Hybrid Melanoma Detection Using Neural Network Classifier difficult task. This article illustrates a method that involves the combination of the existing ABCD rule (involving symmetry, edge, color and diameter detections) and the Gray Level Co-occurrence Matrix (GLCM) as well as the local binary model ( LBP) to identify malignant melanoma skin. lesion with greater precision. Several steps such as image acquisition technique, pre-processing (RGB to HSV) and segmentation are undertaken for skin feature selection criteria to successfully determine lesion characteristics for classification purposes. Textural features such as contrast, energy, entropy, and homogeneity of the skin lesion are extracted using LBP and GLCM for discriminating the two cases (melanoma and non-melanoma). Back propagation neural network (BPN) is used for the classification process. Say no to plagiarism. Get a tailor-made essay on “Why violent video games should not be banned”?Get the original essayIntroductionCancer is an abnormal growth of cells that tends to proliferate uncontrollably and, in some cases, metastasize. It can involve any tissue in the body and takes many different forms in each area of the body. It is a group of more than 100 different and distinct diseases, varying in severity. Most cancers are named after the type of cell or organ in which they start. Among the different types of cancer, skin cancer is one of the most common. The annual cost of treating skin cancers in the United States is estimated at $8.1 billion, including approximately $4.8 billion for non-melanoma skin cancers and $3.3 billion for melanoma. According to studies published by the American Cancer Society in 2018, at least one death is due to melanoma every hour. Despite the high mortality, if diagnosed and detected at an early stage, it can be treated. Early detection techniques involve dermoscopic techniques and image processing techniques such as ABCD rule, pattern feature analysis and many others. Despite these existing methods, diagnosing early-stage melanoma accurately is a difficult task. In the past, a mainly computer-assisted pattern classification system for dermoscopy images was used. This method was found to be accurate and had several parametric values for the skin lesion, but it had a downside of high computational complexity. Later, a less supervised approach was desired for detection. This has led to the use of SVM (Support Vector Machine) which involves a large number of training sets. Unlike this ABCD rule, we see that it has a much lower number of training sets and lower computational complexity if we take and compare only its best performing score parameters. Besides the aforementioned approaches, melanoma detection using Delaunay triangulation is also a popular method that gives decent accuracy results without its slow convergence rate. Recently, a detection methodology involving histogram of oriented gradient (HOG) and histogram of oriented lines (HOL) was used to extract the bagged texture and color features of the skin lesion.This method has a major disadvantage: it only allows good results to be obtained if there is high contrast between the lesioned areas. In comparison, gray level co-occurrence matrix (GLCM) coupled with local binary pattern (LBP) extracts texture features from skin lesions and uses them for melanoma diagnosis. We see that it obtains good results whatever the contrast, unlike the histogram method. Methodology Existing methodologies involve only ABCD parameters which are used for classification or extracted texture features using GLCM and LBP methods. The proposed method is a combination of ABCD rule and GLCM coupled with LBP using a back-propagation neural network as the final classifier. This approach aims to minimize classification errors and improve the accuracy of the process. Initially, using the segmentation procedure, the skin lesion is extracted using the adaptive threshold algorithm. Then, feature extraction takes place, giving the texture and color parameters of the image. The input dermoscopic image is subjected to preprocessing techniques such as color space conversion. This step consists of extracting the characteristic chromatic characteristics of the image subjected to observation. This involves converting to find the hue, saturation and value (HSV) of the image. This gives information such as color identity, purity and intensity. A local binary pattern operator is described as a gray-level invariant texture living in a local neighborhood. Then, the local binary pattern operator labels the pixel of an image by thresholding the 3X3 neighborhood of each pixel and concatenates the results binomially to create a manifold. The LBP operator helps classify the image region as uniform or non-uniform. GLCM considers the relationship between two nearby pixels called reference pixel and neighboring pixel. It is composed of a matrix comprising rows and columns equivalent to the number of gray levels of the image. By assigning intensity factors and distance between pixels to the elements of the matrix, various texture characteristics can be calculated. Among the 14 texture features, the best performing texture features with the highest discriminant factor are considered, such as contrast, energy, homogeneity and entropy, which are extracted using of GLCM and LBP. These parameters are compared to non-melanoma cases for comparison purposes. Melanoma and non-melanoma Meanwhile, the extracted features are used for ABCD classification which involves assigning specific score values to features such as area, perimeter, major axis length, minor axis length, solidity. , center of gravity and orientation. These values, which significantly represent the symmetry, border, color and diameter of the skin lesion, are used to compare melanoma and non-melanoma cases. The data from ABCD methodology and GLCM methodology is fed to the back-propagation neural network which is used to act as a classifier. The experimental data includes 120 dermoscopic images of which 20 are test images and 100 are training images. The features of the training sample with the assigned target vectors are fed into the BPN model created for supervised training to obtain network parameters such as node biases and weighting factors. Finally, the features of the test image are simulated with a trained network to make a decision on the stagesskin lesions, such as malignant or benign. Related work There are several systems for the identification of melanoma in dermoscopy images. The classification