Automated Melanoma Diagnosis Research Articles

Automatic Diagnosis of Melanoma Based on EfficientNet and Patch Strategy

Melanoma is a fatal skin disease, and there are many challenging tasks in the detection of melanoma through neural network at this stage. We propose a new method for melanoma diagnosis based on EfficientNet and patch strategy. The diagnosis method has three stages of operation. First, Cyclegan is applied offline to synthesize the under-represented category samples from the over-represented category samples, and the original image and the synthesized samples are combined into a new training set to complete the conditional image synthesis task; second, we creatively propose the patch strategy and implement the patch algorithm, and apply the patch strategy offline to obtain the patch image of the newly merged training set; finally, the newly merged training set and the obtained patch image are sent to the classification network. Where, the model we proposed is composed of three parts: Basic Convolutional Neural Network, Auxiliary Convolutional Neural Network, and Fusion Convolutional Neural Network, and applies a weighted integration strategy. We evaluated the proposed method on the ISIC 2016 Skin Injury Challenge classification dataset. Experiments show that the patch strategy plays an important role in the field of melanoma classification, and the melanoma detection method proposed in this paper obtains an accuracy of 0.852 and an AUC value of 0.854 on the test set. This method can focus the attention of the classification network on the meaningful area of the skin lesion image through manual intervention, and can effectively solve the problem of category imbalance, thereby improving the performance of skin lesion classification.

Unintended Bias in Artificial Intelligence Driven Diagnosis of Melanoma: A Systematic Review

Melanoma remains a public health crisis, with incidence rates increasing rapidly in the past decades. Improving diagnostic accuracy to decrease misdiagnosis using Artificial intelligence (AI) continues to be documented. Unfortunately, unintended racially biased outcomes a product of lack of diversity in the dataset used, with a noted class imbalance favoring lighter vs. darker skin tone have increasingly been recognized as a problem. Resulting in noted limitations of the accuracy of the Convolutional neural network (CNN) models. CNN models are prone to biased output due to biases in the dataset used to train them. Although the incidence of melanoma is lower in patients with darker skin tone, it is associated with a worse prognosis than in Caucasians, underscoring the need for accurate early diagnosis in these patients. Our objective in this systematic review was to assess to what degree race/ethnicity, specifically Black/ African American patient cohort were included in training datasets used in generating machine learning algorithms for automated melanoma diagnosis. Our review documents the fact that there is a remarkable lack of and inconsistent reporting of patient demographics especially race/ethnicity with notable under-representation of patients of color, highlighting a currently unmet critical need of lack of diversity in the publicly available skin image datasets. These publicly available skin image datasets, are an inherently unbalanced unintentionally biased datasets from which AI models created for the diagnosis of melanoma skin cancer have restricted applicability to real life clinical scenarios and limited population representation preventing generalizability to the community as a whole.

Computer Aided Diagnosis of Melanoma Using Deep Neural Networks and Game Theory: Application on Dermoscopic Images of Skin Lesions.

Early detection of melanoma remains a daily challenge due to the increasing number of cases and the lack of dermatologists. Thus, AI-assisted diagnosis is considered as a possible solution for this issue. Despite the great advances brought by deep learning and especially convolutional neural networks (CNNs), computer-aided diagnosis (CAD) systems are still not used in clinical practice. This may be explained by the dermatologist's fear of being misled by a false negative and the assimilation of CNNs to a "black box", making their decision process difficult to understand by a non-expert. Decision theory, especially game theory, is a potential solution as it focuses on identifying the best decision option that maximizes the decision-maker's expected utility. This study presents a new framework for automated melanoma diagnosis. Pursuing the goal of improving the performance of existing systems, our approach also attempts to bring more transparency in the decision process. The proposed framework includes a multi-class CNN and six binary CNNs assimilated to players. The players' strategies is to first cluster the pigmented lesions (melanoma, nevus, and benign keratosis), using the introduced method of evaluating the confidence of the predictions, into confidence level (confident, medium, uncertain). Then, a subset of players has the strategy to refine the diagnosis for difficult lesions with medium and uncertain prediction. We used EfficientNetB5 as the backbone of our networks and evaluated our approach on the public ISIC dataset consisting of 8917 lesions: melanoma (1113), nevi (6705) and benign keratosis (1099). The proposed framework achieved an area under the receiver operating curve (AUROC) of 0.93 for melanoma, 0.96 for nevus and 0.97 for benign keratosis. Furthermore, our approach outperformed existing methods in this task, improving the balanced accuracy (BACC) of the best compared method from 77% to 86%. These results suggest that our framework provides an effective and explainable decision-making strategy. This approach could help dermatologists in their clinical practice for patients with atypical and difficult-to-diagnose pigmented lesions. We also believe that our system could serve as a didactic tool for less experienced dermatologists.

