Smart System for Medical Expertise Assistance

Abstract

The computer-based techniques are more suitable for feature extraction, and classification. The proposed CAD (Computer Aided Diagnosis) system addresses the several steps such as preprocessing, feature extraction, and classification. Though many commercial CAD systems are available, identification of subtle signs for many diseases detection and classification remains difficult. The proposed system presents some advanced techniques in medical imaging to overcome these difficulties. The past decade has seen the growing popularity of Bag of Features (BoF) approaches to many computer vision tasks, including image classification, video search and texture recognition. BoF methods are based on order less collections of quantized local image descriptors; they discard spatial information and are therefore conceptually and computationally simpler than many alternative methods. Despite this, or perhaps because of this, BoF-based systems have set new performance standards on popular image classification benchmarks and have achieved scalability breakthroughs in image retrieval. Emphasis is placed on recent techniques that mitigate quantization errors, improve feature detection, and speed up image retrieval. Among the unresolved issues are determining the best techniques for sampling images, describing local image features, and evaluating system performance. Among the more fundamental challenges are how and whether BoF methods can contribute to localizing objects in complex images, or to associating high-level semantics with natural images.