Breast Microwave Radar is a novel imaging modality for breast cancer detection . This technology uses low power microwaves to illuminate the breast region and it generates a reflectivity map of the breast structures in which tumors can be detected . Microwave imaging for breast cancer detection has been of significant interest for the last two decades . Recent studies focus on solving the imaging problem using an inverse scattering approach . The information about nonlinearity and model structure was used as part of the whole image model . It was found that , in some instances , the models from different clinical classes in the breast cancer detection problem presented a particular structure . For example , NARX models of the malignant class showed higher non-linearity percentage and depended more on exogenous inputs compared to other classes . The results obtained show that ED , SF , and PF breast models had significant mean differences in surface temperature profile with a p value < 0.025 , while HD breast model data pair agreed with the null hypothesis formulated due to the comparable tissue composition percentage to the reference model . The findings suggested that various breast density levels should be considered as a contributing factor to the surface thermal distribution profile alteration in both breast cancer detection and analysis when using the thermography technique . The number of cases of breast cancer has increased worldwide . In this paper , breast cancer disease detection and its subsequent treatment has been discussed . In the proposed work , mammogram and MRI , the two important modalities , have been used to detect the tumorous portion more accurately . One of the important practical applications of computer vision techniques is computer-aided diagnosis ( CAD ) in medical imaging . It has been shown that CAD can improve the accuracy of breast cancer detection and characterization by radiologists on mammograms . In this chapter , we discuss an important step — feature selection — in classifier design for CAD algorithms . Recent progress in establishment of the National Institutes of Health ( NIH ) National Institute of Biomedical Imaging and Bioengineering is summarized . An Institute of Medicine report on emerging technologies for breast cancer detection and screening is announced . Breast cancer detection images are the standard clinical practice for the diagnosis and prognosis of breast cancer . Digital Mammogram has emerged as the most popular screening technique for early detection of Breast Cancer and other abnormalities . Women in the U.S. have a 1 in 7 lifetime risk for developing breast cancer . The breast cancer detection process from mammography to clinical consultation with the medical oncologist is often at minimum 28 days . The pathway of diagnosis to treatment generally begins with either an abnormal screening test or an abnormal self or clinical breast examination . The statistical range of each parameter is calculated for number of iterations . The individual statistical parameter analysis with its impact on lung cancer images is carried out and finally the Artificial Neural Network is the final decision maker in lung cancer diagnosis . This paper also rejects the null hypothesis test by implementing one of the standard statistical methods . Lung parenchyma segmentation is a very important stage in every CAD system for lung cancer detection . In this paper , we propose a new method for CT lung Parenchyma segmentation using the deep SegNet neural network with VGG-16 model . In addition to these variables , the first model contains the frequency of chromatid breaks and the second model contains overall frequency of chromosomal aberrations . As for the lung cancer incidence , we found only two significant factors , the level of exposure to radon and the fact whether or not the subject ever smoked . The other purpose of the paper was to compare our results with the results of the study of Smerhovský et al. [ 8 ] , as our study was an expansion to this study . In this paper we investigated whether the geographical variation of lung cancer incidence can be predicted through examining the spatiotemporal trend of particulate matter air pollution levels . Regional trends of air pollution levels were analyzed by a novel shapelet-based time series analysis technique . To minimize this problem , many systems have been proposed by using different image processing techniques , machine learning , and deep learning techniques . A computed tomography ( CT ) imaging modality is an efficient technique for medical screening used for lung cancer detection and diagnosis . Physician and radiologist use the CT scan images to analyze , interpret and diagnose the lung cancer from lung tissues . The invention discloses a deep convolutional neural network-based lung cancer preventing self-service health cloud service system . The system comprises a convolutional neural network used for deep learning and training identification , a segmentation module which segments out a lung region from a CT image based on a full convolutional neural network , a deep convolutional neural network used for lung cancer diagnosis classification , and a self-service health cloud service platform used for performing early prevention and treatment according to an identified suspected lung cancer type . According to the system , the automation and intelligentization level of mobile internet-based lung cancer screening can be effectively improved , more citizens can know and participate in self-service health detection , assessment and guidance , the sensitivity , specificity and accuracy of early lung cancer screening and clinical diagnosis are improved , the lung cancer can be early discovered , early diagnosed and early treated , and the self-health management capability is enhanced . It has been used to analyze demographic data from lung cancer patients with a view to developing diagnostic algorithms that might improve triage practices in the emergency department . For the lung cancer diagnosis problem , the concise rules extracted from the network achieve an high accuracy rate of on the training data set and on the test data set . Supervised deep learning relies on the assumption that enough training data is available , which presents a problem for its application to several fields , like medical imaging . On the example of a binary image classification task ( breast cancer recognition ) , we show that pretraining a generative