Multi-scale aggregation networks with flexible receptive fields for melanoma segmentation

Skin lesions Segmentation in dermatoscopic images is an important step in automatic diagnosis of melanoma. Due to various of shape and size in skin lesions, this is still a challenging task. In this paper, a multi-scale aggregation network with flexible receptive fields for melanoma segmentation is proposed. We propose the channel-attention dilated convolution module (CDM) to take full of context information. CDM can flexibly adjust the receptive field to capture multi-scale information in response to lesions of various shapes. In addition, we develop aggregation interaction modules to integrate features of adjacent layers of the encoder, which can reduce differences in input features of skip connection and suppress the noise from redundant information. Sub-pixel convolution is adopted as up-sampling operation for improving the fine granularity of detail features. The proposed model is trained on the ISIC 2018 skin segmentation dataset. Experiments and comparison studies are made, and demonstrate that our method produces better segmentation results than other state-of-the-art models in the evaluation metrics of accuracy (Acc), dice coefficient (Dice), Jaccard index (Jac), sensitivity (Sen) and specificity(Spe). Our method achieved 95.7% Acc, 86.4% Dice coefficient, 81.6% Jac, 91.5% Sen, 96.7% Spe, and could well adapt to the scale changes of lesions.

Cascaded context enhancement network for automatic skin lesion segmentation

Skin lesion segmentation is an important step for automatic melanoma diagnosis. Due to the non-negligible diversity of lesions from different patients, extracting powerful context for fine-grained semantic segmentation is still challenging today. Although the deep convolutional neural networks (CNNs) have made significant improvements on skin lesion segmentation, they often fail to reserve the spatial details and long-range dependencies context due to consecutive convolution striding and pooling operations inside CNNs. In this paper, we formulate a cascaded context enhancement neural network for automatic skin lesion segmentation. A new cascaded context aggregation (CCA) module with a gate-based information integration approach is proposed to sequentially and selectively aggregate original image and multi-level features from the encoder sub-network. The generated context is further utilized to guide discriminative features extraction by the designed context-guided local affinity (CGL) module. Furthermore, an auxiliary loss is added to the CCA module for refining the prediction. In our work, we evaluate our approach on four public skin dermoscopy image datasets. The proposed method achieves the Jaccard Index (JA) of 87.1%, 80.3%, 84.3%, and 86.6% on ISIC-2016, ISIC-2017, ISIC-2018, and PH2 datasets, which show highly competitive performance with other state-of-the-art models respectively.

Appending Global to Local features for Skin Lesion Classification on Dermoscpic Images

Skin cancer is the deadliest diseases compared with all other kinds of cancer. In this paper various pre- and post-treatments are proposed for improving automated melanoma diagnosis of dermoscopy images. At first pre-processing have done to exclude unwanted parts, a new triple-A segmentation proposes to extract lesion according to their histogram patterns. Lastly, suggest appending process with testing many factors for superior detection decision. This paper offers a novel approach with testing different detection rules: first system used fuzzy rules based on a different features, a second test has been done by modeled local colours with bag-of-features classifier. Then proposed adding lesion shape on two previous systems as their global form in the first one, while distributing it and appending with local colour patches in the second system. For each case, different features; various colour models, and many other parameters are examined to decide which settings are more discriminating. Evaluates performance of each method has carried out on (ISIC2019 Challenge) dermoscopic database. The novel processes with their a specific parameters are rising the classification accuracy to 98.26%.

Melanoma Recognition by Fusing Convolutional Blocks and Dynamic Routing between Capsules.