of skin lesions is done in the overall region extracted from the dermoscopy image. In the overall method, the segmentation process is carried out using a simple adaptive thresholding algorithm. The GLCM matrix is used to extract texture features in four different orientation angles. There are several systems for identifying melanoma in dermoscopy images. The classification of the skin lesion is done in the overall region extracted from the dermoscopy image. In the overall method, the segmentation process is carried out using a simple adaptive thresholding algorithm. The GLCM matrix is used to extract texture features in four different orientation angles. There are several systems for identifying melanoma in dermoscopy images. The classification of skin lesions is done in the overall region extracted from the dermoscopy image. In the overall method, the segmentation process is carried out using a simple adaptive thresholding algorithm. The GLCM matrix is used to extract texture features in four different orientation angles. There are several systems for the identification of melanoma in dermoscopy images. The classification of skin lesions is done in the overall region extracted from the dermoscopy image. In the overall method, the segmentation process is carried out using a simple adaptive thresholding algorithm. The GLCM matrix is used to extract texture features in four different orientation angles. There are several systems for identifying melanoma in dermoscopy images. The classification of the skin lesion is done in the overall region extracted from the dermoscopy image. In the overall method, the segmentation process is carried out using a simple adaptive thresholding algorithm. The GLCM matrix is used to extract texture features in four different orientation angles. In this case, the segmentation is performed using a Gaussian filter and the Otsu method is used to calculate the overall threshold. Feature extraction is performed using a 2D fast Fourier transform, a 2D discrete cosine transform, a pigment network feature, and color. Classification is performed using SVM-RBF for melanoma detection. This article presents an automated melanoma diagnosis method applied to a set of dermoscopy images. The extracted features are based on gray level co-occurrence matrix (GLCM) and using a multi-layer perceptual classifier (MLP) to classify between melanocytic nevi and malignant melanoma. The MLP classifier was proposed with two different techniques in the training and testing process: automatic MLP and traditional MLP. The results indicated that texture analysis is a useful method for discriminating melanocytic skin tumors with high accuracy. In 2011, Daniel Ruiz, Vicente Berenguer, Antonio Soriano and Belen Sanchez proposed types of ANN classifiers, which relate to multi-layer perception, a Bayesian classifier and the k-nearest neighbors algorithm. These methods work independently and also in combination, creating a collaborative decision support system. The classification rates obtained are around 87%. An Internet-based melanoma screening system has been proposed in which the server is open to the public to download dermoscopy images. In this system, digital dermoscopic image can be uploaded by.

Essay / Hybrid Melanoma Detection Using Neural Network Classifier difficult task. This article illustrates a method that involves the combination of the existing ABCD rule (involving symmetry, edge, color and diameter detections) and the Gray Level Co-occurrence Matrix (GLCM) as well as the local binary model ( LBP) to identify malignant melanoma skin. lesion with greater precision. Several steps such as image acquisition technique, pre-processing (RGB to HSV) and segmentation are undertaken for skin feature selection criteria to successfully determine lesion characteristics for classification purposes. Textural features such as contrast, energy, entropy, and homogeneity of the skin lesion are extracted using LBP and GLCM for discriminating the two cases (melanoma and non-melanoma). Back propagation neural network (BPN) is used for the classification process. Say no to plagiarism. Get a tailor-made essay on “Why violent video games should not be banned”?Get the original essayIntroductionCancer is an abnormal growth of cells that tends to proliferate uncontrollably and, in some cases, metastasize. It can involve any tissue in the body and takes many different forms in each area of the body. It is a group of more than 100 different and distinct diseases, varying in severity. Most cancers are named after the type of cell or organ in which they start. Among the different types of cancer, skin cancer is one of the most common. The annual cost of treating skin cancers in the United States is estimated at $8.1 billion, including approximately $4.8 billion for non-melanoma skin cancers and $3.3 billion for melanoma. According to studies published by the American Cancer Society in 2018, at least one death is due to melanoma every hour. Despite the high mortality, if diagnosed and detected at an early stage, it can be treated. Early detection techniques involve dermoscopic techniques and image processing techniques such as ABCD rule, pattern feature analysis and many others. Despite these existing methods, diagnosing early-stage melanoma accurately is a difficult task. In the past, a mainly computer-assisted pattern classification system for dermoscopy images was used. This method was found to be accurate and had several parametric values for the skin lesion, but it had a downside of high computational complexity. Later, a less supervised approach was desired for detection. This has led to the use of SVM (Support Vector Machine) which involves a large number of training sets. Unlike this ABCD rule, we see that it has a much lower number of training sets and lower computational complexity if we take and compare only its best performing score parameters. Besides the aforementioned approaches, melanoma detection using Delaunay triangulation is also a popular method that gives decent accuracy results without its slow convergence rate. Recently, a detection methodology involving histogram of oriented gradient (HOG) and histogram of oriented lines (HOL) was used to extract the bagged texture and color features of the skin lesion.This method has a major disadvantage: it only allows good results to be obtained if there is high contrast between the lesioned areas. In comparison, gray level co-occurrence matrix (GLCM) coupled with local binary pattern (LBP) extracts texture