Melanoma is a curable aggressive skin cancer if detected early. Typically, the diagnosis involves initial screening with subsequent biopsy and histopathological examination if necessary. Computer aided diagnosis offers an objective score that is independent of clinical experience and the potential to lower the workload of a dermatologist. In the recent past, success of deep learning algorithms in the field of general computer vision has motivated successful application of supervised deep learning methods in computer aided melanoma recognition. However, large quantities of labeled images are required to make further improvements on the supervised method. A good annotation generally requires clinical and histological confirmation, which requires significant effort. In an attempt to alleviate this constraint, we propose to use categorical generative adversarial network to automatically learn the feature representation of dermoscopy images in an unsupervised and semi-supervised manner. Thorough experiments on ISIC 2016 skin lesion chal- lenge demonstrate that the proposed feature learning method has achieved an average precision score of 0.424 with only 140 labeled images. Moreover, the proposed method is also capable of generating real-world like dermoscopy images.
In the pre-deep learning era, hand-crafted features based on shape @cite_12 @cite_26 , color @cite_15 @cite_10 @cite_7 and texture @cite_44 @cite_8 of skin lesion played a key role in automatic melanoma classification systems. The features used are very broad and are generally adopted from the traditional computer vision literature. For example, used a wavelet decomposition based texture feature extraction method in conjunction with other geometric and border based features of the lesions @cite_16 . combined asymmetry, colour and border assessment features with a Support Vector Machine (SVM) classifier to automate melanoma detection @cite_57 . Integration of local and global features is also exploited in some computer-aided diagnosis systems @cite_31 @cite_6 . Among various ways of fusing features from different sources, bag of visual word (BoW) is probably the most popular method to aggregate the low-level features into so-called mid-level features which are more robust to image variations. This has been adopted in many dermoscopy image classification works @cite_13 @cite_38 @cite_33 . A more in-depth review of these traditional techniques can be found in @cite_17 .
@cite_13 @cite_38 @cite_31 @cite_26 @cite_33 @cite_7 @cite_8 @cite_6 @cite_44 @cite_57 @cite_15 @cite_16 @cite_10 @cite_12 @cite_17
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With a rapidly increasing incidence of melanoma skin cancer, there is a need for decision support systems to detect it in its early stages, which would lead to better decisions in treating it successfully. However, developing such systems is still a challenging task for researchers. Several Computer Aided-Diagnosis (CAD) systems have been proposed in the last two decades to increase the accuracy of melanoma detection. Image feature extraction is a critical step in differentiating between melanoma and normal skin lesions. In this paper, we propose to improve a bag-of-words approach by combining features consisting of the color histogram and first order moments with the Histogram of Oriented Gradients (HOG). Experimental results show that the proposed technique significantly improves the detection accuracy, with an average sensitivity of 91 and specificity of 85 . The proposed system was validated on a dataset of 200 medically annotated images (40 melanomas and 160 non-melanomas) obtained from the database of the Hospital Pedro Hispano. [1]. A Computer Aided-Diagnosis (CAD) System for melanoma diagnosis usually makes use of different types of features to characterize the lesions. The features are often combined into a single vector that can belong to a high dimensional space (early fusion). However, it is not clear if this is the optimal strategy and works on other fields have shown that early fusion has some limitations. In this work, we address this issue and investigate which is the best approach to combine different features comparing early and late fusion. Experiments carried on the datasets PH2 (single source) and EDRA (multi source) show that late fusion performs better, leading to classification scores of Sensitivity = 98 and Specificity = 90 (PH 2 ) and Sensitivity = 83 and Specificity = 76 (EDRA). Automatic and reliable diagnosis of skin cancer, as a smartphone application, is of great interest. Among different types of skin cancers, melanoma is the most dangerous one which causes most deaths. Meanwhile, melanoma is curable if it were diagnosed in its early stages. In this paper we propose an efficient system for prescreening of pigmented skin lesions for malignancy using general-purpose digital cameras. These images can be captured by a smartphone or a digital camera. This could be beneficial in different applications, such as computer aided diagnosis and telemedicine applications. It could assist dermatologists, or