2019 International Conference on Advanced Information Technologies (ICAIT)
Melanoma classification on dermoscopy skin images is a demanding work as a result of the low cont... more Melanoma classification on dermoscopy skin images is a demanding work as a result of the low contrast of the lesion images, the intra-structural variants of melanomas, the much visually likeliness level of whether melanoma or non-melanoma lesions, and the covering of hair and ruler marker artifacts. In this study, the malignant melanoma skin cancer classification system is proposed with the aid of correctly classify melanoma skin cancer. The system involves three main steps: segmentation, feature extraction and classification. Ahead of the segmentation process, the preprocessing skin lesion images is processed for getting rid of the covered hair artifacts. In the segmentation step, the input preprocessed lesion image is segmented by using the proposed texture filter-based segmentation method. Then, the extraction of features with the underlying ABCD (Asymmetry, Border, Color, Differential Texture) dermatology rules using shape, edge, colored and textural features are computed from the segmented region. Lastly, the extracted features are classified to identify if the skin image is malignant melanoma or non-melanoma with the use of bag tree ensemble classifier. The system performance is evaluated with the use of the benchmarking datasets: PH2 dataset, ISBI2016 dataset and ISIC2017 dataset. According to the experimental results, the proposed design allows for both reliable classification of real world dermoscopy images and feasible operation time with today’s standard PC computing platforms. To address the class imbalance in the dataset and to yield the improved classification performance, the experiments are also analyzed not only on original imbalanced dataset but also on balancing datasets: undersampled and oversampled datasets. The system works well and provides both high sensitivity and specificity according to the experimental results on the oversampled dataset with bag tree ensemble classifier to leading to statistically better performance compared to original imbalanced dataset.
Making use of search engines is most popular Internet task apart from email. Currently, all major... more Making use of search engines is most popular Internet task apart from email. Currently, all major search engines employ web crawlers because effective web crawling is a key to the success of modern search engines. Web crawlers can give vast amounts of web information possible to explore the web entirely by humans. Therefore, crawling algorithms are crucial in selecting the pages that satisfy the users’ needs. Crawling cultural and/or linguistic specific resources from the borderless Web raises many challenging issues. This paper will review various web crawlers used for searching the web while also exploring the use of various algorithms to retrieve web pages. Keyword: Web Search Engine, Web Crawlers, Web Crawling Algorithms.
Reverse image search is content-based image retrieval (CBIR) query technique which involves provi... more Reverse image search is content-based image retrieval (CBIR) query technique which involves providing the CBIR system with a sample query image then it will base its search upon. Reverse image search can be used to search either data related to the query image or the images related to that image or similar images or exact images. In this study, different features like color, texture, shape, and neuro fuzzy and different techniques like compact composite descriptor, fractal image processing, and genetic algorithm have been reviewed. Different World Wide Web reverse image search engines (Google, Bing, Tineye) that are available and well-known today are also reviewed.
2019 International Conference on Advanced Information Technologies (ICAIT)
Melanoma classification on dermoscopy skin images is a demanding work as a result of the low cont... more Melanoma classification on dermoscopy skin images is a demanding work as a result of the low contrast of the lesion images, the intra-structural variants of melanomas, the much visually likeliness level of whether melanoma or non-melanoma lesions, and the covering of hair and ruler marker artifacts. In this study, the malignant melanoma skin cancer classification system is proposed with the aid of correctly classify melanoma skin cancer. The system involves three main steps: segmentation, feature extraction and classification. Ahead of the segmentation process, the preprocessing skin lesion images is processed for getting rid of the covered hair artifacts. In the segmentation step, the input preprocessed lesion image is segmented by using the proposed texture filter-based segmentation method. Then, the extraction of features with the underlying ABCD (Asymmetry, Border, Color, Differential Texture) dermatology rules using shape, edge, colored and textural features are computed from the segmented region. Lastly, the extracted features are classified to identify if the skin image is malignant melanoma or non-melanoma with the use of bag tree ensemble classifier. The system performance is evaluated with the use of the benchmarking datasets: PH2 dataset, ISBI2016 dataset and ISIC2017 dataset. According to the experimental results, the proposed design allows for both reliable classification of real world dermoscopy images and feasible operation time with today’s standard PC computing platforms. To address the class imbalance in the dataset and to yield the improved classification performance, the experiments are also analyzed not only on original imbalanced dataset but also on balancing datasets: undersampled and oversampled datasets. The system works well and provides both high sensitivity and specificity according to the experimental results on the oversampled dataset with bag tree ensemble classifier to leading to statistically better performance compared to original imbalanced dataset.
Making use of search engines is most popular Internet task apart from email. Currently, all major... more Making use of search engines is most popular Internet task apart from email. Currently, all major search engines employ web crawlers because effective web crawling is a key to the success of modern search engines. Web crawlers can give vast amounts of web information possible to explore the web entirely by humans. Therefore, crawling algorithms are crucial in selecting the pages that satisfy the users’ needs. Crawling cultural and/or linguistic specific resources from the borderless Web raises many challenging issues. This paper will review various web crawlers used for searching the web while also exploring the use of various algorithms to retrieve web pages. Keyword: Web Search Engine, Web Crawlers, Web Crawling Algorithms.
Reverse image search is content-based image retrieval (CBIR) query technique which involves provi... more Reverse image search is content-based image retrieval (CBIR) query technique which involves providing the CBIR system with a sample query image then it will base its search upon. Reverse image search can be used to search either data related to the query image or the images related to that image or similar images or exact images. In this study, different features like color, texture, shape, and neuro fuzzy and different techniques like compact composite descriptor, fractal image processing, and genetic algorithm have been reviewed. Different World Wide Web reverse image search engines (Google, Bing, Tineye) that are available and well-known today are also reviewed.
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Papers by Nay Chi Lynn