scholarly journals Melanoma Detection in Dermoscopic Images using Color Features

2019 ◽  
Vol 12 (1) ◽  
pp. 107-115 ◽  
Author(s):  
Sameena Pathan ◽  
Vatsal Aggarwal ◽  
K. Gopalakrishna Prabhu ◽  
P. C. Siddalingaswamy
Keyword(s):  
Region Of Interest ◽  
Ensemble Classifiers ◽  
Dynamic Selection ◽  
Melanoma Detection ◽  
Color Features ◽  
Melanocytic Lesions ◽  
Average Accuracy ◽  
Malignant Lesions

Color is considered to be a major characteristic feature that is used for distinguishing benign and malignant melanocytic lesions. Most of malignant melanomas are characterized by the presence of six suspicious colors inspired from the ABCD dermoscopic rule. The presence of these suspicious colors histopathologically indicates the presence of melanin in the deeper layers of the epidermis and dermis. The objective of the proposed work is to evaluate the role of color features, a set of fifteen color features have been extracted from the region of interest to determine the role of color in malignancy detection. Further, a set of ensemble classifiers with dynamic selection techniques are used for classification of the extracted features, yielding an average accuracy of 87.5% for classifying benign and malignant lesions.

scholarly journals Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Joanna Jaworek-Korjakowska ◽  
Paweł Kłeczek
Keyword(s):  
Skin Lesion ◽  
Skin Lesions ◽  
New Approach ◽  
Melanoma Detection ◽  
Specificity And Sensitivity ◽  
Melanocytic Lesions ◽  
Feature Extraction And Selection ◽  
Aided Diagnosis ◽  
Automatic Algorithm

Background. Given its propensity to metastasize, and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Different computer-aided diagnosis (CAD) systems have been proposed to increase the specificity and sensitivity of melanoma detection. Although such computer programs are developed for different diagnostic algorithms, to the best of our knowledge, a system to classify different melanocytic lesions has not been proposed yet.Method. In this research we present a new approach to the classification of melanocytic lesions. This work is focused not only on categorization of skin lesions as benign or malignant but also on specifying the exact type of a skin lesion including melanoma, Clark nevus, Spitz/Reed nevus, and blue nevus. The proposed automatic algorithm contains the following steps: image enhancement, lesion segmentation, feature extraction, and selection as well as classification.Results. The algorithm has been tested on 300 dermoscopic images and achieved accuracy of 92% indicating that the proposed approach classified most of the melanocytic lesions correctly.Conclusions. A proposed system can not only help to precisely diagnose the type of the skin mole but also decrease the amount of biopsies and reduce the morbidity related to skin lesion excision.

scholarly journals Performance Analysis of Low-Level and High-Level Intuitive Features for Melanoma Detection

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 672 ◽  
Author(s):  
Muniba Ashfaq ◽  
Nasru Minallah ◽  
Zahid Ullah ◽  
Arbab Masood Ahmad ◽  
Aamir Saeed ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Skin Cancer ◽  
Region Of Interest ◽  
Textural Features ◽  
Statistical Parameters ◽  
Melanoma Detection ◽  
Color Features ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
High Level

This paper presents an intelligent approach for the detection of Melanoma—a deadly skin cancer. The first step in this direction includes the extraction of the textural features of the skin lesion along with the color features. The extracted features are used to train the Multilayer Feed-Forward Artificial Neural Networks. We evaluate the trained networks for the classification of test samples. This work entails three sets of experiments including 50 % , 70 % and 90 % of the data used for training, while the remaining 50 % , 30 % , and 10 % constitute the test sets. Haralick’s statistical parameters are computed for the extraction of textural features from the lesion. Such parameters are based on the Gray Level Co-occurrence Matrices (GLCM) with an offset of 2 , 4 , 8 , 12 , 16 , 20 , 24 and 28, each with an angle of 0 , 45 , 90 and 135 degrees, respectively. In order to distill color features, we have calculated the mean, median and standard deviation of the three color planes of the region of interest. These features are fed to an Artificial Neural Network (ANN) for the detection of skin cancer. The combination of Haralick’s parameters and color features have proven better than considering the features alone. Experimentation based on another set of features such as Asymmetry, Border irregularity, Color and Diameter (ABCD) features usually observed by dermatologists has also been demonstrated. The ‘D’ feature is however modified and named Oblongness. This feature captures the ratio between the length and the width. Furthermore, the use of modified standard deviation coupled with ABCD features improves the detection of Melanoma by an accuracy of 93.7 %

scholarly journals Effects of objects and image quality on melanoma classification using Deep Neural Networks

