Recognizing Breast Cancer Using Edge-Weighted Texture Features of Histopathology Images

作     者：Arslan Akram Javed Rashid Fahima Hajjej Sobia Yaqoob Muhammad Hamid Asma Arshad Nadeem Sarwar 

作者机构：Department of Computer Science and Information TechnologySuperior UniversityLahore54000Pakistan MLC LabMaharban HouseHouse#209Zafar ColonyOkara56300Pakistan Information Technology ServicesUniversity of OkaraOkara56300Pakistan Department of CS&SEInternational Islamic UniversityIslamabad44000Pakistan Department of Information SystemsCollege of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer ScienceUniversity of OkaraOkara56300Pakistan Department of Statistics and Computer ScienceUniversity of Veterinary and Animal SciencesLahorePunjab54000Pakistan School of Biochemistry and BiotechnologyUniversity of the PunjabLahore54000Pakistan Department of Computer ScienceBahria UniversityLahore CampusLahore54600Pakistan 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第77卷第10期

页      面：1081-1101页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R236) Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia 

主　　题：Benign and malignant color conversion wavelet domain texture features xgboost 

摘      要：Around one in eight women will be diagnosed with breast cancer at some *** patient outcomes necessitate both early detection and an accurate *** images are routinely utilized in the process of diagnosing breast *** proposed in recent research only focus on classifying breast cancer on specific magnification *** study has focused on using a combined dataset with multiple magnification levels to classify breast cancer.A strategy for detecting breast cancer is provided in the context of this *** image texture data is used with the wavelet transform in this *** proposed method comprises converting histopathological images from Red Green Blue(RGB)to Chrominance of Blue and Chrominance of Red(YCBCR),utilizing a wavelet transform to extract texture information,and classifying the images with Extreme Gradient Boosting(XGBOOST).Furthermore,SMOTE has been used for resampling as the dataset has imbalanced *** suggested method is evaluated using 10-fold cross-validation and achieves an accuracy of 99.27%on the BreakHis 1.040X dataset,98.95%on the BreakHis 1.0100X dataset,98.92%on the BreakHis 1.0200X dataset,98.78%on the BreakHis 1.0400X dataset,and 98.80%on the combined *** findings of this study imply that improved breast cancer detection rates and patient outcomes can be achieved by combining wavelet transformation with textural signals to detect breast cancer in histopathology images.