scholarly journals A multi-tree genetic programming representation for melanoma detection using local and global features

2020 ◽  
Author(s):  
Q Ul Ain ◽  
Harith Al-Sahaf ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Skin Cancer ◽  
Genetic Programming ◽  
Early Stage ◽  
Extraction Methods ◽  
Image Features ◽  
Color Variation ◽  
Genetic Operators ◽  
Global Features ◽  
Melanoma Detection ◽  
Performance Gains

© Springer Nature Switzerland AG 2018. Melanoma is the deadliest type of skin cancer that accounts for nearly 75% of deaths associated with it. However, survival rate is high, if diagnosed at an early stage. This study develops a novel classification approach to melanoma detection using a multi-tree genetic programming (GP) method. Existing approaches have employed various feature extraction methods to extract features from skin cancer images, where these different types of features are used individually for skin cancer image classification. However they remain unable to use all these features together in a meaningful way to achieve performance gains. In this work, Local Binary Pattern is used to extract local information from gray and color images. Moreover, to capture the global information, color variation among the lesion and skin regions, and geometrical border shape features are extracted. Genetic operators such as crossover and mutation are designed accordingly to fit the objectives of our proposed method. The performance of the proposed method is assessed using two skin image datasets and compared with six commonly used classification algorithms as well as the single tree GP method. The results show that the proposed method significantly outperformed all these classification methods. Being interpretable, this method may help dermatologist identify prominent skin image features, specific to a type of skin cancer.

scholarly journals A multi-tree genetic programming representation for melanoma detection using local and global features

2020 ◽  
Author(s):  
Q Ul Ain ◽  
Harith Al-Sahaf ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Skin Cancer ◽  
Genetic Programming ◽  
Early Stage ◽  
Extraction Methods ◽  
Image Features ◽  
Color Variation ◽  
Genetic Operators ◽  
Global Features ◽  
Melanoma Detection ◽  
Performance Gains

© Springer Nature Switzerland AG 2018. Melanoma is the deadliest type of skin cancer that accounts for nearly 75% of deaths associated with it. However, survival rate is high, if diagnosed at an early stage. This study develops a novel classification approach to melanoma detection using a multi-tree genetic programming (GP) method. Existing approaches have employed various feature extraction methods to extract features from skin cancer images, where these different types of features are used individually for skin cancer image classification. However they remain unable to use all these features together in a meaningful way to achieve performance gains. In this work, Local Binary Pattern is used to extract local information from gray and color images. Moreover, to capture the global information, color variation among the lesion and skin regions, and geometrical border shape features are extracted. Genetic operators such as crossover and mutation are designed accordingly to fit the objectives of our proposed method. The performance of the proposed method is assessed using two skin image datasets and compared with six commonly used classification algorithms as well as the single tree GP method. The results show that the proposed method significantly outperformed all these classification methods. Being interpretable, this method may help dermatologist identify prominent skin image features, specific to a type of skin cancer.

scholarly journals Multi-tree Genetic Programming with A New Fitness Function for Melanoma Detection

2020 ◽  
Author(s):  
Q Ul Ain ◽  
Bing Xue ◽  
Harith Al-Sahaf ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Fitness Function ◽  
Local Binary Patterns ◽  
Skin Lesions ◽  
Color Variation ◽  
Classification Model ◽  
Genetic Operators ◽  
Melanoma Detection ◽  
Multiple Trees ◽  
Performance Gains

© 2019 IEEE. The occurrence of malignant melanoma had enormously increased since past decades. For accurate detection and classification, not only discriminative features are required but a properly designed model to combine these features effectively is also needed. In this study, the multi-tree representation of genetic programming (GP) has been utilised to effectively combine different types of features and evolve a classification model for the task of melanoma detection. Local binary patterns have been used to extract pixel-level informative features. For incorporating the properties of ABCD (asymmetrical property, border shape, color variation and geometrical characteristics) rule of dermoscopy, various features have been used to include local and global information of the skin lesions. To meet the requirements of the proposed multi-tree GP representation, genetic operators such as crossover and mutation are designed accordingly. Moreover, a new weighted fitness function is designed to evolve better GP individuals having multiple trees influencing each other's performance during the evolution, in order to get overall performance gains. The performance of the new method is checked on two benchmark skin image datasets, and compared with six widely used classification algorithms and the single tree GP method. The experimental results have shown that the proposed method has significantly outperformed all these classification methods.

scholarly journals Multi-tree Genetic Programming with A New Fitness Function for Melanoma Detection

