2) blastic pDC neoplasm (BPDCN). MPDCP is a clonal expansion of mature pDCs that is predominantly associated with chronic myelomonocytic leukemia. In contrast, BPDCN is a clinically aggressive myeloid malignancy involving the skin, bone marrow, lymphatic organs, and central nervous system. There are various types of skin lesions, ranging from solitary brown or violaceous to disseminated cutaneous lesions, which often spread throughout the body. The expression of CD4, CD56, CD123, and pDC markers (TCL-1, TCF4, CD303, and CD304, etc.) are typical immunophenotype of BPDCN. Historically, BPDCN treatment has been based on acute leukemia regimens and allogeneic hematopoietic cell transplantation in selected patients. Recent advances in molecular biology and genetics have led to the development of targeted agents, such as tagraxofusp (a recombinant fusion protein targeting CD123), anti-CD123 CAR-T cells, XmAb14045, and IMGN632. Lastly, this review provides a comprehensive overview of pDC neoplasms.

Skin Lesion Segmentation and Classification Using Deep Learning

Abstract: Classification of skin lesions has recently received a lot of attention. Due to the high degree of similarity between the skin lesions, physicians frequently take a long time to examine them. A deep learning-based automated classification system can help doctors identify the type of skin lesion and improve the patient's health. With the development of deep learning architecture, the classification of skin lesions has emerged as a popular area of research. In this research, we present a method to use segmentation and techniques like Averaging of Deep Learning Architectures and classify skin lesions. We evaluated the proposal using a large dataset: HAM 10000. Our numerical results using VGGNet and ResNet using good results on the mentioned dataset.

A novel approach toward skin cancer classification through fused deep features and neutrosophic environment.

Variations in the size and texture of melanoma make the classification procedure more complex in a computer-aided diagnostic (CAD) system. The research proposes an innovative hybrid deep learning-based layer-fusion and neutrosophic-set technique for identifying skin lesions. The off-the-shelf networks are examined to categorize eight types of skin lesions using transfer learning on International Skin Imaging Collaboration (ISIC) 2019 skin lesion datasets. The top two networks, which are GoogleNet and DarkNet, achieved an accuracy of 77.41 and 82.42%, respectively. The proposed method works in two successive stages: first, boosting the classification accuracy of the trained networks individually. A suggested feature fusion methodology is applied to enrich the extracted features' descriptive power, which promotes the accuracy to 79.2 and 84.5%, respectively. The second stage explores how to combine these networks for further improvement. The error-correcting output codes (ECOC) paradigm is utilized for constructing a set of well-trained true and false support vector machine (SVM) classifiers via fused DarkNet and GoogleNet feature maps, respectively. The ECOC's coding matrices are designed to train each true classifier and its opponent in a one-versus-other fashion. Consequently, contradictions between true and false classifiers in terms of their classification scores create an ambiguity zone quantified by the indeterminacy set. Recent neutrosophic techniques resolve this ambiguity to tilt the balance toward the correct skin cancer class. As a result, the classification score is increased to 85.74%, outperforming the recent proposals by an obvious step. The trained models alongside the implementation of the proposed single-valued neutrosophic sets (SVNSs) will be publicly available for aiding relevant research fields.

Skin Diseases Associated With COVID-19: A Narrative Review.

COVID-19 is an infectious disease caused by SARS-CoV-2, which emerged in China in 2019 and rapidly spread worldwide to become a pandemic in March 2020. Although the most severe manifestations concern the lower respiratory tract, COVID-19 is a multiorgan disease that also affects the skin. Several types of skin lesions have been reported to be associated with SARS-CoV-2 infection, though their causal relationship with the virus has not yet been well documented. In addition to the cutaneous manifestations that develop in patients with COVID-19-thought to be caused by the virus-other findings associated with the pandemic in a broader sense include dermatoses triggered or aggravated by the infection, the adverse cutaneous effects due to the drugs and protective devices used to prevent or fight the infection, and the adverse cutaneous effects of COVID-19 vaccines. We provide an overview of these dermatoses associated with the COVID-19 pandemic.

Dermatological Manifestations in COVID-Related Illness in Children: A Retrospective Observational Study at a Tertiary Care Hospital of Eastern India

