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.