2020 ◽  
Author(s):  
Q Ul Ain ◽  
Bing Xue ◽  
Harith Al-Sahaf ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Fitness Function ◽  
Local Binary Patterns ◽  
Skin Lesions ◽  
Color Variation ◽  
Classification Model ◽  
Genetic Operators ◽  
Melanoma Detection ◽  
Multiple Trees ◽  
Performance Gains

© 2019 IEEE. The occurrence of malignant melanoma had enormously increased since past decades. For accurate detection and classification, not only discriminative features are required but a properly designed model to combine these features effectively is also needed. In this study, the multi-tree representation of genetic programming (GP) has been utilised to effectively combine different types of features and evolve a classification model for the task of melanoma detection. Local binary patterns have been used to extract pixel-level informative features. For incorporating the properties of ABCD (asymmetrical property, border shape, color variation and geometrical characteristics) rule of dermoscopy, various features have been used to include local and global information of the skin lesions. To meet the requirements of the proposed multi-tree GP representation, genetic operators such as crossover and mutation are designed accordingly. Moreover, a new weighted fitness function is designed to evolve better GP individuals having multiple trees influencing each other's performance during the evolution, in order to get overall performance gains. The performance of the new method is checked on two benchmark skin image datasets, and compared with six widely used classification algorithms and the single tree GP method. The experimental results have shown that the proposed method has significantly outperformed all these classification methods.

scholarly journals Skin Cancer Recognition Using SVM Image Processing Technique

2020 ◽  
pp. 11-15
Author(s):  
Rahul Chand Thakur ◽  
◽  
Vaibhav Panwar ◽  
Keyword(s):  
Image Processing ◽  
Skin Cancer ◽  
Supervised Learning ◽  
Detection System ◽  
Early Stage ◽  
Image Features ◽  
The Body ◽  
Image Processing Technique ◽  
Support Vector ◽  
Skin Cells

Skin cancer is considered as commonest cause of death among humans in today's world. This type of cancer shows non uniform or patchy growth of skin cells that most commonly occurs on of the certain parts of body which are more likely exposed to the light, but it can occur anywhere on the body. The majority of skin cancers can be treated if detected early. As a result, finding skin cancer early and easily will save a patient's life. Early detection of skin cancer at an early stage is now possible thanks to modern technologies. Biopsy procedure [1] is a systematic method for diagnosis skin cancer. It is achieved by extracting skin cells, after which the sample is sent to different laboratories for examination. It's a very long (in terms of time) and painful process. For primitive detection of skin cancer disease, we proposed a skin cancer detection system based on svm. It is more helpful to patients. Various methods of image processing and the supervised learning algorithm called Support Vector Machine (SVM) are used in the identification process. Epiluminescence microscopy is taken using an image and particular to several preprocessing techniques which are used in the reduction of sound artifacts and improvise quality of images. Segmentation is done by using certain thresholding techniques like OTSU. The GLCM technique must be used to remove certain image features. These characteristics are fed into the classifier as input. The Supervised learning model called (SVM) is used to distinguish data sets. It determines whether a picture is cancerous or not.

scholarly journals A Methodological Approach for Early Melanoma Detection Using Smartphone Captured Images

2019 ◽  
pp. 503-510
Author(s):  
Haritha U ◽  
Muhammad Shameem
Keyword(s):  
Malignant Melanoma ◽  
Methodological Approach ◽  
Selection Criterion ◽  
Image Features ◽  
Motion Blur ◽  
Color Variation ◽  
Correct Result ◽  
Melanoma Detection ◽  
Blurred Image ◽  
New Feature

Researches on applications of mobile devices bring wide variety of uses in healthcare. One such work focus on detection of malignant melanoma using mobile image analysis. Dermoscopy is one of a current use, but need a special expertise for the detection of cancer melanoma. The image taken using smartphone is used for this purpose. It mainly focus on localization of the skin lesion by combining fast skin detection and fusion of two fast segmentation results. This also introduces some set of image features and to capture color variation and border irregularity which are useful for smartphone-captured images. It propose a new feature selection criterion to select a small set of good features used in the final lightweight system. The method introduces a new module for the detection of distorted images such as motion blur and alert users in such situations. The blurred image undergo deblurring to detect the correct result. The result of this application will identify whether the image is malignant melanoma or benign with their intensity value from smartphone captured images used.

scholarly journals Genetic Programming for Automatic Global and Local Feature Extraction to Image Classification

2021 ◽  
Author(s):  
Ying Bi ◽  
Mengjie Zhang ◽  
Bing Xue
Keyword(s):  
Feature Extraction ◽  
Genetic Programming ◽  
Image Classification ◽  
Extraction Methods ◽  
Input Image ◽  
Image Features ◽  
Local Feature ◽  
Almost All ◽  
Local Feature Extraction ◽  
Global And Local