Objectives: To identify the spectrum of cutaneous manifestations and to evaluate temporal relationship between each type of dermatological lesions and the severity of COVID-19-related illness in the pediatric population. Subjects and Methods: Retrospective observational study on 35 children (upto12 years) from Kolkata and different district areas of West Bengal, admitted to our tertiary care hospital with COVID-related illnesses (COVID-19 and/or multisystem inflammatory syndrome in children [MIS-C]). Results: Maculopapular rash was heading the list (n = 18, 51.4%) followed by chilblain-like lesions (n = 12, 34.2%), vasculistic lesion (n = 8, 22.8%), vesicular rash (n = 5, 14.3%), and urticaria (n = 3, 8.5%). In majority of patients (n = 26, 74.2%), dermatologic manifestations proceeded (n = 16, 45.7%) or merged (n = 10, 28.6%) with the onset of systemic symptoms. In rest of the patients (n = 9, 25.7%), skin manifestations occurred after the systemic symptoms, particularly in vascular lesions. 57.1% children required pediatric intensive care unit (PICU) admission. All children with vasculitis (n = 8) required PICU admission and 87.5% (n = 7) of them needed inotrope support. Intravenous immunoglobulin (IVIG) was mostly given in children with chilblains (n = 11, 92%). Methylprednisolone and repeat dose of IVIG was mostly needed in patients with vasculitis (75% and 25% of vasculitis children, respectively). The duration of PICU stay was least in patients with vesicular rash (4.4 ± 2.5 days) and longest in vasculistic lesions (18.75 ± 1 days), highest being 56 days. The overall mortality rate among MIS-C patients with cutaneous manifestations was 25.7% (n = 9) while patients with vasculistic lesions had the highest mortality rate of 50% (n = 4). Conclusion: Our study finding reveals that COVID-related disease severity is highly depends on the type of skin lesions but not just on mere occurrence of skin manifestations. Lesions such as maculopapular, chilblains, and vesicular rash had good prognosis and urticaria and vasculitis had poor prognosis.

A novel nonlinear automated multi-class skin lesion detection system using soft-attention based convolutional neural networks

Skin cancer is one of the most common cancer types that negatively affect human life worldwide and can result in death. So early diagnosis is essential for patient treatment. However, even by expert dermatologists, accurate skin lesion detection is complex. Therefore, developing computer-aided skin lesion detection and classification systems will help dermatologists provide early diagnosis and more effective treatment of skin lesion disease. Recent studies show deep learning networks have great potential for recognizing and classifying different skin lesions. This study aims to benefit from data processing, analysis, and a deep learning model base on soft attention to improve the performance for skin lesion classification. This research proposes an attention mechanism-based deep learning approach supported by data balancing methods. The HAM10000 dataset containing 10,015 labeled images belonging to 7 different skin lesion types was used. The dataset is balanced with SMOTE, ADASYN, RandomOverSampler, and Data Augmentation methods. A soft attention module was preferred as an attention mechanism to focus on the input data features and obtain a feature map. The proposed model consists of two convolution layers, a soft attention module, and four convolution layers. By combining convolutional neural networks and the attention mechanism, we extract the image features of the convolutional neural networks. In contrast, the soft attention module focuses on the relevant areas of the image. The accuracy rate of the proposed model is increased by the soft attention module and balance techniques in skin lesion classification problems. Various studies have been conducted using convolutional neural networks and attention mechanisms on open-source datasets for skin lesion classification. The originality of our study can be stated as using the attention mechanism in the middle of the neural network and performing experiments on the balanced dataset with various balancing methods. The training was carried out on the original, and the balanced versions of the HAM10000 dataset and the test results of the proposed model were presented. As a result of the training, 85.73 % training, 70.90 % validation, and 69.75 % test accuracy rates were obtained on the unbalanced HAM10000 dataset. The balanced dataset with SMOTE methods obtained 99.86 % train, 96.41 % validation, and 95.94 % test accuracy rates. SMOTE method performs better results than other balancing methods. It is seen that the proposed model, with data balancing techniques, has achieved high accuracy rates. It was concluded that using attention mechanisms and data balancing methods in deep neural networks increases the network's success.

Leveraging CNN and Vision Transformer with Transfer Learning to Diagnose Pigmented Skin Lesions

The transformer has emerged in computer vision, but whether it can rival or even replace the status of a convolutional neural network (CNN) still lacks deeper cognition. Therefore, this paper will study the feasibility and effectiveness of transformers in identifying the types of pigmented skin lesions by comparing one of the most widely known CNN architectures with three state-of-the-art Vision Transformer (ViT) models. The research results found that the transformer surprisingly outperforms the CNN. In other words, the overall accuracy and F1-score per class of its classifier are much higher than those of the CNN. In addition, the experimental data results show that if the ViT model has more training data input, its performance seems to have a large room for improvement. Conversely, the CNN model shows no indication that increasing training data will affect its performance. Therefore, if there are more dermoscopy images with corresponding label data to make the dataset larger and balanced, the transformer may completely replace CNN on this task.

AI Techniques of Dermoscopy Image Analysis for the Early Detection of Skin Lesions Based on Combined CNN Features.

Melanoma is one of the deadliest types of skin cancer that leads to death if not diagnosed early. Many skin lesions are similar in the early stages, which causes an inaccurate diagnosis. Accurate diagnosis of the types of skin lesions helps dermatologists save patients' lives. In this paper, we propose hybrid systems based on the advantages of fused CNN models. CNN models receive dermoscopy images of the ISIC 2019 dataset after segmenting the area of lesions and isolating them from healthy skin through the Geometric Active Contour (GAC) algorithm. Artificial neural network (ANN) and Random Forest (Rf) receive fused CNN features and classify them with high accuracy. The first methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid models CNN-ANN and CNN-RF. CNN models (AlexNet, GoogLeNet and VGG16) receive lesions area only and produce high depth feature maps. Thus, the deep feature maps were reduced by the PCA and then classified by ANN and RF networks. The second methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid CNN-ANN and CNN-RF models based on the features of the fused CNN models. It is worth noting that the features of the CNN models were serially integrated after reducing their high dimensions by Principal Component Analysis (PCA). Hybrid models based on fused CNN features achieved promising results for diagnosing dermatoscopic images of the ISIC 2019 data set and distinguishing skin cancer from other skin lesions. The AlexNet-GoogLeNet-VGG16-ANN hybrid model achieved an AUC of 94.41%, sensitivity of 88.90%, accuracy of 96.10%, precision of 88.69%, and specificity of 99.44%.

