Radiomic and deep learning analysis of dermoscopic images for skin lesion pattern decoding

This study aims to explore the efficacy of a hybrid deep learning and radiomics approach, supplemented with patient metadata, in the noninvasive dermoscopic imaging-based diagnosis of skin lesions. We analyzed dermoscopic images from the International Skin Imaging Collaboration (ISIC) dataset, spanning 2016-2020, encompassing a variety of skin lesions. Our approach integrates deep learning with a comprehensive radiomics analysis, utilizing a vast array of quantitative image features to precisely quantify skin lesion patterns. The dataset includes cases of three, four, and eight different skin lesion types. Our methodology was benchmarked against seven classification methods from the ISIC 2020 challenge and prior research using a binary decision framework. The proposed hybrid model demonstrated superior performance in distinguishing benign from malignant lesions, achieving area under the receiver operating characteristic curve (AUROC) scores of 99%, 95%, and 96%, and multiclass decoding AUROCs of 98.5%, 94.9%, and 96.4%, with sensitivities of 97.6%, 93.9%, and 96.0% and specificities of 98.4%, 96.7%, and 96.9% in the internal ISIC 2018 challenge, as well as in the external Jinan and Longhua datasets, respectively. Our findings suggest that the integration of radiomics and deep learning, utilizing dermoscopic images, effectively captures the heterogeneity and pattern expression of skin lesions.

The accuracy of artificial intelligence used for non-melanoma skin cancer diagnoses: a meta-analysis

BACKGROUND

With rising incidence of skin cancer and relatively increased mortality rates, an improved diagnosis of such a potentially fatal disease is of vital importance. Although frequently curable, it nevertheless places a considerable burden upon healthcare systems. Among the various types of skin cancers, non-melanoma skin cancer is most prevalent. Despite such prevalence and its associated cost, scant proof concerning the diagnostic accuracy via Artificial Intelligence (AI) for non-melanoma skin cancer exists. This study meta-analyzes the diagnostic test accuracy of AI used to diagnose non-melanoma forms of skin cancer, and it identifies potential covariates that account for heterogeneity between extant studies.

METHODS

Various electronic databases (Scopus, PubMed, ScienceDirect, SpringerLink, and Dimensions) were examined to discern eligible studies beginning from March 2022. Those AI studies predictive of non-melanoma skin cancer were included. Summary estimates of sensitivity, specificity, and area under receiver operating characteristic curves were used to evaluate diagnostic accuracy. The revised Quality Assessment of Diagnostic Studies served to assess any risk of bias.

RESULTS

A literature search produced 39 eligible articles for meta-analysis. The summary sensitivity, specificity, and area under receiver operating characteristic curve of AI for diagnosing non-melanoma skin cancer was 0.78, 0.98, & 0.97, respectively. Skin cancer typology, data sources, cross validation, ensemble models, types of techniques, pre-trained models, and image augmentation became significant covariates accounting for heterogeneity in terms of both sensitivity and/or specificity.

CONCLUSIONS

Meta-analysis results revealed that AI is predictive of non-melanoma with an acceptable performance, but sensitivity may become improved. Further, ensemble models and pre-trained models are employable to improve true positive rating.

A survey on deep learning for skin lesion segmentation

Skin cancer is a major public health problem that could benefit from computer-aided diagnosis to reduce the burden of this common disease. Skin lesion segmentation from images is an important step toward achieving this goal. However, the presence of natural and artificial artifacts (e.g., hair and air bubbles), intrinsic factors (e.g., lesion shape and contrast), and variations in image acquisition conditions make skin lesion segmentation a challenging task. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. In this survey, we cross-examine 177 research papers that deal with deep learning-based segmentation of skin lesions. We analyze these works along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules, and losses), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions both from the viewpoint of select seminal works, and from a systematic viewpoint, examining how those choices have influenced current trends, and how their limitations should be addressed. To facilitate comparisons, we summarize all examined works in a comprehensive table as well as an interactive table available online³.

