Computational Diagnosis of Skin Lesions from Dermoscopic Images using Combined Features

There has been an alarming increase in the number of skin cancer cases worldwide in recent years, which has raised interest in computational systems for automatic diagnosis to assist early diagnosis and prevention. Feature extraction to describe skin lesions is a challenging research area due to the difficulty in selecting meaningful features. The main objective of this work is to find the best combination of features, based on shape properties, colour variation and texture analysis, to be extracted using various feature extraction methods. Several colour spaces are used for the extraction of both colour- and texture-related features. Different categories of classifiers were adopted to evaluate the proposed feature extraction step, and several feature selection algorithms were compared for the classification of skin lesions. The developed skin lesion computational diagnosis system was applied to a set of 1104 dermoscopic images using a cross-validation procedure. The best results were obtained by an optimum-path forest classifier with very promising results. The proposed system achieved an accuracy of 92.3%, sensitivity of 87.5% and specificity of 97.1% when the full set of features was used. Furthermore, it achieved an accuracy of 91.6%, sensitivity of 87% and specificity of 96.2%, when 50 features were selected using a correlation-based feature selection algorithm

Skin Lesion Computational Diagnosis of Dermoscopic Images: Ensemble Models based on Input Feature Manipulation

Background and objectives: The number of deaths worldwide due to melanoma has risen in recent times, in part because melanoma is the most aggressive type of skin cancer. Computational systems have been developed to assist dermatologists in early diagnosis of skin cancer, or even to monitor skin lesions. However, there still remains a challenge to improve classifiers for the diagnosis of such skin lesions. The main objective of this article is to evaluate different ensemble classification models based on input feature manipulation to diagnose skin lesions. Methods: Input feature manipulation processes are based on feature subset selections from shape properties, colour variation and texture analysis to generate diversity for the ensemble models. Three subset selection models are presented here: (1) a subset selection model based on specific feature groups, (2) a correlation-based subset selection model, and (3) a subset selection model based on feature selection algorithms. Each ensemble classification model is generated using an optimum-path forest classifier and integrated with a majority voting strategy. The proposed models were applied on a set of 1104 dermoscopic images using a cross-validation procedure. Results: The best results were obtained by the first ensemble classification model that generates a feature subset ensemble based on specific feature groups. The skin lesion diagnosis computational system achieved 94.3% accuracy, 91.8% sensitivity and 96.7% specificity. Conclusions: The input feature manipulation process based on specific feature subsets generated the greatest diversity for the ensemble classification model with very promising results

A computational approach for detecting pigmented skin lesions in macroscopic images

Skin cancer is considered one of the most common types of cancer in several countries and its incidencerate has increased in recent years. Computational methods have been developed to assist dermatologistsin early diagnosis of skin cancer. Computational analysis of skin lesion images has become a challengingresearch area due to the difficulty in discerning some types of skin lesions. A novel computational approachis presented for extracting skin lesion features from images based on asymmetry, border, colourand texture analysis, in order to diagnose skin lesion types. The approach is based on an anisotropic diffusionfilter, an active contour model without edges and a support vector machine. Experiments wereperformed regarding the segmentation and classification of pigmented skin lesions in macroscopic images,with the results obtained being very promising

Selection of Reference Genes for Transcriptional Analysis of Edible Tubers of Potato (Solanum tuberosum L.)

Potato (Solanum tuberosum) yield has increased dramatically over the last 50 years and this has been achieved by a combination of improved agronomy and biotechnology efforts. Gene studies are taking place to improve new qualities and develop new cultivars. Reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) is a bench-marking analytical tool for gene expression analysis, but its accuracy is highly dependent on a reliable normalization strategy of an invariant reference genes. For this reason, the goal of this work was to select and validate reference genes for transcriptional analysis of edible tubers of potato. To do so, RT-qPCR primers were designed for ten genes with relatively stable expression in potato tubers as observed in RNA-Seq experiments. Primers were designed across exon boundaries to avoid genomic DNA contamination. Differences were observed in the ranking of candidate genes identified by geNorm, NormFinder and BestKeeper algorithms. The ranks determined by geNorm and NormFinder were very similar and for all samples the most stable candidates were C2, exocyst complex component sec3 (SEC3) and ATCUL3/ ATCUL3A/CUL3/CUL3A (CUL3A). According to BestKeeper, the importin alpha and ubiquitin-associated/ts-n genes were the most stable. Three genes were selected as reference genes for potato edible tubers in RT-qPCR studies. The first one, called C2, was selected in common by NormFinder and geNorm, the second one is SEC3, selected by NormFinder, and the third one is CUL3A, selected by geNorm. Appropriate reference genes identified in this work will help to improve the accuracy of gene expression quantification analyses by taking into account differences that may be observed in RNA quality or reverse transcription efficiency across the samples

