breast cancer is a major public health problem for women in the iran and many other parts of the world. dynamic contrast-enhanced magnetic resonance imaging (dce-mri) plays a pivotal role in breast cancer care, including detection, diagnosis, and treatment monitoring. but segmentation of these images which is seriously affected by intensity inhomogeneities created by radio-frequency coils, is a...

breast lesion segmentation in mr images is one of the most important parts of clinical diagnostic tools. pixel classification methods have been frequently used in image segmentation with two supervised and unsupervised approaches up to now. supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to be obtained. on t...

Breast lesion is a malignant tumor that occurs in the epithelial tissue of breast. The early detection breast lesions can make patients for treatment and improve survival rate. Thus, accurate automatic segmentation from ultrasound images fundamental task. However, effectively still faced up with two challenges. One characteristics lesions’ multi-scale other one blurred edges difficult. To solve...

Accurate segmentation of breast lesions is a crucial step in evaluating the characteristics of tumors. However, this is a challenging task, since breast lesions have sophisticated shape, topological structure, and variation in the intensity distribution. In this paper, we evaluated the performance of three unsupervised algorithms for the task of breast Magnetic Resonance (MRI) lesion segmentati...

An application of an unsupervised neural network-based computer-aided diagnosis (CAD) system is reported for the detection and characterization of small indeterminate breast lesions, average size 1.1 mm, in dynamic contrast-enhanced MRI. This system enables the extraction of spatial and temporal features of dynamic MRI data and additionally provides a segmentation with regard to identification ...

Breast cancer screening is an efficient method to detect breast lesions early. The common techniques are tomosynthesis and mammography images. However, the traditional manual diagnosis requires intense workload for pathologists, hence prone diagnostic errors. Thus, aim of this study was build a deep convolutional neural network automatic detection, segmentation, classification in Based on learn...

Breast cancer (BC) is one of the most prevailing and life-threatening types impacting women worldwide. Early detection accurate diagnosis are crucial for effective treatment improved patient outcomes. Deep learning techniques have shown remarkable promise in medical image analysis tasks, particularly segmentation. This research leverages Ultrasound Images BUSI dataset to develop two variations ...

Magnetic resonance imaging (MRI) is an effective imaging modality for identifying and localizing breast lesions in women. Accurate and precise lesion segmentation using a computer-aided-diagnosis (CAD) system, is a crucial step in evaluating tumor volume and in the quantification of tumor characteristics. However, this is a challenging task, since breast lesions have sophisticated shape, topolo...