2020 ◽  
Author(s):  
Bilge Süheyla Akkoca-Gazioğlu ◽  
Mustafa Kamasak
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Lesion Size ◽  
Low Contrast ◽  
Melanoma Detection ◽  
Skin Cancers ◽  
Average Accuracy ◽  
Melanoma Classification ◽  
Sensitivity Specificity

Abstract Background: Melanoma is a type of skin cancer with a higher mortality compared to other types of skin cancers. Early and accurate diagnosis of melanoma has critical importance on its prognosis. Recently, deep neural network based models dominated the CAD systems for classification of the potential melanoma lesions. In clinical settings, capturing impeccable skin images is not always possible. In some cases, an external object such as a ruler is required for determination of lesion size. Sometimes, the skin images can be blurry, noisy or have low contrast. The aim of this work is to investigate the effects of external objects (ruler, hair) and image quality (blur, noise, contrast) on the classification of melanoma using commonly used Convolutional Neural Network(CNN) models.Results: Performance is analyzed using accuracy, sensitivity, specificity and precision metrics over 6 different test sets. Hair set has 89.22%, ruler set has 86% and none set has 88.81% as the best accuracy with DenseNet121 architecture. Also, DenseNet has the best average accuracy with comparing the other three models in other datasets, which are noise and blur. We find that ResNet is better for contrast dataset. We can infer that DenseNet can be used for melanoma classification with image distortions and degradations. Conclusion: In this study, we investigate the effect of ruler/hair and image blur, noise and contrast on the melanoma detection performance of four commonly used CNN models: ResNet50, DenseNet121, VGG16 and AlexNet. Melanoma images can be better recognized under contrast changes unlike the benign images, we recommend ResNet model whenever there is contrast issue. Noise significantly degrades the performance on melanoma images and the recognition rates decrease with compared to benign lesions in noisy set. DenseNet121 also works well in this set. Both classes are sensitive to blur changes and best accuracy is obtained with DenseNet model. The images contain ruler has decreased the classification accuracy and ResNet has better performance if there is ruler in an image. Hairy images have the best success rate in our system since it has the maximum number of images in total dataset. DenseNet performs better for both hairy and high quality images.

Quantitative accuracy of serotonergic neurotrans-mission imaging with high-resolution 123I SPECT

2004 ◽  
Vol 43 (06) ◽  
pp. 185-189 ◽  
Author(s):  
J. T. Kuikka
Keyword(s):  
High Resolution ◽  
Scatter Correction ◽  
Region Of Interest ◽  
Binding Potential ◽  
Partial Volume ◽  
Therapeutic Drugs ◽  
Quantitative Accuracy ◽  
Critical Error ◽  
Statistical Noise

Summary Aim: Serotonin transporter (SERT) imaging can be used to study the role of regional abnormalities of neurotransmitter release in various mental disorders and to study the mechanism of action of therapeutic drugs or drugs’ abuse. We examine the quantitative accuracy and reproducibility that can be achieved with high-resolution SPECT of serotonergic neurotransmission. Method: Binding potential (BP) of 123I labeled tracer specific for midbrain SERT was assessed in 20 healthy persons. The effects of scatter, attenuation, partial volume, mis-registration and statistical noise were estimated using phantom and human studies. Results: Without any correction, BP was underestimated by 73%. The partial volume error was the major component in this underestimation whereas the most critical error for the reproducibility was misplacement of region of interest (ROI). Conclusion: The proper ROI registration, the use of the multiple head gamma camera with transmission based scatter correction introduce more relevant results. However, due to the small dimensions of the midbrain SERT structures and poor spatial resolution of SPECT, the improvement without the partial volume correction is not great enough to restore the estimate of BP to that of the true one.

Rapid evaluation of FDG imaging alternatives using head-to-head comparisons of full ring and gamma camera based PET scanners –

2002 ◽  
Vol 41 (05) ◽  
pp. 208-213 ◽  
Author(s):  
L. M. Haslinghuis-Bajan ◽  
L. Hooft ◽  
A. van Lingen ◽  
M. van Tulder ◽  
W. Devillé ◽  
...  
Keyword(s):  
Methodological Quality ◽  
Gamma Camera ◽  
First Generation ◽  
Prospective Evaluation ◽  
First Approximation ◽  
Variable Head ◽  
Malignant Lesions ◽  
The Impact ◽  
Pet Scanners