model for meaningful image augmentation helps enhance the performance of the resulting classifier . By augmenting the data , performance on downstream classification tasks could be improved even with a relatively small training set . Breast cancer treatable while still stadium early . The first step in the treatment of disease breast cancer is detection by true that symptoms appearing on the body patient is really cancer cells . Stadium cancer is the result of diagnostic by physicians so that ease in understanding for patients . Breast cancer treatable while still stadium early . The first step in the treatment of disease breast cancer is detection by true that symptoms appearing on the body patient is really cancer cells . Stadium cancer is the result of diagnostic by physicians so that ease in understanding for patients . As a result , systems have evolved with a dedicated emphasis on the quality with which they can deliver the radiation dose to a specified target . In parallel , imaging systems have advanced at an accelerated pace of innovation bringing outstanding performance in cancer characterization and diagnosis . Hallmarks of quality in diagnostic imaging have focused on distinguishing tissue contrasts for categorization of disease , high throughput , and low radiation exposure . Skin cancer has become the most common form of cancer and most well-known disease of the human . Early identification of skin cancer is important for improving prognosis , as patients with specific conditions can treat these diseases properly when distinguished in the initial stages . Therefore , computer-aided diagnostic systems must be developed to assist early detection of skin cancer . Small-molecule compounds identified from the pharmacophore screening were selected for in vitro screening in a cell-based CXCR2-mediated β-arrestin-2 recruitment assay and further characterized in several cell-based assays and lipopolysaccharide (LPS)-induced lung inflammation studies in mice . CX compounds identified from pharmacophore modeling inhibited cell migration , lung and colon cancer cell proliferation , and colony formation . Mechanistic studies of CX4152 showed that this compound inhibits CXCR2 signaling through downregulation of surface CXCR2 . Further sophistication and flexibility of imaging systems have and will allow these techniques to move further into the clinical realm . In this chapter , we will review the basic aspects of cancer optical imaging , including therapeutic , diagnostic , and surgical applications . Computerized classification plays an important role in the classification of cancer stage . Hence , there is a growing need for automatic classification of cancer data . The objective of this research is to lead specialists analyzing the breast cancer patients effectively . This research by analyzing 2010 breast cancer patients 1 ) attempts to propose six different statistical models using parametric and semi-parametric approaches for survival analysis of breast cancer patients , 2 ) compares the performance capabilities of the proposed statistical models analytically , and 3 ) addresses the most superior approach for a survival analysis of a breast cancer . To analyze the capability of the six proposed models Akaike term is used . The performances of these applications demonstrate the high quality of our method . Based on the present work , our method can be widely used to discriminate different cancer subtypes , which will contribute to the development of technology for the recovery of the cancer . Pulmonary nodules usually act as the early stage characterization of lung cancer . It 's a crucial way to control the disease and reduce the mortality by further examination on the diagnosis of pulmonary nodules . Evolutionary Algorithms ( EAs ) have demonstrated to be useful for solving optimization problems in a heuristic way . This chapter presents a comparative study related to the prediction of cancer cells based on Machine Learning and Evolutionary Algorithms . As well as , a brief introduction of machine learning and evolutionary technics is presented . Targeted cancer drugs have been developed to interfere with specific molecular targets , which are expected to affect the growth of cancer cells in a way that can be characterized by multi-omics data . The prediction of cancer drug sensitivity simultaneously for multiple drugs based on heterogeneous multi-omics data ( e.g. , mRNA expression , DNA copy number or DNA mutation ) is an important but challenging task . We use joint penalized regression models for multiple cancer drugs rather than a separate model for each drug , thus being able to address the correlation structure between drugs . The prediction of cell-lines sensitivity to a given set of compounds is a very important factor in the optimization of in-vitro assays . To date , the most common prediction strategies are based upon machine learning or other quantitative structure-activity relationships ( QSAR ) based approaches . Furthermore , we found that this noise reduction method is robust and effective even when a large amount of artificial noise is added to the input data . ConclusionWe found that our extended off-subspace noise-reduction method combining heterogeneous biological data is successful and quite useful to improve prediction of human cell cancer dru responses from microarray data . Results We introduce a probabilistic framework to incorporate expert feedback about the impact of genomic measurements on the outcome of interest and present a novel approach to collect the feedback efficiently , based on Bayesian experimental design . The new approach outperformed other recent alternatives in two medical applications : prediction of metabolic traits and prediction of sensitivity of cancer cells to different drugs , both using genomic features as predictors . Furthermore , the intelligent approach to collect feedback reduced the workload of the expert to approximately 11 % , compared to a baseline approach . BackgroundThe success of targeted anti-cancer drugs are frequently hindered by the lack of knowledge of the individual pathway of the patient and the extreme data requirements on the estimation of the personalized genetic network of the patient ’s tumor . The prediction of tumor sensitivity to targeted drugs remains a major challenge in the design of optimal therapeutic strategies . The current sensitivity prediction approaches are primarily based on genetic characterizations of the tumor sample .
