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Content-based image retrieval techniques for the analysis of dermatological lesions using particle swarm optimization technique |
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| Affiliation: | 1. Department of Computer Science & Engineering, Dr. Sivanthi Aditanar College of Engineering, Tiruchendur 628215, India;2. Dr. Sivanthi Aditanar College of Engineering, Tiruchendur 628215, India;1. Department of Information Technology, MCKV Institute of Engineering, Liluah, Howrah 711204, India;2. Department of Computer Science and Technology, IIEST, Shibpur, Howrah 711103, India;1. Louisiana State University Health Shreveport, Shreveport, LA;2. Boston University Medical Center, Boston, MA;1. Centre for Biomedical Engineering, Transportation Research Alliance, Universiti Teknologi Malaysia, Skudai, Malaysia;2. Faculty of Bioscience and Medical Engineering, Universiti Teknologi Malaysia, Skudai, Malaysia;1. Department of Computer Engineering, Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy;2. CORISA, Department of Computer Science, University of Salerno, 84084 Fisciano, Italy |
| Abstract: | This method presents extraction of effective color and shape features for the analysis of dermatology images. We employ three phases of operation in order to perform efficient retrieval of images of skin lesions. Our proposed algorithm used color and shape feature vectors and the features are normalized using Min–Max normalization. Particle swarm optimization (PSO) technique for multi-class classification is used to converge the search space more efficiently. The results using receiver operating characteristic (ROC) curve proved that the proposed architecture is highly contributed to computer-aided diagnosis of skin lesions. Experiments on a set of 1450 images yielded a specificity of 98.22% and a sensitivity of 94%. Our empirical evaluation has a superior retrieval and diagnosis performance when compared to the performance of other works. We present explicit combinations of feature vectors corresponding to healthy and lesion skin. |
| Keywords: | Retrieval Features Classification Particle swarm optimization |
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