Web-based Approach for Detection of Acute Lymphoblastic Leukemia From Microscopic Blood Cell Images Using Convolutional Neural Network-Reference-Cited by-同舟云学术

Web-based Approach for Detection of Acute Lymphoblastic Leukemia From Microscopic Blood Cell Images Using Convolutional Neural Network

Published:2023-03-22 Issue: Volume: Page:
ISSN:
Container-title:
language:
Short-container-title:

Author:

M Menagadevi¹,M Nirmala²,D Thiyagarajan³,R Rajkumar⁴

Affiliation:

1. Dr NGP Institute of Technology

2. NGPiTECH: Dr NGP Institute of Technology

3. Malla Reddy University

4. Kongu Engineering College

Abstract

Abstract Acute Lymphoblastic Leukemia (ALL) is a blood tumor that affects bone marrow and blood. Leukemia affects young people and adults worldwide. Early diagnosis of leukemia provides a patient with the right treatment option, especially for children. In medical image processing, computational tools play a major role in the field of research. The objective of this paper is to predict the ALL cells using image processing and a Computational Neural Network (CNN). 498 images were collected from the publicly available datasets and the analysis was performed. In the preprocessing stage, noise in the image is eliminated by median and weiner filters and the RGB color leukemia images are converted into lab color images as it enhances segmentation of the nucleus of the image. In this study, the k-means clustering algorithm was applied to segment the nucleus from microscopic blood cell images. Based on the characteristics of the nucleus in the leukemia image, the tumor is detected. CNN takes different features from the input blood sample image and assigns weights to the input image, which helps the network with classification. This model was evaluated with a sensitivity of 98.2%, a specificity of 97%, and an accuracy of 98%. Finally, a flask web page is developed using python to display the output.

Publisher

Research Square Platform LLC

Reference28 articles.

1. Devikanniga, D., Ramu, A., & Haldorai, A. (Jul. 2018). “Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm,”EAI Endorsed Transactions on Energy Web, p.164177,

2. Rehman, A., Abbas, N., Saba, T., Rahman, S. I. U., Mehmood, Z., & Kolivand, H. (2018). “Classification of acute lymphoblastic leukemia using deep learning,'' Microsc. Res. Techn., vol. 81, no. 11, pp. 1310–1317, Nov.

3. Anandakumar, H., R. Arulmurugan 2017 “Region-Based Seed Point Cell Segmentation and Detection for Biomedical Image Analysis”, International Journal of Biomedical Engineering and Technology, Inderscience Publication, Scopus Indexed, S.No – 4868, ISSN: 1752–6426

4. Kumar, D. (2020). "Automatic Detection of White Blood Cancer from Bone Marrow Microscopic Images Using Convolutional Neural Networks," in IEEE Access, vol. 8, pp. 142521–142531,

5. Kasmin, F., Prabuwonw, A. S., & Abdullah, A., “Detection of Leukemia in human blood sample based on microscopic images: A Study”.Journal of Theoretical and Applied Information Technology volume46, issue 2, pp.579–586.