Automatically Diagnosing Skin Cancers From Multimodality Images Using Two-Stage Genetic Programming

Developing a computer-aided diagnostic system for detecting various skin malignancies from images has attracted many researchers. Unlike many machine-learning approaches, such as artificial neural networks, genetic programming (GP) automatically evolves models with flexible representation. GP successfully provides effective solutions using its intrinsic ability to select prominent features (i.e., feature selection) and build new features (i.e., feature construction). Existing approaches have utilized GP to construct new features from the complete set of original features and the set of operators. However, the complete set of features may contain redundant or irrelevant features that do not provide useful information for classification. This study aims to develop a two-stage GP method, where the first stage selects prominent features, and the second stage constructs new features from these selected features and operators, such as multiplication in a wrapper approach to improve the classification performance. To include local, global, texture, color, and multiscale image properties of skin images, GP selects and constructs features extracted from local binary patterns and pyramid-structured wavelet decomposition. The accuracy of this GP method is assessed using two real-world skin image datasets captured from the standard camera and specialized instruments, and compared with commonly used classification algorithms, three state of the art, and an existing embedded GP method. The results reveal that this new approach of feature selection and feature construction effectively helps improve the performance of the machine-learning classification algorithms. Unlike other black-box models, the evolved models by GP are interpretable; therefore, the proposed method can assist dermatologists to identify prominent features, which has been shown by further analysis on the evolved models.

Categorization of Common Pigmented Skin Lesions (CPSL) using Multi-Deep Features and Support Vector Machine

The skin is the main organ. It is approximately 8 pounds for the average adult. Our skin is a truly wonderful organ. It isolates us and shields our bodies from hazards. However, the skin is also vulnerable to damage and distracted from its original appearance: brown, black, or blue, or combinations of those colors, known as pigmented skin lesions. These common pigmented skin lesions (CPSL) are the leading factor of skin cancer, or can say these are the primary causes of skin cancer. In the healthcare sector, the categorization of CPSL is the main problem because of inaccurate outputs, overfitting, and higher computational costs. Hence, we proposed a classification model based on multi-deep feature and support vector machine (SVM) for the classification of CPSL. The proposed system comprises two phases: First, evaluate the 11 CNN model's performance in the deep feature extraction approach with SVM, and then, concatenate the top performed three CNN model's deep features and with the help of SVM to categorize the CPSL. In the second step, 8192 and 12,288 features are obtained by combining binary and triple networks of 4096 features from the top performed CNN model. These features are also given to the SVM classifiers. The SVM results are also evaluated with principal component analysis (PCA) algorithm to the combined feature of 8192 and 12,288. The highest results are obtained with 12,288 features. The experimentation results, the combination of the deep feature of Alexnet, VGG16 and VGG19, achieved the highest accuracy of 91.7% using SVM classifier. As a result, the results show that the proposed methods are a useful tool for CPSL classification.

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researchers have shown an increasing interest in developing computer-aided diagnosis systems. This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems. This study discusses the papers published in the last five years in ScienceDirect, IEEE, and SpringerLink databases. It includes 53 articles using traditional machine learning methods and 49 articles using deep learning methods. The studies are compared based on their contributions, the methods used and the achieved results. The work identified the main challenges of evaluating skin lesion segmentation and classification methods such as small datasets, ad hoc image selection and racial bias.

Advances in the Etiology, Detection, and Clinical Management of Seborrheic Keratoses

Seborrheic keratoses (SKs) are ubiquitous, generally benign skin tumors that exhibit high clinical variability. While age is a known risk factor, the precise roles of UV exposure and immune abnormalities are currently unclear. The underlying mechanisms of this benign disorder are paradoxically driven by oncogenic mutations and may have profound implications for our understanding of the malignant state. Advances in molecular pathogenesis suggest that inhibition of Akt and APP, as well as existing treatments for skin cancer, may have therapeutic potential in SK. Dermoscopic criteria have also become increasingly important to the accurate detection of SK, and other noninvasive diagnostic methods, such as reflectance confocal microscopy and optical coherence tomography, are rapidly developing. Given their ability to mimic malignant tumors, SK cases are often used to train artificial intelligence-based algorithms in the computerized detection of skin disease. These technologies are becoming increasingly accurate and have the potential to significantly augment clinical practice. Current treatment options for SK cause discomfort and can lead to adverse post-treatment effects, especially in skin of color. In light of the discontinuation of ESKATA in late 2019, promising alternatives, such as nitric-zinc and trichloroacetic acid topicals, should be further developed. There is also a need for larger, head-to-head trials of emerging laser therapies to ensure that future treatment standards address diverse patient needs.

A Novel Convolutional Neural Network for the Diagnosis and Classification of Rosacea: Usability Study

BACKGROUND

Rosacea is a chronic inflammatory disease with variable clinical presentations, including transient flushing, fixed erythema, papules, pustules, and phymatous changes on the central face. Owing to the diversity in the clinical manifestations of rosacea, the lack of objective biochemical examinations, and nonspecificity in histopathological findings, accurate identification of rosacea is a big challenge. Artificial intelligence has emerged as a potential tool in the identification and evaluation of some skin diseases such as melanoma, basal cell carcinoma, and psoriasis.

