Abstract
Computer-Aided Detection (CADe) system has a significant role as a preventative effort in the early detection of breast cancer. There are some phases in developing the pattern recognition on the CADe system, including the availability of a large number of data, feature extraction, selection and use of features, and the selection of the appropriate classification method. Haar cascade classifier has been successfully developed to detect the faces in the multimedia image automatically and quickly. The success of the face detection system must not be separated from the availability of the training data in the large numbers. However, it is not easy to implement on a medical image because of some reasons, including its low quality, the very little gray-value differences, and the limited number of the patches for the examples of the positive data. Therefore, this research proposes an algorithm to overcome the limitation of the number of patches on the region of interest to detect whether the lesion exists or not on the mammogram images based on the Haar cascade classifier. This research uses the mammogram and ultrasonography images from the breast imaging of 60 probands and patients in the Clinic of Oncology, Yogyakarta. The testing of the CADe system is done by comparing the reading result of that system with the mammography reading result validated with the reading of the ultrasonography image by the Radiologist. The testing result of the k-fold cross validation demonstrates that the use of the algorithm for the multiplication of intersection rectangle may improve the system performance with accuracy, sensitivity, and specificity of 76%, 89%, and 63%, respectively.
Highlights
Breast Cancer is the second most common forms of cancer in the world and has the first position as the most common form of cancer among women (World Cancer Research Fund International)
The annotation is conducted on the mammogram image on any part considered as the disorder by the Radiologist with the assessment validation using the ultrasonography image
From the cropping result conducted by the Radiologist, it may be inferred that there is only one rectangle/Region of Interest (RoI) on a single image, but on some mammogram images, it is found that the Radiologists give the annotations more than once at the close/intersected locations, and some are at different locations
Summary
Breast Cancer is the second most common forms of cancer in the world and has the first position as the most common form of cancer among women (World Cancer Research Fund International). Previous researches, for the CADe system, use the mammography that is developed based on the three features on the mammogram image, those are the features of color, texture and shape. Some previous researches that have developed the RoI classification system into two classes RoI (mass and a nonmass) mostly conduct the feature extraction statistically [6] and [12]. The limited number of patches on the positive sample for the training data is one obstacle in developing the CADe system using the cascade classifier. An algorithm is required to multiply the number of patches as a positive sample on the CADe system to improve its performance
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Computer Science and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
