An Amalgam Approach For Dicom Image Classification And Recognition

• Main Authors: J. Umamaheswari, G. Radhamani
• Format: Text
• Language: English
• Published: Zenodo 2012
• Subjects: Recognition; classification; Relaxed Median Filter; Adaptive thresholding; clustering and Neural Networks; Clayton; Ergen; Redondo; Ruan; sami
• Online Access: https://dx.doi.org/10.5281/zenodo.1080992; https://zenodo.org/record/1080992

This paper describes about the process of recognition and classification of brain images such as normal and abnormal based on PSO-SVM. Image Classification is becoming more important for medical diagnosis process. In medical area especially for diagnosis the abnormality of the patient is classified, which plays a great role for the doctors to diagnosis the patient according to the severeness of the diseases. In case of DICOM images it is very tough for optimal recognition and early detection of diseases. Our work focuses on recognition and classification of DICOM image based on collective approach of digital image processing. For optimal recognition and classification Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Support Vector Machine (SVM) are used. 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