IEEE Organizations related to Melanoma

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Conferences related to Melanoma

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2021 IEEE Pulsed Power Conference (PPC)

The Pulsed Power Conference is held on a biannual basis and serves as the principal forum forthe exchange of information on pulsed power technology and engineering.


2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2020 IEEE International Conference on Plasma Science (ICOPS)

IEEE International Conference on Plasma Science (ICOPS) is an annual conference coordinated by the Plasma Science and Application Committee (PSAC) of the IEEE Nuclear & Plasma Sciences Society.


2020 IEEE International Power Modulator and High Voltage Conference (IPMHVC)

This conference provides an exchange of technical topics in the fields of Solid State Modulators and Switches, Breakdown and Insulation, Compact Pulsed Power Systems, High Voltage Design, High Power Microwaves, Biological Applications, Analytical Methods and Modeling, and Accelerators.


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Periodicals related to Melanoma

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Most published Xplore authors for Melanoma

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Xplore Articles related to Melanoma

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Image Analysis Model For Skin Disease Detection: Framework

2018 7th International Conference on Computer and Communication Engineering (ICCCE), 2018

Skin disease is the most common disease in the world. The diagnosis of the skin disease requires a high level of expertise and accuracy for dermatologist, so computer aided skin disease diagnosis model is proposed to provide more objective and reliable solution. Many researches were done to help detect skin diseases like skin cancer and tumor skin. But the accurate ...


Convolutional Neural Network Algorithm with Parameterized Activation Function for Melanoma Classification

2018 International Conference on Information and Communication Technology Convergence (ICTC), 2018

Melanoma is the deadliest form of skin cancer, which is considered one of the most common human malignancies in the world. Early detection of this disease can affect the result of the illness and improve the chance of surviving. The tremendous improvement of deep learning algorithms in image recognition tasks promises a great success for medical image analysis, in particular, ...


Automatic Diagnosis of Skin Cancer Using Neural Networks

2019 11th International Symposium on Advanced Topics in Electrical Engineering (ATEE), 2019

The paper presents an automated classification system for melanoma diagnosis. It is based on a convolutional neural network that is fed with images of skin lesions which are preprocessed in advance. The preprocess step is necessary for reducing the number of artifacts present in the images and hence, maximize the classification accuracy. The proposed solution is based on the training ...


A hardware Implementation of OTSU Thresholding Method for Skin Cancer Image Segmentation

2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), 2019

Skin Cancer represents currently a grave health problem and the search for an exact clinical diagnosis has been a continuous concern for dermatologists and an important field study for many researchers, especially on the Digital Image Processing area, in which segmentation is a basic process and produces permanently an effective result for next process. Commonly Image Segmentation is following up ...


Deep Learning based Melanoma Detection from Dermoscopic Images

2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), 2019

Melanoma which occurs with non-healing DNA degradation in melanocyte cells, is the most deadly type of skin cancers. Importantly, it can be identified for a treatment before it spreads to other tissues, i.e., early diagnosis. To identify, a specialist visually inspects whether the suspected lesion is melanoma or not. However, due to different education and experience levels of specialists or ...


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Educational Resources on Melanoma

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IEEE.tv Videos

No IEEE.tv Videos are currently tagged "Melanoma"

IEEE-USA E-Books

  • Image Analysis Model For Skin Disease Detection: Framework

    Skin disease is the most common disease in the world. The diagnosis of the skin disease requires a high level of expertise and accuracy for dermatologist, so computer aided skin disease diagnosis model is proposed to provide more objective and reliable solution. Many researches were done to help detect skin diseases like skin cancer and tumor skin. But the accurate recognition of the disease is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between Disease and non-Disease area, etc. This paper aims to detect skin disease from the skin image and to analyze this image by applying filter to remove noise or unwanted things, convert the image to grey to help in the processing and get the useful information. This help to give evidence for any type of skin disease and illustrate emergency orientation. Analysis result of this study can support doctor to help in initial diagnoses and to know the type of disease. That is compatible with skin and to avoid side effects.

  • Convolutional Neural Network Algorithm with Parameterized Activation Function for Melanoma Classification

    Melanoma is the deadliest form of skin cancer, which is considered one of the most common human malignancies in the world. Early detection of this disease can affect the result of the illness and improve the chance of surviving. The tremendous improvement of deep learning algorithms in image recognition tasks promises a great success for medical image analysis, in particular, melanoma classification for skin cancer diagnosis. Activation functions play an important role in the performance of deep neural networks for image recognition problems as well as medical image classification. In this paper, we show that a deep neural network model with adaptive piecewise linear units can achieve excellent results in melanoma recognition. Experimental results show that a convolutional neural network model with parameterized adaptive piecewise linear units outperforms the same network with different activation functions in the melanoma classification task. All experiments are performed using the data provided in International Skin Imaging Collaboration (ISIC) 2018 Skin Lesion Analysis towards Melanoma Detection.

  • Automatic Diagnosis of Skin Cancer Using Neural Networks

    The paper presents an automated classification system for melanoma diagnosis. It is based on a convolutional neural network that is fed with images of skin lesions which are preprocessed in advance. The preprocess step is necessary for reducing the number of artifacts present in the images and hence, maximize the classification accuracy. The proposed solution is based on the training of the neural network with a series of preprocessed clinical images, classifying them in two categories: benign or malignant.