features from skin lesions and uses them for melanoma diagnosis. We see that it obtains good results whatever the contrast, unlike the histogram method. Methodology Existing methodologies involve only ABCD parameters which are used for classification or extracted texture features using GLCM and LBP methods. The proposed method is a combination of ABCD rule and GLCM coupled with LBP using a back-propagation neural network as the final classifier. This approach aims to minimize classification errors and improve the accuracy of the process. Initially, using the segmentation procedure, the skin lesion is extracted using the adaptive threshold algorithm. Then, feature extraction takes place, giving the texture and color parameters of the image. The input dermoscopic image is subjected to preprocessing techniques such as color space conversion. This step consists of extracting the characteristic chromatic characteristics of the image subjected to observation. This involves converting to find the hue, saturation and value (HSV) of the image. This gives information such as color identity, purity and intensity. A local binary pattern operator is described as a gray-level invariant texture living in a local neighborhood. Then, the local binary pattern operator labels the pixel of an image by thresholding the 3X3 neighborhood of each pixel and concatenates the results binomially to create a manifold. The LBP operator helps classify the image region as uniform or non-uniform. GLCM considers the relationship between two nearby pixels called reference pixel and neighboring pixel. It is composed of a matrix comprising rows and columns equivalent to the number of gray levels of the image. By assigning intensity factors and distance between pixels to the elements of the matrix, various texture characteristics can be calculated. Among the 14 texture features, the best performing texture features with the highest discriminant factor are considered, such as contrast, energy, homogeneity and entropy, which are extracted using of GLCM and LBP. These parameters are compared to non-melanoma cases for comparison purposes. Melanoma and non-melanoma Meanwhile, the extracted features are used for ABCD classification which involves assigning specific score values to features such as area, perimeter, major axis length, minor axis length, solidity. , center of gravity and orientation. These values, which significantly represent the symmetry, border, color and diameter of the skin lesion, are used to compare melanoma and non-melanoma cases. The data from ABCD methodology and GLCM methodology is fed to the back-propagation neural network which is used to act as a classifier. The experimental data includes 120 dermoscopic images of which 20 are test images and 100 are training images. The features of the training sample with the assigned target vectors are fed into the BPN model created for supervised training to obtain network parameters such as node biases and weighting factors. Finally, the features of the test image are simulated with a trained network to make a decision on the stagesskin lesions, such as malignant or benign. Related work There are several systems for the identification of melanoma in dermoscopy images. The classification of skin lesions is done in the overall region extracted from the dermoscopy image. In the overall method, the segmentation process is carried out using a simple adaptive thresholding algorithm. The GLCM matrix is used to extract texture features in four different orientation angles. There are several systems for identifying melanoma in dermoscopy images. The classification of the skin lesion is done in the overall region extracted from the dermoscopy image. In the overall method, the segmentation process is carried out using a simple adaptive thresholding algorithm. The GLCM matrix is used to extract texture features in four different orientation angles. There are several systems for identifying melanoma in dermoscopy images. The classification of skin lesions is done in the overall region extracted from the dermoscopy image. In the overall method, the segmentation process is carried out using a simple adaptive thresholding algorithm. The GLCM matrix is used to extract texture features in four different orientation angles. There are several systems for the identification of melanoma in dermoscopy images. The classification of skin lesions is done in the overall region extracted from the dermoscopy image. In the overall method, the segmentation process is carried out using a simple adaptive thresholding algorithm. The GLCM matrix is used to extract texture features in four different orientation angles. There are several systems for identifying melanoma in dermoscopy images. The classification of the skin lesion is done in the overall region extracted from the dermoscopy image. In the overall method, the segmentation process is carried out using a simple adaptive thresholding algorithm. The GLCM matrix is used to extract texture features in four different orientation angles. In this case, the segmentation is performed using a Gaussian filter and the Otsu method is used to calculate the overall threshold. Feature extraction is performed using a 2D fast Fourier transform, a 2D discrete cosine transform, a pigment network feature, and color. Classification is performed using SVM-RBF for melanoma detection. This article presents an automated melanoma diagnosis method applied to a set of dermoscopy images. The extracted features are based on gray level co-occurrence matrix (GLCM) and using a multi-layer perceptual classifier (MLP) to classify between melanocytic nevi and malignant melanoma. The MLP classifier was proposed with two different techniques in the training and testing process: automatic MLP and traditional MLP. The results indicated that texture analysis is a useful method for discriminating melanocytic skin tumors with high accuracy. In 2011, Daniel Ruiz, Vicente Berenguer, Antonio Soriano and Belen Sanchez proposed types of ANN classifiers, which relate to multi-layer perception, a Bayesian classifier and the k-nearest neighbors algorithm. These methods work independently and also in combination, creating a collaborative decision support system. The classification rates obtained are around 87%. An Internet-based melanoma screening system has been proposed in which the server is open to the public to download dermoscopy images. In this system, digital dermoscopic image can be uploaded by.

Navigation

« Prev 1 2 3 4 5 Next »

Get In Touch