Detection of Ovarian Tumors in Obstetric Ultrasound Imaging Using Logistic Regression Classifier With an Advanced Machine Learning Approach

Abstract

This paper addresses the use in different stages of pregnancy of ultrasound imaging and to examine the tumors diagnosed during lactation or pregnancy. There are recent advancements in the application of obstetric ultrasound and imaging techniques helpful for improving the outcome of the pregnancy using various Learning techniques. This paper addresses the need to implement sustainable ultrasound standards with an acceptably high maternal and perinatal mortality rates to provide better and more affordable, quality Ultrasonic Flaw (UT) equipment which can improve Obstetric health care. The stateof-the-art learning approach for obstetric ultrasound is a category of methods in machine learning that are gaining popularity and attracting interest in various fields, including image processing and computer vision. In this paper advanced Machine learning processes map a raw input image to the desired output image using logistic regression classifier(LRC) and Convolution neural networks (CNNs) are of particular interest among all Machine learning methods. Furthermore, we have utilized the Internet of Medical Things (IoMT) for obstetric tumor image segmentation and identification of tumors for the medical experts. The experimental results show the LRC based on CNN can be utilized to predict the output of the ultrasound of obstetric with increased maternal and perinatal mobility rates. INDEX TERMS Machine learning, convolution neural network, logistic regression model, obstetric ultrasound, IoMT. I. BACKGROUND AND INTRODUCTION

Power System Fault Classification and Prediction Based on a Three-Layer Data Mining Structure

“…The proposed method is compared with other methods to accomplish its algorithm verification, which further illustrates the effectiveness and scalability of the proposed method. The regression algorithms involved in the comparison include logistic regression [37], SVM [38][39], random forest [40][41], SGD and the proposed method. The constructed model is shown in Fig.…”

Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry

“…(4) More test data for different time periods than in this study can be used for further reverifying the study performance. (5) The inclusion of additional evaluation criteria is a requirement for further measuring the proposed model. ( 6) Other potential models can be organized with the proposed hybrid model to further measure and test the proposed model.…”

“…Furthermore, from the limited examples of extensive literature reviews on data mining techniques, it is found that some effective classifiers (or called models) have emerged in various application fields and become popular among practitioners and academics due to their superior performance in the past few decades. These classifiers (algorithms), such as Naive Bayes (NB) [4], logistic (LG) [5], K-nearest neighbor (KNN) [6], bagging (BAG) algorithm [7], and decision trees (DTs) [8], also have good application performance in financial domains; thus, they are selected for use in this study. Moreover, the well-known Altman Z-score model [9] is ever emphasized for such financial predictions as bankruptcy of stock market, and it also provides outstanding results; at the same time, the logistic regression (LR) method [10] is also a helpful technique designed for identifying classification models for financial datasets.…”

“…Given the above evidence, this study thus contributes to identifying the financial attributes that may have a significant impact on the Taiwan stock market by using the financial data of various companies combined with data mining techniques and the LR method, so as to provide a useful reference for enterprise operators to evaluate capital allocation and for small investors to avoid potential financial risks. Importantly, the five main purposes of this study are described as follows: (1) to conduct analysis of the corresponding financial data of listed and delisted companies partitioned with different ratios (i.e., from 1:1 to 5:1) and combine this with a variety of intelligent hybrid classification models for mining technology, (2) to identify the significant financial attributes (also called financial ratios of financial statements) that affect financial bankruptcy prediction, (3) to generate a rule set of a decisional tree structure with highly significant financial attributes to frame knowledgebased framework for bankruptcy prediction, (4) to identify the classifiers or models with better classification accuracy, and (5) to analyze different hybrid classification models to discover common highly significant determinants for influencing financial bankruptcy from a varied view of comparison purposes. By providing the above facts and interests, it is trusted that proposing such an advanced hybrid intelligent classification model in the flow of scientific outputs devoted to financial bankruptcy prediction benefits all the above research purposes; interestingly, the proposed integrated hybrid model is rarely seen in this bankruptcy prediction field, given the limited evidence in the literature.…”

Portable Ultrasound Research System for Use in Automated Bladder Monitoring with Machine-Learning-Based Segmentation

“…In summary, all these devices optimized for the daily clinical routine (or for home-care settings in the case of DFree or SENS-U) are difficult to utilize in research applications, where customized signal and image processing algorithms need to be applied to the data. In particular, machine-learning based approaches have been shown to have tremendous potential for automated segmentation of ultrasound data [ 10 ], and have been reported in particular for breast imaging [ 11 ], coronary arteries [ 12 ], and thyroid [ 13 ] or different tumors [ 14 ]. In comparison to these applications, where the anatomy is more complex and the contrast difference is reduced, bladder segmentation represents an ideal use case for ML-based approaches due to the low echogenity and the resulting high contrast to surrounding tissue.…”