smartphone users, evaluate risk of suspicious moles. The proposed method enhances borders and extracts a broad set of dermatologically important features. These discriminative features allow classification of lesions into two groups of melanoma and benign. This method is computationally appropriate as a smartphone application. Experimental results show that our proposed method is superior in diagnosis accuracy compared to state-of-the-art methods. Early detection of malignant melanoma can be lifesaving (1–3) and may be aided by digital image analysis (4–10). Certain colors are associated with melanoma in dermoscopy images, including blue and gray in veil structures, dark gray, blue and black in peppering associated with regression, white in scar-like regression, erythema associated with inflammation, brown in dots, globules and blotches as well as the pigment network, and black in dots (11, 12). Automatic detection of colors in clinical images (13–18) and dermoscopy images (5, 6, 19) using a number of techniques has been presented, but delineation of the range of benign vs. malignant coloring in digital dermoscopy images has not previously been attempted. In previous research, a data-driven relative color histogram analysis technique was examined for determining colors characteristic of melanomas in clinical images (16, 17). The use of relative color (13), in which the background color is subtracted from the image color, has been proposed as a technique to avoid color distortion in the imaging process as well as a method of accounting for variations in normal skin color. This research presents the relative color histogram technique that is applied to discrimination of benign and malignant colors in dermoscopy images. An overview of the color feature analysis algorithm used in the present study is presented in Fig. 1. Experiments presented here compare detection of clustered colors, as seen in the dermoscopy image lesion structures listed above, with detection of colors without regard to structure. Additional experiments are performed to evaluate the histogram analysis technique based on diagnostic accuracy achieved with: (1) different numbers of lesion images used as training data to identify melanoma colors and (2) different areas of the lesion selected for color analysis. Fig. 1 Overview of algorithm for color feature calculations and lesion discrimination. The remaining sections of this paper include: (1) the techniques used to calculate the percent melanoma color and color clustering ratio features, (2) experiments performed for melanoma and benign lesion discrimination, (3) experimental results, (4) discussion, and (5) conclusions from the study. This paper presents a novel computer-aided diagnosis system for melanoma. The novelty lies in the optimized selection and integration of features derived from textural, border-based, and geometrical properties of the melanoma lesion. The texture features are derived from using wavelet-decomposition, the border features are derived from constructing a boundary-series model of the lesion border and analyzing it in spatial and frequency domains, and the geometry features are derived from shape indexes. The optimized selection of features is achieved by using the gain-ratio method, which is shown to be computationally efficient for melanoma diagnosis application. Classification is done through the use of four classifiers; namely, support vector machine, random forest, logistic model tree, and hidden naive Bayes. The proposed diagnostic system is applied on a set of 289 dermoscopy images (114 malignant, 175 benign) partitioned into train, validation, and test image sets. The system achieves an accuracy of 91.26 and area under curve value of 0.937, when 23 features are used. Other important findings include 1) the clear advantage gained in complementing texture with border and geometry features, compared to using texture information only, and 2) higher contribution of texture features than border-based features in the optimized feature set. Abstract Computerized image analysis methods for dermoscopy are primarily of great interest and benefit, as it provides significant information about the lesion, which can be of pertinence for the clinicians and a stand-alone warning implement. Computer-based diagnostic systems require dedicated image processing algorithms to provide mathematical descriptions of the suspected regions, such systems hold a great potential in oncology. In this paper, we have performed a review of the state of art techniques used in computer-aided diagnostic systems, by giving the domain aspects of melanoma followed by the prominent techniques used in each of the steps. The steps include dermoscopic image pre-processing, segmentation, extraction and selection of peculiar features, and relegation of skin lesions. The paper also presents cognizance to judge the consequentiality of every methodology utilized in the literature, in addition to the corresponding results obtained in this perspective. The inadequacies and the future research directions are accentuated.