HISTO-PATHOLOGICAL SPECTRUM OF SKIN LESIONS IN TERTIARY CARE CENTRE: A THREE YEAR STUDY

Introduction: The largest sensory organ of human body is the skin, which accounts for about 15% of total body weight. It is a complex organ having multiple functions. Primarily it acts as a barrier against various harmful environmental agents (1).Skin diseases affect all age groups and are much common in developing countries. In the field of dermatology, 2000 different skin diseases are well known(2) Objectives: The objectives of the study were as follows: (1) To estimate the prevalence of various non-neoplastic and neoplastic skin lesions in our tertiary care hospital. (2) To study the age and sex-wise distribution of non-neoplastic and neoplastic skin lesions. Methods: The present study comprised a total of 102 skin biopsies of non-neoplastic lesions and neoplastic skin lesions, relevant data like age, sex, and sites of skin lesion were obtained from request forms provided to histopathology section. Specimens received were fixed in 10% formalin. Biopsy bits were submitted as whole and processed in automated tissue processor for routine paraffin embedding. Results: Out of 102 biopsies that were analyzed, most belonged to 21- 60years with slight male predominance with a total of 85 cases being Non-Neoplastic skin lesions, of which Chronic dermatitis 18 (28.5%) was the most common histopathological diagnosis, followed by Hansens disease 21(24.7%) among granulomatous skin disease, and a total of 17 cases of neoplastic skin lesions were identified of which dermatofibroma 2(11.8%) and eccrine spiradenoma 2(11.8%) were the benign variants , and Squamous cell carcinoma 3(17.6%) was the most common malignant variant. Conclusion: Histopathological evaluation of skin biopsies holds gold standard technique for diagnosing various skin lesions and their microscopic evaluation helps in assessing variegated morphological features of different types of skin lesions.

Correlation of Human Papillomavirus types with the level of damage in women attended in a private laboratory

Introduction: the Human Papillomavirus (HPV) is a virus capable of promoting various types of skin and mucosal lesions, which can be malignant or benign. Some of them can cause genital warts, and others can reach the cervix and cause a Cervical Intraepithelial Neoplasm (CIN) that can progress to a more advanced stage, which is cancer. The sexually active population is more likely to come into contact with the virus at some point in life, and as a result, the human papillomavirus is the most common sexually transmitted infection (STI) in the world. Objective: to evaluate the incidence of HPV in women attended at a private laboratory in Fortaleza/CE. Method: this was a quantitative, cross-sectional, retrospective and descriptive research, using data from the patients' medical records. Results: after analyzing the patient’s medical records, it was observed that the predominant age group in this study was concentrated between 16 and 34 years old. Furthermore, it was evident that the vast majority of women obtained positive results for HPV, and they arrived with observations in the search for screening. Furthermore, the most prevalent intraepithelial lesions were LSIL and ASC-US. Conclusion: HPV was more associated with young women, and screening is of paramount importance since HPV is asymptomatic, and this factor can lead to women's erroneous thinking that without evident symptoms there is no lesion.

Multi-Models of Analyzing Dermoscopy Images for Early Detection of Multi-Class Skin Lesions Based on Fused Features

Melanoma is a cancer that threatens life and leads to death. Effective detection of skin lesion types by images is a challenging task. Dermoscopy is an effective technique for detecting skin lesions. Early diagnosis of skin cancer is essential for proper treatment. Skin lesions are similar in their early stages, so manual diagnosis is difficult. Thus, artificial intelligence techniques can analyze images of skin lesions and discover hidden features not seen by the naked eye. This study developed hybrid techniques based on hybrid features to effectively analyse dermoscopic images to classify two datasets, HAM10000 and PH2, of skin lesions. The images have been optimized for all techniques, and the problem of imbalance between the two datasets has been resolved. The HAM10000 and PH2 datasets were classified by pre-trained MobileNet and ResNet101 models. For effective detection of the early stages skin lesions, hybrid techniques SVM-MobileNet, SVM-ResNet101 and SVM-MobileNet-ResNet101 were applied, which showed better performance than pre-trained CNN models due to the effectiveness of the handcrafted features that extract the features of color, texture and shape. Then, handcrafted features were combined with the features of the MobileNet and ResNet101 models to form a high accuracy feature. Finally, features of MobileNet-handcrafted and ResNet101-handcrafted were sent to ANN for classification with high accuracy. For the HAM10000 dataset, the ANN with MobileNet and handcrafted features achieved an AUC of 97.53%, accuracy of 98.4%, sensitivity of 94.46%, precision of 93.44% and specificity of 99.43%. Using the same technique, the PH2 data set achieved 100% for all metrics.