Simple SummaryThe early treatment of skin cancer can effectively reduce mortality rates. Recently, automatic melanoma diagnosis from skin images has gained attention, which was mainly encouraged by the well-known challenge developed by the International Skin Imaging Collaboration project. The majority of contestant submitted Convolutional Neural Network based solutions. However, this type of model presents disadvantages. As a consequence, Dynamic Routing between Capsules has been proposed to overcome such limitations. The aim of our proposal was to assess the advantages of combining both architectures. An extensive experimental study showed the proposal significantly outperformed state-of-the-art models, achieving 166% higher predictive performance compared to ResNet in non-dermoscopic images. In addition, the pixels activated during prediction were shown, which allows to assess the rationale to give such a conclusion. Finally, more research should be conducted in order to demonstrate the potential of this neural network architecture in other areas.Skin cancer is one of the most common types of cancers in the world, with melanoma being the most lethal form. Automatic melanoma diagnosis from skin images has recently gained attention within the machine learning community, due to the complexity involved. In the past few years, convolutional neural network models have been commonly used to approach this issue. This type of model, however, presents disadvantages that sometimes hamper its application in real-world situations, e.g., the construction of transformation-invariant models and their inability to consider spatial hierarchies between entities within an image. Recently, Dynamic Routing between Capsules architecture (CapsNet) has been proposed to overcome such limitations. This work is aimed at proposing a new architecture which combines convolutional blocks with a customized CapsNet architecture, allowing for the extraction of richer abstract features. This architecture uses high-quality skin lesion images, and a hyper-tuning of the main parameters is performed in order to ensure effective learning under limited training data. An extensive experimental study on eleven image datasets was conducted where the proposal significantly outperformed several state-of-the-art models. Finally, predictions made by the model were validated through the application of two modern model-agnostic interpretation tools.

Melanoma classification employing inter neighbor statistical color and mean order pattern texture feature

Automatic melanoma diagnosis is very important to lower the mortality rate by detecting the disease in earlier stages and accurate diagnosis. The main aim of this paper is to improve the classification accuracy of melanoma using a proposed novel color and texture feature descriptor. Initially, the skin lesion area of input dermoscopy image is segmented using a convolutional neural network-based U-Net algorithm. Then extract discriminate color, texture and combined color-texture features with the help of proposed Inter Neighbor Statistical Color Feature (INSCF), Inter Neighbor Mean Order Pattern (INMOP) and Inter Neighbor Statistical Color Mean Order Pattern (INSCMOP) by incorporating inter color channel values. This proposed feature descriptors are capable to discriminate the detailed information derived from spatial inter-chromatic texture patterns of different channels within a region. Finally, three classifiers namely, K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM) are used to classify the skin lesion in a dermoscopic image as benign lesion or melanoma. Experimental results indicate that the proposed INSCMOP with Random Forest classifier achieves the highest classification performance based on accuracy of 93.27% for ISIC 2016 dataset, 86% for ISIC 2017 dataset and 92.31% for ISBI 2019 dataset. Moreover, comparing the proposed method with other systems shows that this approach has an excellent performance in melanoma classification. In this research, without any manual interaction, the classification process is performed in an automated way. It would set up a valuable assistance for dermatologist in clinical practice.

Diagnosis of Melanoma Based on the Sparse Auto-Encoder for Feature Extraction

Aims: Skin cancer is a fairly critical disease all over the world and especially in Western countries and America. However, if it is perceived and treated early, it is quite often curable. The main risk factors for melanoma are exposure to UV rays, the presence of many moles, and heredity. For this reason, this work focuses on the issue of automatic diagnosis of melanoma. The aim is to extract significant features from pixels of the images based on an unsupervised deep learning technique which is the sparse autoencoder method.
 Methodology: A preprocessing phase is required to remove the artifacts and enhance the contrast of the images before proceeding with the feature extraction. Once the characteristics are extracted automatically, the support vector machine classifier and the k-nearest neighbors are applied for the classification phase. The objective is to differentiate between 3 categories: melanoma, suspected case, and non-melanoma. Finally, the PH2 database is used to test the proposed approaches (200 images are presented in this dataset: 80 atypical nevi, 80 common nevi, and 40 melanoma).
 Results: The obtained results in terms of specificity, accuracy, and sensitivity present noticeable performances with the support vector machine classifier (achieved 94 % overall accuracy) and the k-nearest neighbors (92 %).
 Conclusion: This study's experimental findings showed that the best performance was obtained by the approach based on a deep sparse autoencoder combined with support vector machine.

Convolutional neural networks for the automatic diagnosis of melanoma: An extensive experimental study.