OBJECTIVE

The objective of our study was to utilize a convolutional neural network (CNN) to differentiate the clinical photos of patients with rosacea (taken from 3 different angles) from those of patients with other skin diseases such as acne, seborrheic dermatitis, and eczema that could be easily confused with rosacea.

METHODS

In this study, 24,736 photos comprising of 18,647 photos of patients with rosacea and 6089 photos of patients with other skin diseases such as acne, facial seborrheic dermatitis, and eczema were included and analyzed by our CNN model based on ResNet-50.

RESULTS

The CNN in our study achieved an overall accuracy and precision of 0.914 and 0.898, with an area under the receiver operating characteristic curve of 0.972 for the detection of rosacea. The accuracy of classifying 3 subtypes of rosacea, that is, erythematotelangiectatic rosacea, papulopustular rosacea, and phymatous rosacea was 83.9%, 74.3%, and 80.0%, respectively. Moreover, the accuracy and precision of our CNN to distinguish rosacea from acne reached 0.931 and 0.893, respectively. For the differentiation between rosacea, seborrheic dermatitis, and eczema, the overall accuracy of our CNN was 0.757 and the precision was 0.667. Finally, by comparing the CNN diagnosis with the diagnoses by dermatologists of different expertise levels, we found that our CNN system is capable of identifying rosacea with a performance superior to that of resident doctors or attending physicians and comparable to that of experienced dermatologists.

CONCLUSIONS

The findings of our study showed that by assessing clinical images, the CNN system in our study could identify rosacea with accuracy and precision comparable to that of an experienced dermatologist.

Classification of Dermoscopy Skin Lesion Color-Images Using Fractal-Deep Learning Features

The detection of skin diseases is becoming one of the priority tasks worldwide due to the increasing amount of skin cancer. Computer-aided diagnosis is a helpful tool to help dermatologists in the detection of these kinds of illnesses. This work proposes a computer-aided diagnosis based on 1D fractal signatures of texture-based features combining with deep-learning features using transferred learning based in Densenet-201. This proposal works with three 1D fractal signatures built per color-image. The energy, variance, and entropy of the fractal signatures are used combined with 100 features extracted from Densenet-201 to construct the features vector. Because commonly, the classes in the dataset of skin lesion images are imbalanced, we use the technique of ensemble of classifiers: K-nearest neighbors and two types of support vector machines. The computer-aided diagnosis output was determined based on the linear plurality vote. In this work, we obtained an average accuracy of 97.35%, an average precision of 91.61%, an average sensitivity of 66.45%, and an average specificity of 97.85% in the eight classes’ classification in the International Skin Imaging Collaboration (ISIC) archive-2019.

Artificial Intelligence in Cutaneous Oncology

Skin cancer, previously known to be a common disease in Western countries, is becoming more common in Asian countries. Skin cancer differs from other carcinomas in that it is visible to our eyes. Although skin biopsy is essential for the diagnosis of skin cancer, decisions regarding whether or not to conduct a biopsy are made by an experienced dermatologist. From this perspective, it is easy to obtain and store photos using a smartphone, and artificial intelligence technologies developed to analyze these photos can represent a useful tool to complement the dermatologist's knowledge. In addition, the universal use of dermoscopy, which allows for non-invasive inspection of the upper dermal level of skin lesions with a usual 10-fold magnification, adds to the image storage and analysis techniques, foreshadowing breakthroughs in skin cancer diagnosis. Current problems include the inaccuracy of the available technology and resulting legal liabilities. This paper presents a comprehensive review of the clinical applications of artificial intelligence and a discussion on how it can be implemented in the field of cutaneous oncology.

