International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
p-ISSN: 2395-0072
www.irjet.net
Fetal Heart Detection Using Machine Learning M. Sri Varsha Shwetha1, K. Aparna2, R. Kirthiga 3, A. Mahalakshmi 4 1 Assistant Professor, Department of Information Technology, Meenakshi College of Engineering, Chennai, Tamil
Nadu, India 2-4Students, Department of Information Technology, Meenakshi College of Engineering, Chennai, Tamil Nadu, India
---------------------------------------------------------------------***--------------------------------------------------------------------can be used to feed into an XGBoost classifier. This model Abstract -An Early detection of congenital heart defects trains itself on the differences between a healthy and abnormal fetal heart with increased accuracy. Reducing subjectivity from the diagnosis process will allow for early intervention, thereby improving the condition of mother and child.
(CHDs) is essential to allow prenatal care and medical treatment in time. This system is a machine learning-based system used for fetal heart assessment from ultrasound images. The system works on an end-to-end basis, processing images through various stages, including preprocessing, feature selection, and classification, to separate normal from abnormal fetal heart images. The workflow begins with image enhancement techniques that are used for preprocessing to maintain uniform conditions across the images. Then, K-means clustering is implemented to automatically cluster the images into normal and abnormal categories. To assist in classification, features are extracted with the help of two complementary approaches: Histogram of Orientated Gradients (HOG), which highlights the shape and structural patterns of the heart, and Local Binary Patterns (LBP), which emphasize the texture details. Finally, these features are fed to an XGBoost classifier to distinguish between the classes. This proposed approach thus allows clinicians to reach fast yet reliable decisions, which in turn ensures better prenatal outcomes and early treatment planning of congenital heart diseases.
2. EXISTING SYSTEM The existing system for heart disease prediction utilizes traditional machine learning methods such as Support Vector Machine (SVM), Random Forests (RF), and logistic regression (LR) on structured clinical data [1][5][8]. Such systems work on datasets like the Cleveland Heart Disease Dataset (CHDD) and a private heart disease dataset amalgamated from various medical centres [4][6]. These systems, however, require manual engineering of features from parameters within tables such as age, sex, cholesterol, blood pressure, and various other clinical indicators. Such features are picked on the basis of expert knowledge and this lack of consistency generally lowers the level of performance of the model and its generalizability across populations [7] [9]. Moreover, the existing systems do not take into account ultrasound images, thereby losing out on critical spatial, shape, or texture features crucial for a diagnosis [2][3][10]. Such limitations are pivotal for complicated diagnoses that rest on visual patterns, particularly for an early-stage detection process. This severely limits, in turn, the capability of traditional models for early detection and diagnosis.
Keywords: Fetal heart detection, Congenital heart defects (CHDs), Ultrasound imaging, Machine learning, Image preprocessing, K-means clustering, Feature extraction, Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), XGBoost classifier, Prenatal diagnosis, Medical image analysis.
1. INTRODUCTION
3. PROPOSED SYSTEM
Congenital heart defects (CHDs) constitute one of the leading reasons for neonatal complications. Their early diagnosis is relevant for timely intervention and appropriate prenatal treatment. In this project, we present a machine-learning-based method for fetal heart assessment through ultrasound images. The stage is set by preprocessing, which entails improving image clarity and standardizing image quality. The cronies, K-means, cluster the images into normal and abnormal patterns by visual pattern matching. Then, the extraction of features takes place: HOG captures shape information and LBP texturerelated information from each image. These features are combined to form a unique signature for each image that
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This work proposes an intelligent fetal heart detection system that uses a machine learning pipeline. It functions through combined shape and texture analysis to classify fetal heart ultrasound images into normal and abnormal. using K-means clustering. Features are extracted using Histogram of Oriented Gradients (HOG) for shape and Local Binary Pattern (LBP) for texture. Then, the XGBoost classifier uses the extracted features to detect the fetal heart condition. The resultant system aims at minimizing manual interpretation errors while also reinforcing earlystage diagnosis accuracies. These predictions and alerts
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