Comparison of Implementation in Blood Cancer Causes and Diseases

Tripti R, Kulkarni and Bharathi, Gururaj and Aditi, Jaiswal (2025) Comparison of Implementation in Blood Cancer Causes and Diseases. International Journal of Trend in Scientific Research and Development, 9 (1). pp. 503-512. ISSN 2456-6470

Text ijtsrd73869.pdf Download (1MB)

Abstract

Blood malignancies are extremely dangerous for human life. Early and accurate detection is essential for efficient treatment and improved patient outcomes. Traditional diagnostic methods can be subjective and time-consuming. Delays in diagnosis can lead to life-threatening complications, as some blood cancers progress rapidly. This work explores the transformative potential of Machine Learning (ML) and Deep Learning (DL) in blood cancer detection. Support Vector Machine (SVM) and other machine learning methods and K Nearest Neighbour (KNN) analyze blood cell images and identify cancerous cell features, achieving high accuracy in leukemia detection. This allows for faster and more objective diagnoses, potentially leading to earlier interventions and improved patient outcomes. Deep Learning approaches, particularly Convolutional Neural Networks (CNNs), hold even greater promise. The requirement for manual feature extraction is eliminated by CNNs' ability to automatically learn features from images. The integration of ML and DL significantly improves blood cancer detection accuracy and efficiency. This paves the way for earlier diagnoses, improved patient care, and ultimately, saving lives. This work concludes by pointing forth possible directions for more study, such as improving these methods even more.

Item Type:	Article
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	27 Jan 2025 07:23
Last Modified:	27 Jan 2025 07:23
URI:	http://eprints.umsida.ac.id/id/eprint/15300

Actions (login required)

View Item