A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification

Automated skin lesion segmentation and classification are two most essential and related tasks in the computer-aided diagnosis of skin cancer. Despite their prevalence, deep learning models are usually designed for only one task, ignoring the potential benefits in jointly performing both tasks. In this paper, we propose the mutual bootstrapping deep convolutional neural networks (MB-DCNN) model for simultaneous skin lesion segmentation and classification. This model consists of a coarse segmentation network (coarse-SN), a mask-guided classification network (mask-CN), and an enhanced segmentation network (enhanced-SN). On one hand, the coarse-SN generates coarse lesion masks that provide a prior bootstrapping for mask-CN to help it locate and classify skin lesions accurately. On the other hand, the lesion localization maps produced by mask-CN are then fed into enhanced-SN, aiming to transfer the localization information learned by mask-CN to enhanced-SN for accurate lesion segmentation. In this way, both segmentation and classification networks mutually transfer knowledge between each other and facilitate each other in a bootstrapping way. Meanwhile, we also design a novel rank loss and jointly use it with the Dice loss in segmentation networks to address the issues caused by class imbalance and hard-easy pixel imbalance. We evaluate the proposed MB-DCNN model on the ISIC-2017 and PH2 datasets, and achieve a Jaccard index of 80.4% and 89.4% in skin lesion segmentation and an average AUC of 93.8% and 97.7% in skin lesion classification, which are superior to the performance of representative state-of-the-art skin lesion segmentation and classification methods. Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way.

Artificial Intelligence Applications in Dermatology: Where Do We Stand?

Artificial intelligence (AI) has become a progressively prevalent Research Topic in medicine and is increasingly being applied to dermatology. There is a need to understand this technology's progress to help guide and shape the future for medical care providers and recipients. We reviewed the literature to evaluate the types of publications on the subject, the specific dermatological topics addressed by AI, and the most challenging barriers to its implementation. A substantial number of original articles and commentaries have been published to date and only few detailed reviews exist. Most AI applications focus on differentiating between benign and malignant skin lesions, however; others exist pertaining to ulcers, inflammatory skin diseases, allergen exposure, dermatopathology, and gene expression profiling. Applications commonly analyze and classify images, however, other tools such as risk assessment calculators are becoming increasingly available. Although many applications are technologically feasible, important implementation barriers have been identified including systematic biases, difficulty of standardization, interpretability, and acceptance by physicians and patients alike. This review provides insight into future research needs and possibilities. There is a strong need for clinical investigation in dermatology providing evidence of success overcoming the identified barriers. With these research goals in mind, an appropriate role for AI in dermatology may be achieved in not so distant future.

Characterizing the role of dermatologists in developing artificial intelligence for assessment of skin cancer: A systematic review

BACKGROUND

The use of artificial intelligence (AI) for skin cancer assessment has been an emerging topic in dermatology. Leadership of dermatologists is necessary in defining how these technologies fit into clinical practice.

OBJECTIVE

To characterize the evolution of AI in skin cancer assessment and characterize the involvement of dermatologists in developing these technologies.

METHODS

An electronic literature search was performed using PubMed by searching machine learning or artificial intelligence combined with skin cancer or melanoma. Articles were included if they used AI for screening and diagnosis of skin cancer using data sets consisting of dermoscopic images or photographs of gross lesions.

RESULTS

Fifty-one articles were included, and 41% of these had dermatologists included as authors. Articles that included dermatologists described algorithms built with more images versus articles that did not include dermatologists (mean, 12,111 vs 660 images, respectively). In terms of underlying technology, AI used for skin cancer assessment has followed trends in the field of image recognition.

LIMITATIONS

This review focused on models described in the medical literature and did not account for those described elsewhere.

CONCLUSIONS

Greater involvement of dermatologists is needed in thinking through issues in data collection, data set biases, and applications of technology. Dermatologists can provide access to large, diverse data sets that are increasingly important for building these models.

Kernel sparse representation based model for skin lesions segmentation and classification

BACKGROUND AND OBJECTIVES

Melanoma is a dangerous kind of skin disease with a high death rate, and its prevalence has increased rapidly in recent years. Diagnosis of melanoma in a primary phase can be helpful for its cure. Due to costs for dermatology, we need an automatic system to diagnose melanoma through lesion images.

METHODS

Here, we propose a sparse representation based method for segmentation and classification of lesion images. The main idea of our framework is based on a kernel sparse representation, which produces discriminative sparse codes to represent features in a high-dimensional feature space. Our novel formulation for discriminative kernel sparse coding jointly learns a kernel-based dictionary and a linear classifier. We also present an adaptive K-SVD algorithm for kernel dictionary and classifier learning.

RESULTS

We test our approach for both segmentation and classification tasks. The evaluation results on both dermoscopic and digital datasets demonstrate our approach to be competitive as compared to the available state-of-the-art methods, with the advantage of not needing any pre-processing.

CONCLUSIONS

Our method is insensitive to noise and image conditions and can be used effectively for challenging skin lesions. Our approach is so extensive to be adapted to various medical image segmentations.

