جستجوی مقالات مرتبط با کلیدواژه « سرطان سینه » در نشریات گروه « فناوری اطلاعات »

Breast cancer is one of the most common causes of death among women around the world, but early and accurate diagnosis of this type of cancer can dramatically improve treatment. Thermal imaging is one of the primary methods of diagnosing breast cancer. The computer diagnosis system can also be used to help physicians to increase the accuracy of interpretation of results. This paper presents an intelligent computer diagnostic system for the detection of breast cancer using thermal imaging. The proposed intelligent computer diagnosis system includes SFTA method for feature extraction and SVM, kNN and D-Tree algorithms for classification of results. The performance of the proposed intelligent computer diagnosis system is evaluated using the DMR-IR and Fluminense Federal University databases and MATLAB2018, when using the cuckoo feature selection algorithm and without using the feature selection algorithm. The results show that the average accuracy, sensitivity and specificity are 99%, 99.5% and 98.03%, respectively, using the cuckoo feature selection algorithm and SVM classification algorithm. Also, the presented computer diagnostic system has advantages compared to other computer diagnosis systems. These results indicate that the use of SFTA feature extraction method, cuckoo feature selection algorithm, SVM classification algorithm and DMR-IR database in the proposed computer diagnosis system can improve the evaluation results.

Mammography is the most common and effective screening method for breast cancer detection. In this paper a computer aided system for classification of benign and malignant tumors in digital mammogram is presented. First, a median filter is used for noise reduction, and then artifacts and pectoral muscle are removed to make the mammogram ready for segmentation. For segmentation of mammogram, a new contrast enhancement method is presented which employs the difference of two complement enhanced images and then a histogram based fuzzy C-means (HFCM) clustering are used for region-of-interest (ROI) extraction. Then, some geometrical and textural features are extracted, and finally linear support vector machine and decision tree classifier are used to classify the region of interest into benign and malignant classes. The proposed algorithm is validated on the MIAS and DDSM databases. The experimental results showed that the performance of the proposed method is promising compared to the other methods evaluated.

Cancerous tumor segmentation in mammogram images is an important stage and a challenging problemin computer aided detection (CAD) systems. In this paper, local feature information and Chan-Vese(LFI-CV)active contour modelare used for tumor segmentation. First, the texture feature mapsof mammograms are extracted. The utilized texture feature information includes gray level co-occurrence matrix (GLCM) and Gabor features. Using this information,the force values ofChan-Vese model are set and active contour model’s energy is minimized.As a result, the contour accurately segments the tumor. The results show that tumor segmentation using the proposed active contour modelandGabor texture feature at orientationis efficient in regard to the number of iterations, accuracy, and sensitivity. The mini-MIAS database is used for evaluation.