Approaches for Analysing Ultrasound Images Using Image Processing and Machine Learning Techniques

Sahaya Mercy A SJC, Dr. G. Arockia Sahaya Sheela — Tue, 31 Mar 2026 00:00:00 +0000

Abstract

Objective: This study's primary objective is to examine different machine learning and image processing techniques for ultrasound picture analysis. In order to facilitate early diagnosis in medical field, new innovative skills should be introduced to procure accurate result of ultrasound images. Methods: The study pre-processes ultrasonic pictures using sophisticated image processing methods like feature extraction, edge detection, and filtering. Machine learning techniques, such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and decision trees, are used to classify and segregate pertinent information in the ultrasound scans. In order to determine the most effective approaches for precise analysis, the study compares the effectiveness of machine learning models with conventional image processing methods. Findings: The findings demonstrate that machine learning-based strategies, especially deep learning approaches, perform faster and more accurately than conventional image processing techniques. Specifically, CNNs show excellent accuracy in identifying and classifying important anatomical characteristics in ultrasound pictures. In order to elevate model performance, the study also emphasizes the difficulties associated with data annotation and the requirement for sizable obtained datasets. Novelty: In order to give a thorough comparison for ultrasound image analysis, this work presents a novel methodology by fusing contemporary machine learning algorithms with conventional image processing techniques. Additionally, the study investigates how several machine learning models might be integrated to produce hybrid solutions that maximize diagnostic results. By simplifying medical imaging processes, the suggested framework may improve diagnostic precision and lower human error.

The Exponential Scaling in Quantum Science: Origins, Implications, and Opportunities across Chemistry and Quantum Technologies

HEMA RAVURI — Tue, 31 Mar 2026 00:00:00 +0000

The Exponential scaling is a defining characteristic of quantum science that underpins both its transformative computational potential and its profound theoretical challenges. Unlike classical systems whose state spaces typically scale linearly or polynomially with system size, quantum systems exhibit exponential growth of Hilbert space dimensionality as the number of quantum degrees of freedom increases. This scaling governs quantum information storage, entanglement complexity, quantum simulation capabilities, and the difficulty of classical emulation of quantum phenomena. This article examines the physical origins of exponential scaling, its implications across quantum computing, quantum many-body physics, quantum sensing, and quantum communication, and the emerging strategies developed to harness or mitigate exponential complexity. The discussion highlights how exponential scaling simultaneously represents the power and the bottleneck of modern quantum technologies.

International Journal of Information Technology, Research and Applications

Approaches for Analysing Ultrasound Images Using Image Processing and Machine Learning Techniques

The Exponential Scaling in Quantum Science: Origins, Implications, and Opportunities across Chemistry and Quantum Technologies