Breast Microwave Radar is a novel imaging modality for breast cancer detection . This technology uses low power microwaves to illuminate the breast region and it generates a reflectivity map of the breast structures in which tumors can be detected .
Microwave imaging for breast cancer detection has been of significant interest for the last two decades . Recent studies focus on solving the imaging problem using an inverse scattering approach .
The information about nonlinearity and model structure was used as part of the whole image model . It was found that , in some instances , the models from different clinical classes in the breast cancer detection problem presented a particular structure . For example , NARX models of the malignant class showed higher non-linearity percentage and depended more on exogenous inputs compared to other classes .
The results obtained show that ED , SF , and PF breast models had significant mean differences in surface temperature profile with a p value < 0.025 , while HD breast model data pair agreed with the null hypothesis formulated due to the comparable tissue composition percentage to the reference model . The findings suggested that various breast density levels should be considered as a contributing factor to the surface thermal distribution profile alteration in both breast cancer detection and analysis when using the thermography technique .
The number of cases of breast cancer has increased worldwide . In this paper , breast cancer disease detection and its subsequent treatment has been discussed . In the proposed work , mammogram and MRI , the two important modalities , have been used to detect the tumorous portion more accurately .
One of the important practical applications of computer vision techniques is computer-aided diagnosis ( CAD ) in medical imaging . It has been shown that CAD can improve the accuracy of breast cancer detection and characterization by radiologists on mammograms . In this chapter , we discuss an important step — feature selection — in classifier design for CAD algorithms .
Recent progress in establishment of the National Institutes of Health ( NIH ) National Institute of Biomedical Imaging and Bioengineering is summarized . An Institute of Medicine report on emerging technologies for breast cancer detection and screening is announced .
Breast cancer detection images are the standard clinical practice for the diagnosis and prognosis of breast cancer . Digital Mammogram has emerged as the most popular screening technique for early detection of Breast Cancer and other abnormalities .
Women in the U.S. have a 1 in 7 lifetime risk for developing breast cancer . The breast cancer detection process from mammography to clinical consultation with the medical oncologist is often at minimum 28 days . The pathway of diagnosis to treatment generally begins with either an abnormal screening test or an abnormal self or clinical breast examination .
The statistical range of each parameter is calculated for number of iterations . The individual statistical parameter analysis with its impact on lung cancer images is carried out and finally the Artificial Neural Network is the final decision maker in lung cancer diagnosis . This paper also rejects the null hypothesis test by implementing one of the standard statistical methods .
Lung parenchyma segmentation is a very important stage in every CAD system for lung cancer detection . In this paper , we propose a new method for CT lung Parenchyma segmentation using the deep SegNet neural network with VGG-16 model .
In addition to these variables , the first model contains the frequency of chromatid breaks and the second model contains overall frequency of chromosomal aberrations . As for the lung cancer incidence , we found only two significant factors , the level of exposure to radon and the fact whether or not the subject ever smoked . The other purpose of the paper was to compare our results with the results of the study of Smerhovský et al. [ 8 ] , as our study was an expansion to this study .
In this paper we investigated whether the geographical variation of lung cancer incidence can be predicted through examining the spatiotemporal trend of particulate matter air pollution levels . Regional trends of air pollution levels were analyzed by a novel shapelet-based time series analysis technique .
To minimize this problem , many systems have been proposed by using different image processing techniques , machine learning , and deep learning techniques . A computed tomography ( CT ) imaging modality is an efficient technique for medical screening used for lung cancer detection and diagnosis . Physician and radiologist use the CT scan images to analyze , interpret and diagnose the lung cancer from lung tissues .
