Machine Learning Methods for Breast Cancer Diagnostic

This chapter discusses radio-pathological correlation with recent imaging advances such as machine learning (ML) with the use of technical methods such as mammography and histopathology. Although criteria for diagnostic categories for radiology and pathology are well established, manual detection and grading, respectively, are tedious and subjective processes and thus suffer from inter-observer and intra-observer variations. Two most popular techniques that use ML, computer aided detection (CADe) and computer aided diagnosis (CADx), are presented. CADe is a rejection model based on SVM algorithm which is used to reduce the False Positive (FP) of the output of the Chan-Vese segmentation algorithm that was initialized by the marker controller watershed (MCWS) algorithm. CADx method applies the ensemble framework, consisting of four-base SVM (RBF) classifiers, where each base classifier is a specialist and is trained to use the selected features of a particular tissue component. In general, both proposed methods offer alternative decision-making ability and are able to assist the medical expert in giving second opinion on more precise nodule detection. Hence, it reduces FP rate that causes over segmentation and improves the performance for detection and diagnosis of the breast cancer and is able to create a platform that integrates diagnostic reporting system

Fusion of B‐mode and shear wave elastography ultrasound features for automated detection of axillary lymph node metastasis in breast carcinoma

Abstract: In this study, we evaluate and compare the diagnostic performance of ultrasound for non‐invasive axillary lymph node (ALN) metastasis detection. The study was based on fusing shear wave elastography (SWE) and B‐mode ultrasonography (USG) images. These images were subjected to pre‐processing and feature extraction, based on bi‐dimensional empirical mode decomposition and higher order spectra methods. The resulting nonlinear features were ranked according to their p‐value, which was established with Student's t‐test. The ranked features were used to train and test six classification algorithms with 10‐fold cross‐validation. Initially, we considered B‐mode USG images in isolation. A probabilistic neural network (PNN) classifier was able to discriminate positive from negative cases with an accuracy of 74.77% using 15 features. Subsequently, only SWE images were used and as before, the PNN classifier delivered the best result with an accuracy of 87.85% based on 47 features. Finally, we combined SWE and B‐mode USG images. Again, the PNN classifier delivered the best result with an accuracy of 89.72% based on 71 features. These three tests indicate that SWE images contain more diagnostically relevant information when compared with B‐mode USG. Furthermore, there is scope in fusing SWE and B‐mode USG to improve non‐invasive ALN metastasis detection

Modelling the underpinning factors of word of mouth (WOM) intentions of students in an ODL institution

The purpose of this research is to examine the impact of the predictors – quality of service, students perceived satisfaction, and university image on word of mouth (WOM) intention in an open distance learning (ODL) institution. Understanding the expectation of customers is an important component in the marketing kit. Competitive market among educational institution lead educational institutions to think of ways to improve the marketing strategy. The paper also investigates the mediating effect of university image and student perceived satisfaction on WOM intentions of students on the institution. Online survey questionnaires were distributed to 1012 students who are studying at an ODL institution. The sample is selected from the various learning centres and selection is based on the number of semesters these students have been studying at the institution. For the purpose of the research the sample are those who have completed six semesters of study at the institution. The items in the questionnaire were developed using existing constructs. The findings showed that student perceived satisfaction; quality services and university image have a positive and significant impact on word of mouth intention at p 0.05. This study establishes the fact that the quality of services provided, the image of the university and how students feel about the services are predictors of word of mouth intention of students about the university. (Abstract by authors

Comprehensive Board Diversity and Quality of Corporate Social Responsibility Disclosure: Evidence from an Emerging Market

This study empirically examines the relationship between wide-ranging board diversity and the quality of corporate social responsibility (CSR) disclosure variables in Malaysia. We extend prior literature covering broader dimensions of board diversity (e.g gender,education level,education background,age,tenure,nationality and ethnicity) and their impact on CSR after controlling for board and audit committee characteristics. Using 200 listed firms in Bursa Malaysia during 2009 and 2013 and applying both OLS and 2SLS instrumental variables (IV) approaches, we document significant positive effect of board education level and board tenure diversity on the quality of CSR disclosure. Further analysis using robust regression also shows positive association between gender diversity and CSR disclosure. Our findings also demonstrate that the quality of CSR disclosure is significantly negatively associated with board age and nationality diversity. These results remain consistent with using alternative measures for board diversity, and characteristics for board of director and audit committees as well as split samples between large and small firms. Additional tests exhibit complementary relationship of education level and nationality with gender, while substitutive relationship of age and tenure with gender in influencing CSR. These findings provide useful insights into the policy makers in setting regulations in respect of board diversity in Malaysia and other emerging economies in the Asian region. Our evidence is also useful for listed companies in setting the criteria to identify directors who can support their strategic decisions