SummaryAim: While FDG full ring PET (FRPET) has been gradually accepted in oncology, the role of the cheaper gamma camera based alternatives (GCPET) is less clear. Since technology is evolving rapidly, “tracker trials” would be most helpful to provide a first approximation of the relative merits of these alternatives. As difference in scanner sensitivity is the key variable, head-to-head comparison with FRPET is an attractive study design. This systematic review summarises such studies. Methods: Nine studies were identified until July 1, 2000. Two observers assessed the methodological quality (Cochrane criteria), and extracted data. Results: The studies comprised a variety of tumours and indications. The reported GC- and FRPET agreement for detection of malignant lesions ranged from 55 to 100%, but with methodological limitations (blinding, standardisation, limited patient spectrum). Mean lesion diameter was 2.9 cm (SD 1.8), with only about 20% <1.5 cm. The 3 studies with the highest quality reported concordances of 74-79%, for the studied lesion spectrum. Contrast at GCPET was lower than that of FRPET, contrast and detection agreement were positively related. Logistic regression analysis suggested that pre-test indicators might be used to predict FRPET-GCPET concordance. Conclusion: In spite of methodological limitations, “first generation” GCPET devices detected sufficient FRPET positive lesions to allow prospective evaluation in clinical situations where the impact of FRPET is not confined to detection of small lesions (<1.5 cm). The efficiency of head-to-head comparative studies would benefit from application in a clinically relevant patient spectrum, with proper blinding and standardisation of acquisition procedures.

Raiders Activity on the Market for Corporate Control as a Product of Coercive Entrepreneurship Evolution

2007 ◽  
pp. 80-92
Author(s):  
A. Kireev
Keyword(s):  
Corporate Control ◽  
Role Of Government ◽  
Market For Corporate Control ◽  
Basic Characteristics

The paper studies the problem of raiders activity on the market for corporate control. This activity is considered as a product of coercive entrepreneurship evolution. Their similarities and sharp distinctions are shown. The article presents the classification of raiders activity, discribes its basic characteristics and tendencies, defines the role of government in the process of its transformation.

ASPECTS IN BUILDING A COMPANY WEB-SITE AND ITS ROLE FOR THE DEVELOPMENT OF BUSINESS

2018 ◽  
pp. 117-124
Author(s):  
Petar Halachev ◽  
Victoria Radeva ◽  
Albena Nikiforova ◽  
Miglena Veneva
Keyword(s):  
Life Cycle ◽  
Web Site ◽  
The Internet ◽  
A Company ◽  
The Web ◽  
Design Construction ◽  
Made In ◽  
Site Types

This report is dedicated to the role of the web site as an important tool for presenting business on the Internet. Classification of site types has been made in terms of their application in the business and the types of structures in their construction. The Models of the Life Cycle for designing business websites are analyzed and are outlined their strengths and weaknesses. The stages in the design, construction, commissioning, and maintenance of a business website are distinguished and the activities and requirements of each stage are specified.

A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms

2020 ◽  
Vol 16 (3) ◽  
pp. 199-205
Author(s):  
Yuejun Liu ◽  
Yifei Xu ◽  
Xiangzheng Meng ◽  
Xuguang Wang ◽  
Tianxu Bai
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Region Of Interest ◽  
Classification Performance ◽  
Thyroid Diseases ◽  
Great Success ◽  
Learning Models ◽  
Good Classification Performance ◽  
Spect Images

Background: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. Objective: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. Methods: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. Results: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. Conclusion: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

Creating with Constraints

2020 ◽  
Author(s):  
Robert Hasegawa
Keyword(s):  
Visual Art ◽  
Creative Activity ◽  
Georges Perec ◽  
Gyorgy Ligeti ◽  
Raymond Queneau ◽  
Art Forms ◽  
Musical Creativity ◽  
Process Constraints

Musicians have long framed their creative activity within constraints, whether imposed externally or consciously chosen. As noted by Leonard Meyer, any style can be viewed as an ensemble of constraints, requiring the features of the artwork to conform with accepted norms. Such received stylistic constraints may be complemented by additional, voluntary limitations: for example, using only a limited palette of pitches or sounds, setting rules to govern repetition or transformation, controlling the formal layout and proportions of the work, or limiting the variety of operations involved in its creation. This chapter proposes a fourfold classification of the limits most often encountered in music creation into material (absolute and relative), formal, style/genre, and process constraints. The role of constraints as a spur and guide to musical creativity is explored in the domains of composition, improvisation, performance, and even listening, with examples drawn from contemporary composers including György Ligeti, George Aperghis, and James Tenney. Such musical constraints are comparable to self-imposed limitations in other art forms, from film (the Dogme 95 Manifesto) and visual art (Robert Morris’s Blind Time Drawings) to the writings of authors associated with the Oulipo (Ouvroir de littérature potentielle) such as Georges Perec and Raymond Queneau.

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