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This work investigates the use of terahertz imaging for differentiating between cancer and healthy tissue regions in freshly excised breast cancer tumors. Bulk tissue specimens are obtained fresh in a nutrient medium from a biobank and scanned on a pulsed terahertz system in the reflection mode to produce the reflection image. Tissues are then sent for histopathology processing, and the subsequent...
Mammography is still the most effective procedure for early diagnosis of the breast cancer. Computer-aided Diagnosis (CAD) systems can be very helpful in this direction for radiologists to recognize abnormal and normal regions of interest in digital mammograms faster than traditional screening program. In this work, we propose a new method for breast cancer identification of all types of lesions in...
Breast cancer is the second most common cause of death among women. However, it is not deadly if the cancerous cells remain in the breast. The life threat starts when cancerous cells travel to other parts of body like lung, liver, bone and brain. So, most breast cancer deaths derive from metastasis to other organs. In this study, we introduce novel proteins and cellular pathways that play important...
A cell which grows unusually and divides in categorical routine is Cancer and it is a deadly disease. Though it is unfortunate to detect but still it is possible to rectify at initial stage detection. This survey will be dealing with various cancer type and detecting methods available. The major motive of this document is to examine the benefits and difficulty of detection methods available and to...
Breast cancer became one of the deadliest cancer in women. It occurs when the growth of the cells in breast tissue become out of control. Cells are the building blocks for the organs and tissues in the body. When the growth of new cells are uncontrolled then they build-up mass of tissue called tumor. The tumors are categorized in to benign and malignant tumors. Early diagnosis needs an accurate diagnosis...
To improve the performance of the computer-aided systems for breast cancer diagnosis, the ensemble classifier is proposed for classifying the histological structures in the breast cancer microscopic images into three region types: positive cancer cells, negative cancer cells and non-cancer cell (stromal cells and lymphocyte cells) image. The bagging and boosting ensemble techniques are used with the...
According to the World Health Organization (WHO)1, an early detection of cancer greatly increases the chances of making the right decision in a successful treatment plan. Over the last decade, the increasing world-wide demand for early detection of breast cancer at the hospitals has resulted in necessity of new research avenues. The traditional domain knowledge based diagnostic method requires hand-crafted...
The increasing availability of novel health-related data sources —e.g., from molecular analysis, health Apps and electronic health records— might eventually overwhelm the physician, and the community is investigating analytics approaches that might be useful to support clinical decisions. In particular, the success of the latest developments in Deep Learning has demonstrated that machine learning...
In this study, we aim to analyze the quantitative features for characterizing microcalcification clusters on mammograms for pathological classification into benign and malignant classes. Our database includes 101 cases: 48 cases of benign and 53 cases of malignant with biopsy proven. Two views of mammogram images, Cranial Caudal (CC) view and MedioLateral Oblique (MLO) view were used in our experiments...
An elliptical planar ultra-wideband (UWB) ring antenna in the presence of a humanoid breast phantom is presented in this paper. The antenna is designed for breast imaging. Therefore, it is modeled in front of a humanoid cubic breast phantom and its performances are studied. The antenna operates in a wide frequency band from about 1.5 GHz to 7 GHz. The impedance bandwidth is about 126%. This Antenna...
In this paper, an agent framework for solving multidisciplinary decisions is proposed, including a conceptual decision model coupled with an agent scheme, and a set of functional signatures that drive the inference. The framework is specially designed to support decision-making in organisational structuring among care specialists towards complex problems, individual planning and argumentation, and...
Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel computer-aided diagnose (CAD) system based on one of the regional deep learning techniques: a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO). Our proposed YOLO-based...
Breast cancer is a major public health issue and currently the second leading cause of cancer death in women. Early detection on whether a tumor is benign or malignant is crucial to a patient's treatment and their odds of survival. There are already existing methods in place to detect breast cancer based on physical characteristics of tumors as well as identifying novel biomarker genes. However the...
Machine learning has been widely used for solving various classification problems in biomedical research field due to its strength in handling massive data set systematically. In this paper, a feasible way of breast cancer localization via machine learning is presented with a preliminary result for 10 cancerous breast tissue samples (600 μm diameter and 8 μm thickness). Using a custom-built microscope-compatible...
Accurate cell detection is often an essential prerequisite for subsequent cellular analysis in computer aided diagnosis (CAD) for histopathlogy images. It is challenging due to high cell density, touching cells, low contrast, variant cell shapes and sizes, weak boundaries and the use of different image acquisition techniques. Existing methods are often struggling at tackling with the challenges at...
In this paper, an algorithm that based on pca-bp-bagging model is developed for the prediction of pathological data. This algorithm aims at improving the characteristics of bp neural network that the prediction accuracy of pathological data is low, the generalization ability of single bp neural network model is poor, and the anti-interference ability is weak. To enhance the performance of the whole...
This paper concerns design and numerical simulation for anti-angiogenic tumor treatment for the tumors cause women breast cancer. The paper first provides an exact model knowledge controller to drive tumor volume to a desired trajectory, and then explains the design of an observer for the carrying capacity of the vascular network. Lyapunov-like arguments are used to prove the stability of the system...
Fine needle aspiration cytology is commonly used for diagnosis of breast cancer, with traditional practice being based on the subjective visual assessment of the breast cytopathology cell samples under a microscope to evaluate the state of various cytological features. Therefore, there are many challenges in maintaining consistency and reproducibility of findings. However, digital imaging and computational...
Introduction The increased prevalence of cancer survivors requires a focus on developing long-term, cost-effective management strategies to prevent and limit disability and morbidity. Background Cancer survivors with pain, weakness and restricted movement often benefit from targeted exercise programmes provided by a Physiotherapist. Physical, psychological and situational factors can impact on patients...
In this study, we focused on posting frequency and quality of exercise and physical activity content in Facebook. We sought to (1) explore the frequency of exercise and physical activity topics posted on specialized Breast Cancer channels and (2) evaluate the quality of these information. Evaluations of informations quality were performed independently by two sports and exercise experts, with previous...
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