The invention discloses a deep convolutional neural network-based lung cancer preventing self-service health cloud service system . The system comprises a convolutional neural network used for deep learning and training identification , a segmentation module which segments out a lung region from a CT image based on a full convolutional neural network , a deep convolutional neural network used for lung cancer diagnosis classification , and a self-service health cloud service platform used for performing early prevention and treatment according to an identified suspected lung cancer type . According to the system , the automation and intelligentization level of mobile internet-based lung cancer screening can be effectively improved , more citizens can know and participate in self-service health detection , assessment and guidance , the sensitivity , specificity and accuracy of early lung cancer screening and clinical diagnosis are improved , the lung cancer can be early discovered , early diagnosed and early treated , and the self-health management capability is enhanced .
It has been used to analyze demographic data from lung cancer patients with a view to developing diagnostic algorithms that might improve triage practices in the emergency department . For the lung cancer diagnosis problem , the concise rules extracted from the network achieve an high accuracy rate of on the training data set and on the test data set .
Supervised deep learning relies on the assumption that enough training data is available , which presents a problem for its application to several fields , like medical imaging . On the example of a binary image classification task ( breast cancer recognition ) , we show that pretraining a generative model for meaningful image augmentation helps enhance the performance of the resulting classifier . By augmenting the data , performance on downstream classification tasks could be improved even with a relatively small training set .
Breast cancer treatable while still stadium early . The first step in the treatment of disease breast cancer is detection by true that symptoms appearing on the body patient is really cancer cells . Stadium cancer is the result of diagnostic by physicians so that ease in understanding for patients .
Breast cancer treatable while still stadium early . The first step in the treatment of disease breast cancer is detection by true that symptoms appearing on the body patient is really cancer cells . Stadium cancer is the result of diagnostic by physicians so that ease in understanding for patients .
As a result , systems have evolved with a dedicated emphasis on the quality with which they can deliver the radiation dose to a specified target . In parallel , imaging systems have advanced at an accelerated pace of innovation bringing outstanding performance in cancer characterization and diagnosis . Hallmarks of quality in diagnostic imaging have focused on distinguishing tissue contrasts for categorization of disease , high throughput , and low radiation exposure .
Skin cancer has become the most common form of cancer and most well-known disease of the human . Early identification of skin cancer is important for improving prognosis , as patients with specific conditions can treat these diseases properly when distinguished in the initial stages . Therefore , computer-aided diagnostic systems must be developed to assist early detection of skin cancer .
Small-molecule compounds identified from the pharmacophore screening were selected for in vitro screening in a cell-based CXCR2-mediated β-arrestin-2 recruitment assay and further characterized in several cell-based assays and lipopolysaccharide (LPS)-induced lung inflammation studies in mice . CX compounds identified from pharmacophore modeling inhibited cell migration , lung and colon cancer cell proliferation , and colony formation . Mechanistic studies of CX4152 showed that this compound inhibits CXCR2 signaling through downregulation of surface CXCR2 .
Further sophistication and flexibility of imaging systems have and will allow these techniques to move further into the clinical realm . In this chapter , we will review the basic aspects of cancer optical imaging , including therapeutic , diagnostic , and surgical applications .
Computerized classification plays an important role in the classification of cancer stage . Hence , there is a growing need for automatic classification of cancer data .
The objective of this research is to lead specialists analyzing the breast cancer patients effectively . This research by analyzing 2010 breast cancer patients 1 ) attempts to propose six different statistical models using parametric and semi-parametric approaches for survival analysis of breast cancer patients , 2 ) compares the performance capabilities of the proposed statistical models analytically , and 3 ) addresses the most superior approach for a survival analysis of a breast cancer . To analyze the capability of the six proposed models Akaike term is used .
The performances of these applications demonstrate the high quality of our method . Based on the present work , our method can be widely used to discriminate different cancer subtypes , which will contribute to the development of technology for the recovery of the cancer .
Pulmonary nodules usually act as the early stage characterization of lung cancer . It 's a crucial way to control the disease and reduce the mortality by further examination on the diagnosis of pulmonary nodules .
Evolutionary Algorithms ( EAs ) have demonstrated to be useful for solving optimization problems in a heuristic way . This chapter presents a comparative study related to the prediction of cancer cells based on Machine Learning and Evolutionary Algorithms . As well as , a brief introduction of machine learning and evolutionary technics is presented .
