Enhanced AI-Based Machine Learning Model for Accurate Segmentation and Classification Methods

Aug 3, 2026

Upload by Himbo

Enhanced AI-based machine learning methods address image segmentation and classification by organizing feature extraction, fragmentation, machine vision, and categorization into a structured survey. The study focuses on segmentation and classification of images using machine learning models, drawing on a broad set of research works to compare approaches and highlight performance considerations. In medical image analysis, the document emphasizes clinically meaningful outcomes such as tumor cell classification, blood cell delineation, and tumor localization, where accurate, practical segmentation remains computationally demanding.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Introduction

Segmentation and classification in medical image analysis

Literature Analysis

Segmentation techniques

Cluster-based segmentation

Edge based segmentation

Enhanced Ai-Based Machine Learning Model for an Accurate Segmentation and Classification Methods Dr. G Manikandan1, Bui Thanh Hung2, Dr Siva Shankar S3, Prof. Prasun Chakrabarti4 1PDF Scholar, Professor and Head, Department of Information Technology Kings Engineering College Irrungattukottai [Sriperumbudur 602117 Tamil Nadu. mani4876@gmail.com](Sriperumbudur 602117 Tamil Nadu. mani4876@gmail.com) 2Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh city 12 Nguyen Van Bao street, 4th Ward, Go Vap District , Ho Chi Minh City, Vietnam. [buithanhhung@iuh.edu.vn](buithanhhung@iuh.edu.vn) 3Associate Professor, Department of Computer Science and Engineering, KG Reddy College of Engineering and Technology , Telangana,India [drsivashankars@gmail.com](drsivashankars@gmail.com) 4ITM SLS Baroda University Vadodara Gujarat India [drprasun.cse@gmail.com](drprasun.cse@gmail.com) Abstract: Phone Laser Scanner becomes the versatile sensor module that is premised on Lamp Identification and Spanning methodology and is used in a spectrum of uses. There are several prior editorials in the literary works that concentrate on the implementations or attributes of these processes; even so, evaluations of all those inventive computational techniques reported in the literature have not even been performed in the required thickness. At ToAT that finish, we examine and summarize the latest advances in Artificial Intelligence based machine learning data processing approaches such as extracting features, fragmentation, machine vision, and categorization. In this survey, we have reviewed total 48 papers based on an enhanced AI based machine learning model for accurate classification and segmentation methods. Here, we have reviewed the sections on segmentation and classification of images based on machine learning models. Keywords: Artificial Intelligence, Machine learning, Classification accuracy, Segmentation and classification. I. INTRODUCTION: In clinical diagnosis, prominent information about the data and patients was obtained from the image examination [1. 2] . Both classification and segmentation were popularly utilized used image processing methodologies (IPM) . To interpret the features, the feature analysis and extraction were dependent upon the image classification as well as segmentation methods in medical image (MI) investigation [3, 4] . The tumor cell classification, blood cell delineation, and tumor location are extensive applications [5, 6] . Till recent times, the research of morphological changes using microscopy (EM) has been frequently limited to subjective depiction owing to technical restrictions that precluded the statistical approach of three-dimensional specimens [7, 8] . The creation is an innovative quantity EM technique. Even so, our capacity to evaluate this info has not progressed in the same way; segmenting EM images continues to be a big and moment painstaking system [9, 10] . As a result, in order to fully realize the explanatory possibility of EM, generally applicable and precise assessment remedies are required [11, 12] . A permeation characteristic specifies the covalent bond inside a sturdy, fluid, as well as fuel, so it allows us to quantify the frequency of the discovered image [13] . Hyperspectral information is routinely made up of 100–200 two hundred pop acts of the spectral range. Introduce a lightweight Multiview Attention to successfully segment nuclei and category cells in histopathological images to improve network improve operational precision [14, 15] . II. LITERATURE ANALYSIS In this section, we discussed various kinds of AI oriented machine learning (ML) techniques for accurate classification and segmentation model. 2.1 Segmentation: In this section, different kinds of segmentation techniques such as threshold, multi-level, watershed, cluster based, edge based and region-based segmentation. The splitting of images

FAQs

What is the document’s main focus?

It surveys enhanced AI-based machine learning approaches for accurate image segmentation and classification methods.

How are segmentation and classification used in medical image analysis?

Classification and segmentation support feature analysis and extraction for medical image investigation, enabling tasks such as tumor cell classification, blood cell delineation, and tumor localization.

Which segmentation categories are discussed in the literature analysis?

The document discusses several segmentation techniques, including threshold, multi-level, watershed, cluster-based, edge-based, and region-based methods, with deeper coverage of cluster-based and edge-based segmentation.

Aug 3, 2026

Upload by Himbo