  • A hardware Implementation of OTSU Thresholding Method for Skin Cancer Image Segmentation

    Skin Cancer represents currently a grave health problem and the search for an exact clinical diagnosis has been a continuous concern for dermatologists and an important field study for many researchers, especially on the Digital Image Processing area, in which segmentation is a basic process and produces permanently an effective result for next process. Commonly Image Segmentation is following up software approach in systems, but for better processing speed, hardware implementation favors in comparison with software implementation. Based on the standard version of OTSU Thresholding that present an automatic threshold selection region-based segmentation method, we propose, in this paper, an Adaptive OTSU Thresholding hardware (AOTh) implementation. We are conducting a study on the effectiveness of the implemented approach, based on two measures of performance, the “Jaccard coefficient” and “CPU time” for a set of benchmark images.

  • Deep Learning based Melanoma Detection from Dermoscopic Images

    Melanoma which occurs with non-healing DNA degradation in melanocyte cells, is the most deadly type of skin cancers. Importantly, it can be identified for a treatment before it spreads to other tissues, i.e., early diagnosis. To identify, a specialist visually inspects whether the suspected lesion is melanoma or not. However, due to different education and experience levels of specialists or as a result of the patient not being in a facility that is specialized to this area, the problem of “subjectivity” arises, and a good visual investigation accuracy may not always be achieved. Therefore, there is a significant need for automatic detection tools and systems. In this study, a method based on deep learning for automatic detection of melanoma from dermoscopic images is proposed. The developed system is tested with a large dataset and encouraging results are obtained.

  • Artificial Intelligence Image Recognition Inhealthcare

    Skin melanoma is one of the most dangerous cancer tumor forms. The main reason is not only it(s aggressiveness but also uncontrolled growth. The death(s number due to this cancer tumor rapidly increased in last 20 years, doubled every 10-15 years and shows 7% annual growth. The MIPT(s Special-Purpose Digital Systems Laboratory proposes a new convolutional neural network based algorithm of skin deceases identification. This method provides to reach the classification accuracy of 94% at dermoscopic pictures of skin decease and about 88% at microscopic ones. The highlight of the method is limited training set working ability. Whereas overwhelming variety of neural network-based algorithms demands 10 000 and more pictures to train, algorithm proposed could be operated with a training set of 1000 pictures with a declared accuracy. In February 2018 the laboratory started free web service in testing mode available at https://skincheckup.online.

  • Skin Lesion Semantic Segmentation Using Convolutional Encoder Decoder Architecture

    Computerized skin lesion analysis system often used segmentation technique which is always advantageous due skin lesion unequal size, shape and border. In this research paper deep convolutional encoder decoder neural network is proposed for pixel-wise semantic segmentation of dermascopic image of skin lesion. Proposed segmentation network consist sequence of encoder block and subsequent decoder block and final output is fed to pixel-wise classification layer. The proposed segmentation network is trained and tested on publicly available dermatology images obtained from challenge host by International Skin Imaging Collaboration (ISIC) in the beginning 2016 on “Skin Lesion Analysis towards Melanoma Detection”. This challenge consists of 900 training sample of dermascopic skin images and 379 for evaluation. Experimental results of proposed segmentation network are very encouraging compare to state of the art result which achieves jaccard index value of 0.928.

  • Classifying Pump-Probe Images of Melanocytic Lesions Using the WEYL Transform

    Diagnosis of melanoma is fraught with uncertainty, and discordance rates among physicians remain high because of the lack of a definitive criterion. Motivated by this challenge, this paper first introduces the Patch Weyl transform (PWT), a 2-dimensional variant of the Weyl transform. It then presents a method for classifying pump-probe images of melanocytic lesions based on the PWT coefficients. Performance of the PWT coefficients is shown to be superior to classification based on baseline intensity, on standard descriptors such as the Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP), and on coefficients derived from PCA and Fourier representations of the data.

  • Identification of Melanoma in Dermoscopy Images Using Image Processing Algorithms

    Skin cancer is the most common of all human cancers and is always misunderstood with other kind of skin diseases, so accurate early detection of skin cancer is essential. The main objective of this paper is to segment the lesion and identify melanoma from dermoscopy images. A total of 170 dermoscopy images are used in this research. Firstly, the input images are enhanced for better processing then, the lesion portion is segmented from the enhanced image by two methods 1. Otsu thresholding 2. Morphological operations. The descriptive features are extracted from the segmented lesion. The extracted feature values are used to compute the Total Dermatascopy Score (TDS), which is used to find the presence or absence of melanoma in dermoscopy images. Classification accuracy is calculated to assess the performance of the proposed algorithm.

  • Skin lesion border detection in dermoscopic images

    Malignant Melanoma is a disease that death rate is quite high among the skin cancer types. It is very important to do the correct diagnosis of this cancer type whose danger level is high. Asymmetric shape, heterogeneous color, bigger diameter than 6 mm and untidy borders that are used to diagnose melanoma by the dermatologists visually are important parameters. In this study, an automatic skin lesion segmentation and border detection processes are carried out in order to assist the physician for diagnosis and reduce the probable human errors. PH2dataset that includes 200 lesion images has been used. The segmented reference lesion images which are shared in the dataset have been used for performance analysis of the proposed segmentation and border detection system. The average volume overlap (VO) and Dice coefficient (DC) are achieved as 0.75±6.2 and 0.88±3.4 respectively. As a result, it is aimed to make easier the diagnosis of melanoma type skin cancer and prevent deaths that arisen out of the result of the wrong diagnosis by using the proposed method.



Standards related to Melanoma

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