Melanoma is the type of skin cancer with the highest levels of mortality, and it is more dangerous because it can spread to other parts of the body if not caught and treated early. Melanoma diagnosis is a complex task, even for expert dermatologists, mainly due to the great variety of morphologies in moles of patients. Accordingly, the automatic diagnosis of melanoma is a task that poses the challenge of developing efficient computational methods that ease the diagnostic and, therefore, aid dermatologists in decision-making. In this work, an extensive analysis was conducted, aiming at assessing and illustrating the effectiveness of convolutional neural networks in coping with this complex task. To achieve this objective, twelve well-known convolutional network models were evaluated on eleven public image datasets. The experimental study comprised five phases, where first it was analyzed the sensitivity of the models regarding the optimization algorithm used for their training, and then it was analyzed the impact in performance when using different techniques such as cost-sensitive learning, data augmentation and transfer learning. The conducted study confirmed the usefulness, effectiveness and robustness of different convolutional architectures in solving melanoma diagnosis problem. Also, important guidelines to researchers working on this area were provided, easing the selection of both the proper convolutional model and technique according the characteristics of data.

Automatic melanoma diagnosis framework based on common image feature learning

Automatic melanoma diagnosis based on image processing can give more objective results. To facilitate examination for patients at home, we propose a new automatic melanoma diagnosis framework based on common images. Firstly, we use illumination assessment based on variational framework for Retinex (VFR) to filter the images with illumination problem caused by variation of capturing cameras viewpoint and ambient light. Secondly, the GrabCut algorithm based on colour difference is used to segment lesion area. It can complete segmentation automatically and efficiently. Thirdly, we use convolutional neural network (CNN) to extract high-level features and choose support vector machine (SVM) classifier to complete melanoma classification. Compared to hand-craft features, CNN can acquire deep information of images. Because of the lack of medical images, the SVM classifier is better than other classifiers. Finally, we validated our approach from different perspectives and the accuracy is increased by about 5% over other methods.

Automatic melanoma diagnosis framework based on common image feature learning

Automatic melanoma diagnosis based on image processing can give more objective results. To facilitate examination for patients at home, we propose a new automatic melanoma diagnosis framework based on common images. Firstly, we use illumination assessment based on variational framework for Retinex (VFR) to filter the images with illumination problem caused by variation of capturing cameras viewpoint and ambient light. Secondly, the GrabCut algorithm based on colour difference is used to segment lesion area. It can complete segmentation automatically and efficiently. Thirdly, we use convolutional neural network (CNN) to extract high-level features and choose support vector machine (SVM) classifier to complete melanoma classification. Compared to hand-craft features, CNN can acquire deep information of images. Because of the lack of medical images, the SVM classifier is better than other classifiers. Finally, we validated our approach from different perspectives and the accuracy is increased by about 5% over other methods.

Performance Improvement of Automated Melanoma Diagnosis System by Data Augmentation

Color information is an important tool for diagnosing melanoma. In this study, we used a hyper-spectral imager (HSI), which can measure color information in detail, to develop an automated melanoma diagnosis system. In recent years, the effectiveness of deep learning has become more widely accepted in the field of image recognition. We therefore integrated the deep convolutional neural network with transfer learning into our system. We tried data augmentation to demonstrate how our system improves diagnostic performance. 283 melanoma lesions and 336 non-melanoma lesions were used for the analysis. The data measured by HSI, called the hyperspectral data (HSD), were converted to a single-wavelength image averaged over plus or minus 3 nm. We used GoogLeNet which was pre-trained by ImageNet and then was transferred to analyze the HSD. In the transfer learning, we used not only the original HSD but also artificial augmentation dataset to improve the melanoma classification performance of GoogLeNet. Since GoogLeNet requires three-channel images as input, three wavelengths were selected from those single-wavelength images and assigned to three channels in wavelength order from short to long. The sensitivity and specificity of our system were estimated by 5-fold cross-val-idation. The results of a combination of 530, 560, and 590 nm (combination A) and 500, 620, and 740 nm (com-bination B) were compared. We also compared the diagnostic performance with and without the data augmentation. All images were augmented by inverting the image vertically and/or horizontally. Without data augmentation, the respective sensitivity and specificity of our system were 77.4% and 75.6% for combination A and 73.1% and 80.6% for combination B. With data augmentation, these numbers improved to 79.9% and 82.4% for combination A and 76.7% and 82.2% for combination B. From these results, we conclude that the diagnostic performance of our system has been improved by data augmentation. Furthermore, our system suc-ceeds to differentiate melanoma with a sensitivity of almost 80%. (Less)