Computational methods for the image segmentation of pigmented skin lesions: a review

Background and objectives: Because skin cancer affects millions of people worldwide, computational methods for the segmentation of pigmented skin lesions in images have been developed in order to assist dermatologists in their diagnosis. This paper aims to present a review of the current methods, and outline a comparative analysis with regards to several of the fundamental steps of image processing, such as image acquisition, pre-processing and segmentation. Methods: Techniques that have been proposed to achieve these tasks were identified and reviewed. As to the image segmentation task, the techniques were classified according to their principle. Results: The techniques employed in each step are explained, and their strengths and weaknesses are identified. In addition, several of the reviewed techniques are applied to macroscopic and dermoscopy images in order to exemplify their results. Conclusions: The image segmentation of skin lesions has been addressed successfully in many studies; however, there is a demand for new methodologies in order to improve the efficiency

Caracterização de textura em imagens de lesões de pele por máquina de vetor de suporte

Due to the increased incidence of skin cancer, computational methods using intelligent systems have been developed to aid dermatologists in the diagnosis of skin lesions. This paper proposes a method to classify the texture, considering that it is an important feature in identification of lesions. For this is defined a feature vector with the fractal dimension of images through the box-counting method (BCM), which were used by SVM to classify the texture of the lesions in non-irregular or irregular, where it obtained 72.84% of accuracy

Meninas.comp: computación para niñas en escuelas primarias de Brasil

The Computing field has a gender diversity gap, with female participation much lower when compared to men. In this context, several activities have been developed to include more women in the field of Computing, and the Meninas.comp project has been working to introduce Computing to girls in elementary schools in Brasilia, the capital of Brazil. This article has two objectives: i) to present the activities of the Meninas.comp project for girls in elementary schools; ii) to present a mapping of the literature on computational activities with a focus on girls in elementary schools in Brazil. The systematic literature mapping found publications reporting a large variety of activities, such as unplugged computing, game development, card games, programming classes, competitions, lectures, and workshops. From the Meninas.comp project, this article highlights a smart garden developed by female elementary school students.El campo de la informática tiene una brecha de diversidad de género, con una participación femenina mucho menor en comparación con los hombres. Al respecto, se han desarrollado varias actividades para incluir a más mujeres en el campo de la Computación. El proyecto Meninas.comp ha trabajado en Informática para niñas de escuelas primarias en Brasilia, capital de Brasil. En este contexto, este artículo tiene dos objetivos: i) presentar las actividades del proyecto Meninas.comp en escuelas primarias de niñas; ii) presentar un mapeo de la literatura sobre actividades computacionales con enfoque en niñas de escuelas primarias en Brasil. El mapeo sistemático de literatura encontró publicaciones que informan una gran variedad de actividades, como computación desconectada, desarrollo de juegos, juegos de cartas, clases de programación, concursos, conferencias y talleres. Del proyecto Meninas. comp, este artículo destaca un jardín inteligente desarrollado por alumnas de primaria

Classificação de assimetria em lesões de pele por meio de imagens usando máquina de vetor de suporte

The increased occurrence of cancer cases over the years and the importance of prevention work motivated the development of this work. It aim is help the dermatologist in the diagnosis of skin lesions, providing information about the characteristics of asymmetry of ABCD rule (Asymmetry, Edge, Color and Diameter), widely used in the initial examination to determine if a lesion is malignant or no. To do so, are extracted from scanned images of the asymmetric features of the lesion, and classified as symmetrical or asymmetrical, through a Support Vector Machine (SVM). This process is used an anisotropic diffusion filter to soften the image and the model of active contour without edge (Chan-vese) to segment them. Thus, allows to define the contour of the lesion so that can be extracted their characteristics of asymmetry, used as input in the smart classifier