© 2018 IEEE. Feature extraction is an essential process to image classification. Existing feature extraction methods can extract important and discriminative image features but often require domain expert and human intervention. Genetic Programming (GP) can automatically extract features which are more adaptive to different image classification tasks. However, the majority GP-based methods only extract relatively simple features of one type i.e. local or global, which are not effective and efficient for complex image classification. In this paper, a new GP method (GP-GLF) is proposed to achieve automatically and simultaneously global and local feature extraction to image classification. To extract discriminative image features, several effective and well-known feature extraction methods, such as HOG, SIFT and LBP, are employed as GP functions in global and local scenarios. A novel program structure is developed to allow GP-GLF to evolve descriptors that can synthesise feature vectors from the input image and the automatically detected regions using these functions. The performance of the proposed method is evaluated on four different image classification data sets of varying difficulty and compared with seven GP based methods and a set of non-GP methods. Experimental results show that the proposed method achieves significantly better or similar performance than almost all the peer methods. Further analysis on the evolved programs shows the good interpretability of the GP-GLF method.

scholarly journals Computer Aided System for Malignant Melanoma Detection

2021 ◽  
pp. 72-75
Author(s):  
Mrs. Latha S S ◽  
Harshitha S ◽  
N Preetha ◽  
R Yashaswini ◽  
Nitika Bharati
Keyword(s):  
Skin Cancer ◽  
Early Stage ◽  
Wavelet Packet ◽  
Principal Component ◽  
Support Vector ◽  
Melanoma Detection ◽  
Skin Cancers ◽  
Boundary Tracing ◽  
Processing Techniques ◽  
Cure Rates

Melanoma is one of the skin cancers that attacks the cells of melanocytes that produce skin-forming pigments. In this project a new intelligent method of classifying melanoma lesions is implemented. The system consists of four stages; image pre-processing, image segmentation, feature extraction, and image classification. As the first step of the image analysis, pre-processing techniques are implemented to remove noise and undesired structures from the images using techniques such as median filtering and contrast enhancement. In the second step, a simple thresholding method is used to segment and localize the lesion, a boundary tracing algorithm is also implemented to validate the segmentation. Then, a wavelet approach is used to extract the features, more specifically Wavelet Packet Transform (WPT).Finally, the dimensionality of the selected features is reduced with Principal Component Analysis(PCA) and later supplied to an Artificial Neural Network and Support Vector Machine classifiers for classification. Incident rates of melanoma skin cancer have been rising since last two decades. So, early, fast and effective detection of skin cancer is paramount importance. If detected at an early stage. Skin has one of the highest cure rates, and the most cases, the treatment is quite simple and involves excision of the lesion. Moreover, at an early stage, skin cancer is very economical to treat, while at a late stage, cancerous lesions usually result in near fatal consequences and extremely high cost associated with the necessary treatments.

scholarly journals Genetic Programming for Automatic Global and Local Feature Extraction to Image Classification

2021 ◽  
Author(s):  
Ying Bi ◽  
Mengjie Zhang ◽  
Bing Xue
Keyword(s):  
Feature Extraction ◽  
Genetic Programming ◽  
Image Classification ◽  
Extraction Methods ◽  
Input Image ◽  
Image Features ◽  
Local Feature ◽  
Almost All ◽  
Local Feature Extraction ◽  
Global And Local

© 2018 IEEE. Feature extraction is an essential process to image classification. Existing feature extraction methods can extract important and discriminative image features but often require domain expert and human intervention. Genetic Programming (GP) can automatically extract features which are more adaptive to different image classification tasks. However, the majority GP-based methods only extract relatively simple features of one type i.e. local or global, which are not effective and efficient for complex image classification. In this paper, a new GP method (GP-GLF) is proposed to achieve automatically and simultaneously global and local feature extraction to image classification. To extract discriminative image features, several effective and well-known feature extraction methods, such as HOG, SIFT and LBP, are employed as GP functions in global and local scenarios. A novel program structure is developed to allow GP-GLF to evolve descriptors that can synthesise feature vectors from the input image and the automatically detected regions using these functions. The performance of the proposed method is evaluated on four different image classification data sets of varying difficulty and compared with seven GP based methods and a set of non-GP methods. Experimental results show that the proposed method achieves significantly better or similar performance than almost all the peer methods. Further analysis on the evolved programs shows the good interpretability of the GP-GLF method.

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