Deep Learning From Limited Training Data: Novel Segmentation and Ensemble Algorithms Applied to Automatic Melanoma Diagnosis

Deep learning algorithms often require thousands of training instances to generalize well. The presented research demonstrates a novel algorithm, Predict-Evaluate-Correct K-fold (PECK), that trains ensembles to learn well from limited data. The PECK algorithm is used to train a deep ensemble on 153 non-dermoscopic lesion images, significantly outperforming prior publications and state-of-the-art methods trained and evaluated on the same dataset. The PECK algorithm merges deep convolutional neural networks with support vector machine and random forest classifiers to achieve an introspective learning method. Where the ensemble is organized hierarchically, deeper layers are provided not only more training folds, but also the predictions of previous layers. Subsequent classifiers then learn and correct the previous layer errors by training on the original data with injected predictions for new data folds. In addition to the PECK algorithm, a novel segmentation algorithm, Synthesis and Convergence of Intermediate Decaying Omnigradients (SCIDOG), is developed to accurately detect lesion contours in non-dermoscopic images, even in the presence of significant noise, hair, and fuzzy lesion boundaries. As SCIDOG is a non-learning algorithm, it is unhindered by data quantity limitations. The automatic and precise segmentations that SCIDOG produces allows for the extraction of 1,812 lesion features that quantify shape, color and texture. These morphological features are used in conjunction with convolutional neural network predictions for training the PECK ensemble. The combination of SCIDOG and PECK algorithms are used to diagnose melanomas and benign nevi through automatic digital image analysis on the MED-NODE dataset. Evaluated using 10-fold cross validation, the proposed methods achieve significantly increased diagnostic capability over the best prior methods.

Hybrid fully convolutional networks‐based skin lesion segmentation and melanoma detection using deep feature

AbstractFully convolutional networks (FCNs) take the input of arbitrary size and produce correspondingly sized output with efficient inference and learning. The automatic diagnosis of melanoma is very essential for reducing the mortality rate by identifying the disease in earlier stages. A two‐stage framework is used for implementing the melanoma detection, segmentation of skin lesion, and identification of melanoma lesions. Two FCNs based on VGG‐16 and GoogLeNet are incorporated for improving the segmentation accuracy. A hybrid framework is used for incorporating these two FCNs. The classification is done by extracting the feature from segmented lesion by using deep residual network and a hand‐crafted feature. Classification is done by support vector machine. The performance analysis of our framework gives a promising accuracy, that is, 0.8892 for classification in ISBI 2016 dataset and 0.853 for ISIC 2017 dataset.

Towards the Performance Investigation of Automatic Melanoma Diagnosis Applications

Melanoma is a type of skin cancer, one of the fatal diseases, that appears as an abnormal growth of skin cells and the lesion part often looks like a mole on the skin. Early detection of melanoma from skin lesion by means of screening is an important step towards a reduction in mortality. For this purpose, numerous automatic melanoma diagnosis models based on image processing and machine learning techniques are available for computer-based applications (CBA) and smartphone-based applications (SBA). Since, the smartphones are available as most accessible and easiest methods with built-in camera option, SBA are preferred over CBA. In this paper, we explored the available literature and highlighted the challenges of SBA in terms of execution time due to the limited computing power of smartphones. To resolve this issue of storage of the smartphones, we proposed to develop an SBA that can seamlessly process the image data on the cloud instead of local hardware of the smartphone. Therefore, we designed a study to build a machine learning model of melanoma diagnosis to measure the time taken in preprocessing, segmentation, feature extraction, and classification on the cloud and compared the results with the processing time on the smartphone’s local machine. The results showed there is a significant difference of p value <0.001 on the average processing time taken on both environments. As the processing on the cloud is more efficient. The findings of the proposed research will be helpful for the developers to decide the processing platform while developing smartphone applications for automatic melanoma diagnosis.

Multiscale Morphological Reconstruction for Hair Removal in Dermoscopy Images

The automatic diagnosis of melanoma is usually affected by the noise that is often included in an image, during the acquisition stage or by superficial factors such as hair. Specifically, hair on the surface of a lesion can cause enough distortion, resulting in an erroneous diagnosis of the region of interest. To solve this issue, several techniques have been presented to detect hair on the surface of a dermoscopy image and substitute a surface approximation for these regions. Nonetheless, the existing methods are prone to false detections or reconstructions that are not uniform, demand high computing resources and modify the textures of important characteristics. Therefore, we proposed a method that detects the hairs by means of a convolution of the image with a kernel belonging to the first derivative of the Gaussian function and replaces the hairs using a multiscale morphological reconstruction. In addition, we integrated a refining stage that contributes to maintaining the quality of the patterns on the lesion. We used 36 dermoscopy images in the evaluation, which included a total of 586 hairs that were automatically detected with the proposed process and validated with their respective manual segmentations. Our results showed sensitivity and specificity performance measurements of 94.14% and 99.89%, respective.

