Thank you for reading principles of optics electromagnetic theory of propagation interference and diffraction of light. As you may know, people have search hundreds times for their favorite novels like this principles of optics electromagnetic theory of propagation interference and diffraction of light, but end up in harmful downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some infectious bugs inside their computer.

Principles Of Optics Electromagnetic Theory Of Propagation Interference And Diffraction Of Light

Traditionally, physicians need to manually delineate the suspected breast cancer area. Numerous studies have mentioned that manual segmentation takes time, and depends on the machine and the operator. The algorithm called Convolutional Neural Network Improvement for Breast Cancer Classification (CNNI-BCC) is presented to assist medical experts in breast cancer diagnosis in timely manner. The CNNI-BCC uses a convolutional neural network that improves the breast cancer lesion classification in order to help experts for breast cancer diagnosis. CNNI-BCC can classify incoming breast cancer medical images into malignant, benign, and healthy patients. The application of present algorithm can assist in classification of mammographic medical images into benign patient, malignant patient and healthy patient without prior information of the presence of a cancerous lesion. The presented method aims to help medical experts for the classification of breast cancer lesion through the implementation of convolutional neural network for the classification of breast cancer. CNNI-BCC can categorize incoming medical images as malignant, benign or normal patient with sensitivity, accuracy, area under the receiver operating characteristic curve (AUC) and specificity of 89.47%, 90.50%, 0.901 ± 0.0314 and 90.71% respectively.

Convolutional neural network improvement for breast cancer classification

Early-stage cancer detection could reduce breast cancer death rates significantly in the long-term. The most critical point for best prognosis is to identify early-stage cancer cells. Investigators have studied many breast diagnostic approaches, including mammography, magnetic resonance imaging, ultrasound, computerized tomography, positron emission tomography and biopsy. However, these techniques have some limitations such as being expensive, time consuming and not suitable for young women. Developing a high-sensitive and rapid early-stage breast cancer diagnostic method is urgent. In recent years, investigators have paid their attention in the development of biosensors to detect breast cancer using different biomarkers. Apart from biosensors and biomarkers, microwave imaging techniques have also been intensely studied as a promising diagnostic tool for rapid and cost-effective early-stage breast cancer detection. This paper aims to provide an overview on recent important achievements in breast screening methods (particularly on microwave imaging) and breast biomarkers along with biosensors for rapidly diagnosing breast cancer.

https://www.mdpi.com/1424-8220/17/7/1572/pdf

Early Diagnosis of Breast Cancer.

Breast cancer is one of the leading cancer type among women in worldwide. Many breast cancer patients die every year due to the late diagnosis and treatment. Thus, in recent years, early breast cancer detection systems based on patient's imagery are in demand. Deep learning attracts many researchers recently and many computer vision applications have come out in various environments. Convolutional neural network (CNN) which is known as deep learning architecture, has achieved impressive results in many applications. CNNs generally suffer from tuning a huge number of parameters which bring a great amount of complexity to the system. In addition, the initialization of the weights of the CNN is another handicap that needs to be handle carefully. In this paper, transfer learning and deep feature extraction methods are used which adapt a pre-trained CNN model to the problem at hand. AlexNet and Vgg16 models are considered in the presented work for feature extraction and AlexNet is used for further fine-tuning. The obtained features are then classified by support vector machines (SVM). Extensive experiments on a publicly available histopathologic breast cancer dataset are carried out and the accuracy scores are calculated for performance evaluation. The evaluation results show that the transfer learning produced better result than deep feature extraction and SVM classification.

/pdf/transfer-learning-based-histopathologic-image-classification-2xxadvwrz3.pdf

Transfer learning based histopathologic image classification for breast cancer detection

Recent advances in biosensor for detection of lung cancer biomarkers

Breast cancer is the leading cause of death among females, early diagnostic methods with suitable treatments improve the 5-year survival rates significantly. Microwave breast imaging has been reported as the most potential to become the alternative or additional tool to the current gold standard X-ray mammography for detecting breast cancer. The microwave breast image quality is affected by the microwave sensor, sensor array, the number of sensors in the array and the size of the sensor. In fact, microwave sensor array and sensor play an important role in the microwave breast imaging system. Numerous microwave biosensors have been developed for biomedical applications, with particular focus on breast tumor detection. Compared to the conventional medical imaging and biosensor techniques, these microwave sensors not only enable better cancer detection and improve the image resolution, but also provide attractive features such as label-free detection. This paper aims to provide an overview of recent important achievements in microwave sensors for biomedical imaging applications, with particular focus on breast cancer detection. The electric properties of biological tissues at microwave spectrum, microwave imaging approaches, microwave biosensors, current challenges and future works are also discussed in the manuscript.

Microwave Sensors for Breast Cancer Detection

Early diagnosis of lung cancer helps to reduce the cancer death rate significantly. Over the years, investigators worldwide have extensively investigated many screening modalities for lung cancer detection, including computerized tomography, chest X-ray, positron emission tomography, sputum cytology, magnetic resonance imaging and biopsy. However, these techniques are not suitable for patients with other pathologies. Developing a rapid and sensitive technique for early diagnosis of lung cancer is urgently needed. Biosensor-based techniques have been recently recommended as a rapid and cost-effective tool for early diagnosis of lung tumor markers. This paper reviews the recent development in screening and biosensor-based techniques for early lung cancer detection.

https://www.mdpi.com/1424-8220/17/10/2420/pdf

Screening and Biosensor-Based Approaches for Lung Cancer Detection

This paper presents a new 2D holographic microwave imaging array (HMIA) technique for medical imaging applications. The HMIA technique has been applied to early stage breast cancer detection and brain stroke detection. Computer models are developed to demonstrate the feasibility of detecting and localizing small brain strokes within a 2D numerical head model and breast tumours within a 3D numerical breast model using the HMIA technique. Experimental validation of the HMIA simulation model using a breast phantom has been undertaken and demonstrated a good agreement between experimental and simulated images. Simulation and experimental results showed that the proposed HMIA technique has the potential to become a powerful medical screening and diagnostic tool.

/pdf/holographic-microwave-imaging-for-medical-applications-4vhogbfge6.pdf

Holographic microwave imaging for medical applications

This paper presents a multi-slot coaxial antenna with a pi impedance matching network for liver tumor ablation. A multi-slot radiating probe was optimized by using the modified genetic algorithm to produce a near-spherical heating zone with significantly increased possibility of conformal treatment. A pi impedance matching network was designed to match the feeding transmission line and antenna without increasing antenna size. The reflection coefficient, ablation zone shape, specific absorption rate, and temperature were determined by a finite element electromagnetic simulation using COMSOL. Experimental validations were designed to evaluate the proposed antenna. Both simulation and experimental results show that the proposed antenna has the ability for liver tumor ablation, which offers faster heating rates in the heating center and more localized heating distribution than the conventional single-slot antenna.

https://iopscience.iop.org/article/10.1088/1361-6560/aad9c5/pdf

Lulu Wang

Papers

Early Diagnosis of Breast Cancer.

Microwave Sensors for Breast Cancer Detection

Screening and Biosensor-Based Approaches for Lung Cancer Detection

Holographic microwave imaging for medical applications

A multi-slot coaxial microwave antenna for liver tumor ablation