Bi-directional Dermoscopic Feature Learning and Multi-scale Consistent Decision Fusion for Skin Lesion Segmentation

Accurate segmentation of skin lesion from dermoscopic images is a crucial part of computer-aided diagnosis of melanoma. It is challenging due to the fact that dermoscopic images from different patients have non-negligible lesion variation, which causes difficulties in anatomical structure learning and consistent skin lesion delineation. In this paper, we propose a novel bi-directional dermoscopic feature learning (biDFL) framework to model the complex correlation between skin lesions and their informative context. By controlling feature information passing through two complementary directions, a substantially rich and discriminative feature representation is achieved. Specifically, we place biDFL module on the top of a CNN network to enhance high-level parsing performance. Furthermore, we propose a multi-scale consistent decision fusion (mCDF) that is capable of selectively focusing on the informative decisions generated from multiple classification layers. By analysis of the consistency of the decision at each position, mCDF automatically adjusts the reliability of decisions and thus allows a more insightful skin lesion delineation. The comprehensive experimental results show the effectiveness of the proposed method on skin lesion segmentation, achieving state-of-the-art performance consistently on two publicly available dermoscopic image databases.

Systematic review of machine learning for diagnosis and prognosis in dermatology

Background: Software systems using artificial intelligence for medical purposes have been developed in recent years. The success of deep neural networks (DNN) in 2012 in the image recognition challenge ImageNet LSVRC 2010 fueled expectations of the potential for using such systems in dermatology.Objective: To evaluate the ways in which machine learning has been utilized in dermatology to date and provide an overview of the findings in current literature on the subject.Methods: We conducted a systematic review of existing literature, identifying the literature through a systematic search of the PubMed database. Two doctors assessed screening and eligibility with respect to pre-determined inclusion and exclusion criteria.Results: A total of 2175 publications were identified, and 64 publications were included. We identified eight major categories where machine learning tools were tested in dermatology. Most systems involved image recognition tools that were primarily aimed at binary classification of malignant melanoma (MM). Short system descriptions and results of all included systems are presented in tables.Conclusions: We present a complete overview of artificial intelligence implemented in dermatology. Impressive outcomes were reported in all of the identified eight categories, but head-to-head comparison proved difficult. The many areas of dermatology where we identified machine learning tools indicate the diversity of machine learning.

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

BACKGROUND AND OBJECTIVE

Skin 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.

METHODS

The 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).

RESULTS

The 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.

CONCLUSION

To 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.

Automated detection of nonmelanoma skin cancer using digital images: a systematic review

BACKGROUND

Computer-aided diagnosis of skin lesions is a growing area of research, but its application to nonmelanoma skin cancer (NMSC) is relatively under-studied. The purpose of this review is to synthesize the research that has been conducted on automated detection of NMSC using digital images and to assess the quality of evidence for the diagnostic accuracy of these technologies.

METHODS

Eight databases (PubMed, Google Scholar, Embase, IEEE Xplore, Web of Science, SpringerLink, ScienceDirect, and the ACM Digital Library) were searched to identify diagnostic studies of NMSC using image-based machine learning models. Two reviewers independently screened eligible articles. The level of evidence of each study was evaluated using a five tier rating system, and the applicability and risk of bias of each study was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool.

RESULTS

Thirty-nine studies were reviewed. Twenty-four models were designed to detect basal cell carcinoma, two were designed to detect squamous cell carcinoma, and thirteen were designed to detect both. All studies were conducted in silico. The overall diagnostic accuracy of the classifiers, defined as concordance with histopathologic diagnosis, was high, with reported accuracies ranging from 72 to 100% and areas under the receiver operating characteristic curve ranging from 0.832 to 1. Most studies had substantial methodological limitations, but several were robustly designed and presented a high level of evidence.

CONCLUSION

Most studies of image-based NMSC classifiers report performance greater than or equal to the reported diagnostic accuracy of the average dermatologist, but relatively few studies have presented a high level of evidence. Clinical studies are needed to assess whether these technologies can feasibly be implemented as a real-time aid for clinical diagnosis of NMSC.

