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Approximately 1 in 8 U.S. women will be diagnosed with new invasive breast cancer over the course of her lifetime. An estimated 252,710 new cases of invasive breast cancer are expected to be diagnosed in women in the U.S. in 2017. Mastectomy is recommended in over a third of early-stage breast cancer patients. Those women who elect to undergo breast reconstruction are counseled on surgical risks and...
The automatic and precision classification for breast cancer histopathological image has a great significance in clinical application. However, the existing analysis approaches are difficult to addressing the breast cancer classification problem because the feature subtle differences of inter-class histopathological image and the classification accuracy still hard to meet the clinical application...
Analysis and interpretation of stained tumor sections is one of the main tools in cancer diagnosis and prognosis, which is mainly carried out manually by pathologists. The avent of digital pathology provides us with the challenging opportunity to automatically analyze large amounts of these complex image data in order to draw biological conclusions from them and to study cellular and tissular phenotypes...
The differential diagnosis of proliferative breast lesions, benign usual ductal hyperplasia (UDH) versus malignant ductal carcinoma in situ (DCIS) is challenging. This involves a pathologist examining histopathologic sections of a biopsy using a light microscope, evaluating tissue structures for their architecture or size, and assessing individual cell nuclei for their morphology. Imposing diagnostic...
We introduce a new fully automated breast mass segmentation method from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The method is based on globally optimal inference in a continuous space (GOCS) using a shape prior computed from a semantic segmentation produced by a deep learning (DL) model. We propose this approach because the limited amount of annotated training samples does...
Diagnosis of breast carcinomas has so far been limited to the morphological interpretation of epithelial cells and the assessment of epithelial tissue architecture. Consequently, most of the automated systems have focused on characterizing the epithelial regions of the breast to detect cancer. In this paper, we propose a system for classification of hematoxylin and eosin (H&E) stained breast specimens...
In computerized detection of clustered microcalcifications (MCs) from mammogram images the occurrence of false positives (FPs) varies greatly from case to case. In this work, we develop a probabilistic modeling approach to estimate the number of individual FPs present in a detected MC lesion. We describe the number of true positives (TPs) by a Poisson-Binomial probability distribution, wherein a logistic...
The main aim of this paper is to enhance using hybrid solution for early diagnosis of breast cancer using mammogram images. For the CAD system for any image processing system follows mainly four steps that is image pre processing, segmentation, features extraction and classification and evaluation. For this research purpose follows the same structure using best possible methods in each stage found...
Breast cancer cases are increasing year by year and second leading reasons for the women's death worldwide. Early detection is very important and will help to save thousands of peoples' lives. The available systems such as Mammogram, MRI and ultrasound are invasive, expensive and need expert to operate. This paper presents a low cost and non-invasive breast cancer detection system for early detection...
This paper expounds the cognitive Image processing techniques involved in medical images. There are several varieties of proliferative cancer that are being well-known by means of the researchers, which has now hit the tally to a hundred. Each and every cancer is unique of its sort to be famous alongside the signs. This paper specializes in the more than a few image processing algorithms involved...
Breast cancer is a significantly alarming health issue for women where Dynamic Contrast Enhanced Magnetic Resonance Imaging serves as a pivot in detection, diagnoses and treatment monitoring. In this paper the response given by breast cancer patients to Neoadjuvant Chemotherapy is analyzed with Magnetic Resonance Images of these patients taken before and after treatment. The MRI images are pre-processed...
Breast cancer is the leading cancer that affects women in the world. Early diagnosis of cancer prevents morbidity and mortality rate. Thermography is an additional tool for early diagnosis of breast cancer. Low contrast, poor Signal to Noise Ratio (SNR) and complex breast boundaries are the inherent limitation of breast thermal images which makes segmentation a challenging task. In this work, an attempt...
Breast cancer is the foremost cause of morbidity and mortality among womenfolk. India has 17% of world's population suffering from breast cancer. World Health Organization's International agency for Research on Cancer (IARC) estimates that more than 4,00,000 women die every year due to breast cancer. Thus early identification of breast cancer plays a vital role in reducing the mortality rate. Medical...
Biological system such as neural networks and genetic algorithms are adapted to improve the doctors experience for diagnosis of illnesses. This work is introduced an approach for diagnosing breast cancer via classifying a well-known WBCD dataset based on a hybrid neurogenetic system. The suggested approach showed a good behavior and excellent classification accuracy through the implementation of several...
The ubiquitous presence of smartphones worldwide is contributing to the field of mhealth. These devices are used to promote health in different approaches. Breast cancer is the most frequent cancer in the female population and the mortality rate remains increasing in Brazil, which has been associated to delayed diagnosis. Thus, we aimed to outline a mhealth system to mobilize the target women for...
This paper shows the application of a genetic algorithm (GA) for component selection to improve the accuracy of a kNN (k-Nearest Neighbor) method when using it for breast cancer prognosis. Our GA uses the best chromosome (member) in a generation to produce new ones. The probabilities of crossover and mutation have not fixed values, instead they depend on the evaluation of the chromosomes involved...
Breast ultrasound is an important complement to mammography that allows differentiating between benign and malignant lesions. The early diagnosis and specialized care for women with breast cancer are indispensable for a better therapeutic result and prognosis of cases. Thereby, it is necessary training professionals to emit trustworthy diagnoses. Here, we set up lesions-like structures in a plasticized...
Medical imaging using electromagnetic waves is a promising field of study, due to its non invasive nature and relatively low cost. Typically, it is pursued at frequencies in the microwave regime to provide adequate penetration depth. However, an increase of the frequency of operation is beneficial in order to improve the resolution, but it is accompanied by increased tissue losses. In this work we...
At microwave and mm-wave frequencies, the interactions of the human tissue with electromagnetic fields depend on the dielectric properties of the tissue itself. The knowledge of these properties of healthy and malignant tissues, beyond to be crucial in several biomedical applications, is necessary to fix the safety thresholds in policy-making and the exposure limits for the human body. In particular,...
Microwave and mm-wave imaging systems for breast cancer detection are pursued to provide an additional technique with respect to current approaches, mostly based x-rays, ultrasounds, and magnetic resonance. The use of non-ionizing radiation and the relatively low cost are among the most important advantages, but often with resolution or attenuation below expectations for microwave and mm-wave systems,...
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