Patient acceptance and trust in automated computer-assisted diagnosis of melanoma with dermatofluoroscopy.

Automated computer-guided diagnostic procedures are increasingly being integrated into patient care. However, in contrast to the increasing application of automation, patient acceptance and trust in such technologies has rarely been studied. Automated diagnosis of melanoma with dermatofluoroscopy was recently approved by regulatory agencies. The objective of this study is to assess patient acceptance and trust in automated melanoma diagnosis with dermatofluoroscopy. We examined 140 pigmented skin lesions with dermatofluoroscopy as part of a prospective clinical study. Four weeks after their examination with dermatofluoroscopy, we contacted 100 patients with a 10-item questionnaire addressing their acceptance and trust in this technology on a five-point visual analogue scale. A "high" to "very high" level of patient acceptance and trust in dermatofluoroscopy was found in 74% of responders. Most patients agreed that computer-assisted diagnoses are trustworthy and may generally improve the diagnostic performance of physicians. However, all responders insisted on the interpretation of computer-assisted diagnoses by a physician and frequently rejected the idea of computers completely replacing physicians. Patient acceptance and trust in dermatofluoroscopy was high. Patients clearly supported the use of automated, computer-assisted diagnostics as an adjunct to the physicians' examination.

Automatic diagnosis of melanoma using linear and nonlinear features from digital image.

Melanoma is the most serious type of skin cancer and causes more deaths than other forms of skin cancer. It is a tiny small malignant mole that is usually black or brown but also appears in other color patterns. Early detection of melanoma is key as this is the time period when it is most likely to be cured. Due to the advancement of smartphone technology, automatic and efficient detection of melanoma mole using a smartphone is an active area of research. In this study, we developed an automatic melanoma diagnosis system using images captured from the digital camera. Our work differs from other studies in the area of segmentation of melanoma region and consideration of non-linear features for classification of malignant and benign melanoma. In this paper, a combination of Otsu and k-means clustering segmentation methods are applied to automatically segment and extract the borders of affected region with satisfactory accuracy. Also, we explored and extracted different non-linear features along with color and texture features existed in literature from the lesion mole. The effectiveness of these features was predicted with a machine learning model consisting of five different classifiers. Our model predicted the diagnosis of mole with an accuracy of 89.7%, i.e., around 10% more than reported results by others (to the best of our knowledge) with the same database.

Enriched dermoscopic-structure-based cad system for melanoma diagnosis

Computer-Aided Diagnosis (CAD) systems for melanoma detection have received a lot of attention during the last decades because of the utmost importance of detecting this type of skin cancer in its early stages. However, despite of the many research efforts devoted to this matter, these systems are not used yet in everyday clinical practice. Very likely, this is due to two main reasons: 1) the accuracy of the systems is not high enough; and 2) they simply provide a parallel diagnosis that actually does not help to the doctors (as long as there is no way to interpret it). In this paper, we propose a novel approach that aims to provide the doctor with an enriched diagnosis. Specifically, we rely on a dermoscopic-structure-based soft segmentation to design a set of structure-specific classifiers. Each individual structure-specific classifier is trained to distinguish benign lesions from melanomas just paying attention to one type of dermoscopic structure. Then, the outputs of the individual classifiers are combined by a means of the Bayesian method that, besides the final diagnosis, provide the doctor with additional valuable information, such as the opinions of the individual structure-specific experts and the uncertainty of the diagnosis. The results in terms of the features selected for the structure-specific classifiers are consistent with the expert insights. Furthermore, regarding the automatic melanoma diagnosis problem, the proposed method has been assessed on two different datasets, and the experimental results revealed that the proposed system clearly outperforms other methods in two datasets and compares well with the official submissions of the ISBI 2016 challenge on melanoma detection. Moreover, the system performance is equivalent to that of a well-known dermoscopy expert and its combination with the human diagnosis surpasses the human performance.