An Automatic Classification Method on Chronic Venous Insufficiency Images

Chronic venous insufficiency (CVI) affect a large population, and it cannot heal without doctors' interventions. However, many patients do not get the medical advisory service in time. At the same time, the doctors also need an assistant tool to classify the patients according to the severity level of CVI. We propose an automatic classification method, named CVI-classifier to help doctors and patients. In this approach, first, low-level image features are mapped into middle-level semantic features by a concept classifier, and a multi-scale semantic model is constructed to form the image representation with rich semantics. Second, a scene classifier is trained using an optimized feature subset calculated by the high-order dependency based feature selection approach, and is used to estimate CVI's severity. At last, classification accuracy, kappa coefficient, F1-score are used to evaluate classification performance. Experiments on the CVI images from 217 patients' medical records demonstrated superior performance and efficiency for CVI-classifier, with classification accuracy up to 90.92%, kappa coefficient of 0.8735 and F1score of 0.9006. This method also outperformed doctors' diagnosis (doctors rely solely on images to make judgments) with accuracy, kappa and F1-score improved by 9.11%, 0.1250 and 0.0955 respectively.

A novel cumulative level difference mean based GLDM and modified ABCD features ranked using eigenvector centrality approach for four skin lesion types classification

BACKGROUND AND OBJECTIVE

Melanoma is one of the major death causes while basal cell carcinoma (BCC) is the utmost incident skin lesion type. At their early stages, medical experts may be confused between both types with benign nevus and pigmented benign keratoses (BKL). This inspired the current study to develop an accurate automated, user-friendly skin lesion identification system.

METHODS

The current work targets a novel discrimination technique of four pre-mentioned skin lesion classes. A novel proposed texture feature, named cumulative level-difference mean (CLDM) based on the gray-level difference method (GLDM) is extracted. The asymmetry, border irregularity, color variation and diameter are summed up as the ABCD rule feature vector is originally used to classify the melanoma from benign lesions. The proposed method improved the ABCD rule to also classify BCC and BKL by using the proposed modified-ABCD feature vector. In the modified set of ABCD features, each border feature, such as compact index, fractal dimension, and edge abruptness is considered a separate feature. Then, the composite feature vector having the pre-mentioned features is ranked using the Eigenvector Centrality (ECFS) feature ranking method. The ranked features are then classified by a cubic support vector machine for different numbers of selected features.

RESULTS

The proposed CLDM texture features combined with the ranked ABCD features achieved outstanding performance to classify the four targeted classes (melanoma, BCC, nevi and BKL). The results report 100% outstanding performance of the sensitivity, accuracy and specificity per each class compared to other features when using the highest seven ranked features.

CONCLUSIONS

The proposed system established that Melanoma, BCC, nevus and BKL are efficiently classified using cubic SVM with the new feature set. In addition, the comparative studies proved the superiority of the cubic SVM to classify the four classes.

Melanoma recognition in dermoscopy images using lesion's peripheral region information

BACKGROUND AND OBJECTIVES

Melanoma is one of the most dangerous forms of skin cancer, but it has a high survival rate if diagnosed on time. The first diagnostic approach in melanoma recognition is to visually assess the lesion through dermoscopic images. Computer-aided diagnosis systems for melanoma recognition has attracted a lot of attention in the last decade and proved to be helpful in that area. Methods for skin lesions analysis usually involves three main steps: lesion segmentation, feature extraction, and features classification. Extracting highly discriminative features from the lesion has a great impact on the recognition task. In this paper, we are seeking a lesion recognition system that incorporates these highly discriminative features.

METHODS

For segmentation step, we use contour propagation model with a novel two-component speed function. In the feature extraction step, a new set of features based on peripheral information of the lesion are introduced. For this end, the peripheral area of the lesion is mapped to log-polar space using the Daugman's transformation and then a set of texture features are extracted from it. Newly introduced features do not need further segmentation of dermoscopic structures and are robust against lesion's scale, orientation, location, and shape variation. We also design the other global texture features to describe only the information from the lesion area. In the classification step, we evaluated two different schemes to prove the distinction power of the new features, one comprises linear SVM to recognize melanoma vs. nevus and the other scheme uses RUSBoost classifier to recognize melanoma vs. nevus and atypical-nevus. Sequential feature selection algorithm has been utilized in each classification scheme to rank features based on their distinction power.

RESULTS

Cross-validation experiments on the well-known PH² dataset resulted in an average of 97% for sensitivity and 100% for specificity on melanoma vs. nevus recognition task using only four features. Also, in the second classification scheme, we achieved high sensitivity and specificity values of 95% for melanoma vs. nevus and atypical nevus recognition experiments.

CONCLUSION

High values for evaluation metrics show that the proposed melanoma recognition system is superior to the other state-of-the-art algorithms, which proves the high distinction power of the newly introduced features.