Targeted cancer drugs have been developed to interfere with specific molecular targets , which are expected to affect the growth of cancer cells in a way that can be characterized by multi-omics data . The prediction of cancer drug sensitivity simultaneously for multiple drugs based on heterogeneous multi-omics data ( e.g. , mRNA expression , DNA copy number or DNA mutation ) is an important but challenging task . We use joint penalized regression models for multiple cancer drugs rather than a separate model for each drug , thus being able to address the correlation structure between drugs .
The prediction of cell-lines sensitivity to a given set of compounds is a very important factor in the optimization of in-vitro assays . To date , the most common prediction strategies are based upon machine learning or other quantitative structure-activity relationships ( QSAR ) based approaches .
Furthermore , we found that this noise reduction method is robust and effective even when a large amount of artificial noise is added to the input data . ConclusionWe found that our extended off-subspace noise-reduction method combining heterogeneous biological data is successful and quite useful to improve prediction of human cell cancer dru responses from microarray data .
Results We introduce a probabilistic framework to incorporate expert feedback about the impact of genomic measurements on the outcome of interest and present a novel approach to collect the feedback efficiently , based on Bayesian experimental design . The new approach outperformed other recent alternatives in two medical applications : prediction of metabolic traits and prediction of sensitivity of cancer cells to different drugs , both using genomic features as predictors . Furthermore , the intelligent approach to collect feedback reduced the workload of the expert to approximately 11 % , compared to a baseline approach .
BackgroundThe success of targeted anti-cancer drugs are frequently hindered by the lack of knowledge of the individual pathway of the patient and the extreme data requirements on the estimation of the personalized genetic network of the patient ’s tumor . The prediction of tumor sensitivity to targeted drugs remains a major challenge in the design of optimal therapeutic strategies . The current sensitivity prediction approaches are primarily based on genetic characterizations of the tumor sample .
A comparison study between horn and vivaldi antennas for 1.5–6 GHz breast microwave radar imaging Analysis of forward scattering data for microwave breast imaging Nonlinear Parametric and Neural Network Modelling for Medical Image Classification Thermal distribution analysis of three-dimensional tumor-embedded breast models with different breast density compositions Breast Cancer Detection Using Image Processing Techniques Genetic Algorithms for Feature Selection in Computer-Aided Diagnosis Book reviews, NIBIB, and IOM breast cancer report Development algorithm- computer program of digital mammograms Segmentation for detection of masses breast using Marker-Controlled Watershed in MATLAB environment Expedited Breast Care: A New Model in Breast Health A Novel Approach based on Average Information Parameters for Investigation and Diagnosis of Lung Cancer using ANN Lung CT Parenchyma Segmentation using VGG-16 based SegNet Model Application of Survival Analysis on Analysing the Association Between Chromosomal Aberrations and Carcinoma Predicting lung cancer incidence from air pollution exposures using shapelet-based time series analysis Lung cancer detection using Convolutional Neural Network (CNN) Deep convolutional neural network-based lung cancer preventing self-service health cloud service system Application of Neural Networks in Diagnosing Cancer Disease using Demographic Data Adversarial Augmentation for Enhancing Classification of Mammography Images Aplikasi Sistem Pendukung Keputusan Stadium Penyakit Kanker Payudara Menggunakan Logika Fuzzy Berbasis Android The Application of the Support System Decision Stadium of Breast Cancer Using Fuzzy Logic Based Android Imaging in Radiation Therapy Analysis of Skin Cancer using K-Means Clustering and Hybrid Classification Model Discovery of Novel CXCR2 Inhibitors Using Ligand-Based Pharmacophore Models Optical Imaging of Primary Tumors Adaptive Model to Estimate Most Significant Features for Oversampling Medical Data A Comparative Study of Survival approaches for Breast Cancer Patients Gene Selection Based on Mutual Information for the Classification of Multi-class Cancer Computer-Aided Detection for Pulmonary Nodules Base on the Morphological and Spatial Features Cancer Cell Prediction Using Machine Learning and Evolutionary Algorithms Incorporating correlations between drugs and heterogeneity of multi-omics data in structured penalized regression for drug sensitivity prediction Cell fishing: A similarity based approach and machine learning strategy for multiple cell lines-compound sensitivity prediction Network-based de-noising improves prediction from microarray data Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge A new approach for prediction of tumor sensitivity to targeted drugs based on functional data
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Digital Mammography / IWDM Applications of Computational Intelligence in Biomedical Technology BHI ICIC (3)
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