Under the alternating electrical excitation, biological tissues produce a complex electrical impedance which depends on tissue composition, structures, health status, and applied signal frequency, and hence the bioelectrical impedance methods can be utilized for noninvasive tissue characterization. As the impedance responses of these tissue parameters vary with frequencies of the applied signal, the impedance analysis conducted over a wide frequency band provides more information about the tissue interiors which help us to better understand the biological tissues anatomy, physiology, and pathology. Over past few decades, a number of impedance based noninvasive tissue characterization techniques such as bioelectrical impedance analysis (BIA), electrical impedance spectroscopy (EIS), electrical impedance plethysmography (IPG), impedance cardiography (ICG), and electrical impedance tomography (EIT) have been proposed and a lot of research works have been conducted on these methods for noninvasive tissue characterization and disease diagnosis. In this paper BIA, EIS, IPG, ICG, and EIT techniques and their applications in different fields have been reviewed and technical perspective of these impedance methods has been presented. The working principles, applications, merits, and demerits of these methods has been discussed in detail along with their other technical issues followed by present status and future trends.

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Bioelectrical Impedance Methods for Noninvasive Health Monitoring: A Review.

Medical images play a very important role in making the right diagnosis for the doctor and in the patient’s treatment process. Using intelligent algorithms makes it possible to quickly distinguish the lesions of medical images, and it is especially important to extract features from images. Many studies have integrated various algorithms into medical images. For medical image feature extraction, a large amount of data is analyzed to obtain processing results, helping doctors to make more accurate case diagnosis. In view of this, this paper takes tumor images as the research object, and first performs local binary pattern feature extraction of the tumor image by rotation invariance. As the image shifts and the rotation changes, the image is stationary relative to the coordinate system. The method can accurately describe the texture features of the shallow layer of the tumor image, thereby enhancing the robustness of the image region description. Focusing on image feature extraction based on convolutional neural network (CNN), the basic framework of CNN is built. In order to break the limitations of machine vision and human vision, the research is extended to multi-channel input CNN for image feature extraction. Two convolution models of Xception and Dense Net are built to improve the accuracy of the CNN algorithm. It can be seen from the experimental results that the CNN algorithm shows high accuracy in tumor image feature extraction. In this paper, the CNN algorithm is compared with several classical algorithms in the local binary mode. The CNN algorithm has more accurate feature extraction ability for tumor CT images on a larger data basis. Furthermore, the advantages of CNN algorithms in this field are demonstrated.

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Research on Feature Extraction of Tumor Image Based on Convolutional Neural Network

Triple-negative breast cancer is the most aggressive breast cancer subtype with limited treatment options and a poor prognosis. Approximately 70% of triple-negative breast cancer patients fail to achieve a pathologic complete response (pCR) after chemotherapy due to the lack of targeted therapies for this subtype. We report here the development of a focal-plane-array Fourier transform infrared (FPA-FTIR) microspectroscopic technique combined with principal component analysis (PCA) for monitoring chemotherapy effects in triple-negative breast cancer patients. The PCA results obtained using the FPA-FTIR spectral data collected from the same patients before and after the chemotherapy revealed discriminatory features that were consistent with the pathologic and clinical responses to chemotherapy, indicating the potential of the technique as a monitoring tool for observing chemotherapy efficacy.

FPA-FTIR Microspectroscopy for Monitoring Chemotherapy Efficacy in Triple-Negative Breast Cancer.

A rapid and highly accurate diagnostic tool for distinguishing benign tumors from malignant ones is required owing to the high incidence of breast cancer. Although various computer-aided diagnosis (CAD) systems have been developed to interpret ultrasound images of breast tumors, feature selection and the setting of parameters are still essential to classification accuracy and the minimization of computational complexity. This work develops a highly accurate CAD system that is based on a support vector machine (SVM) and the artificial immune system (AIS) algorithm for evaluating breast tumors. Experiments demonstrate that the accuracy of the proposed CAD system for classifying breast tumors is 96.67 %. The sensitivity, specificity, PPV, and NPV of the proposed CAD system are 96.67, 96.67, 95.60, and 97.48 %, respectively. The receiver operator characteristic (ROC) area index A
 z
 is 0.9827. Hence, the proposed CAD system can reduce the number of biopsies and yield useful results that assist physicians in diagnosing breast tumors.

An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images

Breast cancer is the most common cancer among females, but breast cancer incidence in young women is low. There are limited studies of breast cancer in this age group in Malaysia, while there are conflicting data regarding these women's prognosis compared to older patients. This study describes the common features presented by breast carcinoma on histopathological and ultrasound findings. Objective: To compare the ultrasound findings and histopathological characteristics of breast cancer for women aged below 40 years and those aged 40 years and above. Methodology: This was a retrospective, cross-sectional study using secondary data, in which the data was collected respectively from patient's clinical histories, radiology findings, and histopathology reports of patients with breast cancer in Hospital Serdang from 1 January 2009 until 31 December 2018. Patients were divided into two age groups (ages below 40, ages 40 and above). Results: 205 patients were included in this study. The most common type of breast carcinoma is invasive ductal carcinoma. Common features are grade II breast cancer cells, DCIS high grade, stage 2 (TNM), tumour size of T2 and lymph node invasion. The majority of tumours are positive with oestrogen receptor, progesterone receptor, and HER2 receptor. Conclusion: Younger age group has a similar feature with the older age group, except they have late stage and progesterone negativity. There is no significant association between age group, ultrasound, histological features, and breast carcinoma receptors.

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Comparison of ultrasound and histopatological findings breast cancer between women aged below and above 40 years.

In this paper, first, how to take good ultrasonic pictures of the breast and make correct diagnosis are discussed. The author stressed the earnest attitude to understand and master the apparatus and correlate ultrasonic pictures with cut surfaces or microscopical figures of the specimen. Secondly, diagnostic criteria preliminarily adopted by the Japan Society of Ultrasonics in Medicine are proposed and are explained practically. The elements of this criteria are shape, border, boundary echoes, internal echoes, posterior echoes, bilateral shadows, changes at surrounding tissues and longitudinal-transverse ratio. Lastly, pictures of the benign and malignant lesions of the breast are shown using typical examples.

Kasumi F

Papers

Ultrasound diagnosis of breast cancer