Statistical Detection of Colors in Dermoscopic Images With a Texton-Based Estimation of Probabilities

Color has great diagnostic significance in dermatoscopy. Several diagnosis methods are based on the colors detected within a lesion. Malignant lesions frequently show more than three colors, whereas in benign lesions, three or fewer colors are usually observed. Black, red, white, and blue-gray are found more frequently in melanomas than in benign nevi. In this paper, a method to automatically identify the colors of a lesion is presented. A color label identification problem is proposed and solved by maximizing the posterior probability of a pixel to belong to a label, given its color value and the neighborhood color values. The main contribution of this paper is the estimation of the different terms involved in the computation of this probability. Two evaluations are performed on a database of 200 dermoscopic images. The first one evaluates if all the colors detected in a lesion are indeed present in it. The second analyzes if each pixel within a lesion is assigned the correct color label. The results show that the proposed method performs correctly and outperforms other methods, with an average F-measure of 0.89, an accuracy of 0.90, and a Spearman correlation of 0.831.

A feature fusion system for basal cell carcinoma detection through data-driven feature learning and patient profile

BACKGROUND

Basal cell carcinoma (BCC) is the most common skin cancer, which is highly damaging in its advanced stages. Computer-aided techniques provide a feasible option for early detection of BCC. However, automated BCC detection techniques immensely rely on handcrafting high-level precise features. Such features are not only computationally complex to design but can also represent a very limited aspect of the lesion characteristics. This paper proposes an automated BCC detection technique that directly learns the features from image data, eliminating the need for handcrafted feature design.

METHODS

The proposed method is composed of 2 parts. First, an unsupervised feature learning framework is proposed which attempts to learn hidden characteristics of the data including vascular patterns directly from the images. This is done through the design of a sparse autoencoder (SAE). After the unsupervised learning, we treat each of the learned kernel weights of the SAE as a filter. Convolving each filter with the lesion image yields a feature map. Feature maps are condensed to reduce the dimensionality and are further integrated with patient profile information. The overall features are then fed into a softmax classifier for BCC classification.

RESULTS

On a set of 1199 BCC images, the proposed framework achieved an area under the curve of 91.1%, while the visualization of learned features confirmed meaningful clinical interpretation of the features.

CONCLUSION

The proposed framework provides a non-invasive fast BCC detection tool that incorporates both dermoscopic lesional features and clinical patient information, without the need for complex handcrafted feature extraction.

A novel and robust Bayesian approach for segmentation of psoriasis lesions and its risk stratification

BACKGROUND AND OBJECTIVE

The need for characterization of psoriasis lesion severity is clinically valuable and vital for dermatologists since it provides a reliable and precise decision on risk assessment. The automated delineation of lesion is a prerequisite prior to characterization, which is challenging itself. Thus, this paper has two major objectives: (a) design of a segmentation system which can model by learning the lesion characteristics and this is posed as a Bayesian model; (b) develop a psoriasis risk assessment system (pRAS) by crisscrossing the blocks which drives the fundamental machine learning paradigm.

METHODS

The segmentation system uses the knowledge derived by the experts along with the features reflected by the lesions to build a Bayesian framework that helps to classify each pixel of the image into lesion vs.

BACKGROUND

Since this lesion has several stages and grades, hence the system undergoes the risk assessment to classify into five levels of severity: healthy, mild, moderate, severe and very severe. We build nine kinds of pRAS utilizing different combinations of the key blocks. These nine pRAS systems use three classifiers (Support Vector Machine (SVM), Decision Tree (DT) and Neural Network (NN)) and three feature selection techniques (Principal Component Analysis (PCA), Fisher Discriminant Ratio (FDR) and Mutual Information (MI)). The two major experiments conducted using these nine systems were: (i) selection of best system combination based on classification accuracy and (ii) understanding the reliability of the system. This leads us to computation of key system performance parameters such as: feature retaining power, aggregated feature effect and reliability index besides conventional attributes like accuracy, sensitivity, specificity.

RESULTS

Using the database used in this study consisted of 670 psoriasis images, the combination of SVM and FDR was revealed as the optimal pRAS system and yielded a classification accuracy of 99.84% using cross-validation protocol. Further, SVM-FDR system provides the reliability of 99.99% using cross-validation protocol.

CONCLUSIONS

The study demonstrates a fully novel model of segmentation embedded with risk assessment. Among all nine systems, SVM-FDR produced best results. Further, we validated our pRAS system with automatic segmented lesions against manually segmented lesions showing comparable performance.

