Authors: V. V. Nagamani, Sirigiri Lakshman Sai, Puram Pavan, Chilukuru Venkata Sai
DOI Link: https://doi.org/10.22214/ijraset.2024.60399
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: This paper presents a project that addresses Detection Of Leukemia Disease Using Image Processing and Machine Learning. This research proposes an approach that combines image processing techniques with machine learning algorithms to detect leukemia in microscopic blood smear images at a stage. The proposed method involves enhancing the images through preprocessing identifying the regions of interest through segmentation and extracting features using image processing algorithms. Afterward a machine learning model trained on a dataset of annotated images is employed to classify the samples as either positive or negative, for leukemia. The effectiveness of this approach is evaluated using metrics like accuracy, sensitivity and specificity. Experimental results show promising outcomes in detecting leukemia which could serve as a tool for healthcare professionals, in early diagnosis and treatment planning. By integrating this framework into practice it has the potential to improve efficiency and accuracy in diagnosing leukemia while ultimately leading to patient outcomes and enhanced healthcare management.
I. INTRODUCTION
Leukemia is the abnormal proliferation of white blood cells in blood which requires prompt detection and treatment. Traditional methods of diagnosis rely on the manual examination of microscopic blood smears by expert pathologists. This tradition is fraught with challenges such as being highly laborious, time consuming, and prone to subjectivity. In this paper, we propose an integrated methodology, which combines image processing and machine learning for automated leukemia identification. From these sophisticated algorithms, quantitative features are extracted, and machine learning is applied for effective categorization. The proposed framework attempts to improve diagnosis precision and expedite the process.
The contribution of this paper is to illustrate the potential of the integration to redefine leukemia diagnosis. We have analyzed existing literature and our methodology is systematically presented. The primary aim of research is to automate the analysis of blood smear images. We believe this research is of significant interest to clinicians and the general reader. Furthermore, it could become an invaluable tool to automate the diagnosis of leukemia.
II. LITERATURE REVIEW
III. PROPOSED SYSTEM
The proposed leukemia detection approach enhances cell identification by combining image processing and machine learning. It outperforms conventional approaches by utilizing strong algorithms that assess cellular features in pictures.
This union enhances diagnostic precision, which is crucial for leukemia detection and treatment planning.The advantages of this proposed system include:
IV. METHODOLOGY
The goal is to create an image processing system for blood cell counting, which is essential in leukemia identification. Leukemia is typically accompanied with a drop in white blood cell count, which influences illness incidence. The white blood cell to red blood cell ratio is helpful in prognosis.
The strategy, which emphasizes affordability, helps hematologists forecast results. Previous research developed a low-cost image processing tool for this purpose. The technique involves using this equipment to perform precise cell count analysis in order to detect leukemia. The following steps outline our methodology:
V. ARCHITECTURE
VI. ACKNOWLEDGMENT
The group express our gratitude most sincerely to our guide Ms.V.V.Nagamani who guided and motivated us in this course of time of understanding the concepts. We are grateful for the insightful comments offered by the peer reviewers.
In conclusion, we involved in detection the types of leukemia using microscopic blood sample images. We built the system based on typical microscopic images features by observing changes in texture, geometry, colors and statistical analysis as a classifier input. We require our system to be efficient, reliable, less processing time, smaller error, high accuracy, cheaper cost and must be robust towards varieties from individual, sample collection protocols, time and etc. The information extracted from the microscopic images of blood samples will be benefited to the people of being able to predict, solve and treat blood diseases immediately for a particular patient.
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Copyright © 2024 V. V. Nagamani, Sirigiri Lakshman Sai, Puram Pavan, Chilukuru Venkata Sai. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET60399
Publish Date : 2024-04-16
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here