Differential diagnosis of squamous cell carcinoma in situ using skin histopathological images

Differential diagnosis of squamous cell carcinoma in situ is of great importance for prognosis and decision making in the disease treatment procedure. Currently, differential diagnosis is done by pathologists based on examination of the histopathological slides under the microscope, which is time consuming and prone to inter and intra observer variability. In this paper, we have proposed an automated method for differential diagnosis of SCC in situ from actinic keratosis, which is known to be a precursor of squamous cell carcinoma. The process begins with epidermis segmentation and cornified layer removal. Then, epidermis axis is specified using the paths in its skeleton and the granular layer is removed via connected components analysis. Finally, diagnosis is done based on the classification result of intensity profiles extracted from lines perpendicular to the epidermis axis. The results of the study are in agreement with the gold standards provided by expert pathologists.

Computer-Aided Decision Support for Melanoma Detection Applied on Melanocytic and Nonmelanocytic Skin Lesions: A Comparison of Two Systems Based on Automatic Analysis of Dermoscopic Images

Commercially available clinical decision support systems (CDSSs) for skin cancer have been designed for the detection of melanoma only. Correct use of the systems requires expert knowledge, hampering their utility for nonexperts. Furthermore, there are no systems to detect other common skin cancer types, that is, nonmelanoma skin cancer (NMSC). As early diagnosis of skin cancer is essential, there is a need for a CDSS that is applicable to all types of skin lesions and is suitable for nonexperts. Nevus Doctor (ND) is a CDSS being developed by the authors. We here investigate ND's ability to detect both melanoma and NMSC and the opportunities for improvement. An independent test set of dermoscopic images of 870 skin lesions, including 44 melanomas and 101 NMSCs, were analysed by ND. Its sensitivity to melanoma and NMSC was compared to that of Mole Expert (ME), a commercially available CDSS, using the same set of lesions. ND and ME had similar sensitivity to melanoma. For ND at 95% melanoma sensitivity, the NMSC sensitivity was 100%, and the specificity was 12%. The melanomas misclassified by ND at 95% sensitivity were correctly classified by ME, and vice versa. ND is able to detect NMSC without sacrificing melanoma sensitivity.

A Review of the Quantification and Classification of Pigmented Skin Lesions: From Dedicated to Hand-Held Devices

In recent years, the incidence of skin cancer cases has risen, worldwide, mainly due to the prolonged exposure to harmful ultraviolet radiation. Concurrently, the computer-assisted medical diagnosis of skin cancer has undergone major advances, through an improvement in the instrument and detection technology, and the development of algorithms to process the information. Moreover, because there has been an increased need to store medical data, for monitoring, comparative and assisted-learning purposes, algorithms for data processing and storage have also become more efficient in handling the increase of data. In addition, the potential use of common mobile devices to register high-resolution images of skin lesions has also fueled the need to create real-time processing algorithms that may provide a likelihood for the development of malignancy. This last possibility allows even non-specialists to monitor and follow-up suspected skin cancer cases. In this review, we present the major steps in the pre-processing, processing and post-processing of skin lesion images, with a particular emphasis on the quantification and classification of pigmented skin lesions. We further review and outline the future challenges for the creation of minimum-feature, automated and real-time algorithms for the detection of skin cancer from images acquired via common mobile devices.

Automatic differentiation of melanoma from dysplastic nevi

Malignant melanoma causes the majority of deaths related to skin cancer. Nevertheless, it is the most treatable one, depending on its early diagnosis. The early prognosis is a challenging task for both clinicians and dermatologist, due to the characteristic similarities of melanoma with other skin lesions such as dysplastic nevi. In the past decades, several computerized lesion analysis algorithms have been proposed by the research community for detection of melanoma. These algorithms mostly focus on differentiating melanoma from benign lesions and few have considered the case of melanoma against dysplastic nevi. In this paper, we consider the most challenging task and propose an automatic framework for differentiation of melanoma from dysplastic nevi. The proposed framework also considers combination and comparison of several texture features beside the well used colour and shape features based on "ABCD" clinical rule in the literature. Focusing on dermoscopy images, we evaluate the performance of the framework using two feature extraction approaches, global and local (bag of words) and three classifiers such as support vector machine, gradient boosting and random forest. Our evaluation revealed the potential of texture features and random forest as an almost independent classifier. Using texture features and random forest for differentiation of melanoma and dysplastic nevi, the framework achieved the highest sensitivity of 98% and specificity of 70%.