scholarly journals An Automated Detection of Leukemia

2021 ◽  
Vol 9 (8) ◽  
pp. 1977-1981
Author(s):  
Ajay Rajaraman
Keyword(s):  
Image Processing ◽  
User Interface ◽  
Random Forest ◽  
Graphical User Interface ◽  
Blood Cells ◽  
Visual Inspection ◽  
Automated Detection ◽  
Blood Disorders ◽  
Preliminary Study

Abstract: Currently, the identification of blood disorders is through visual inspection of microscopic images of the blood cells. The identification of blood disorders can lead to the classification of certain diseases related to blood. This paper describes a preliminary study of developing the detection of leukemia types using microscopic blood sample images. Analyzing through images is very important because diseases can be detected and diagnosed at an earlier stage. From there further actions like controlling, monitoring, and prevention of diseases can be done. Keywords: Image processing; leukemia detection; Lymphocytes; Myelocytes; Random Forest; Graphical User Interface.

Detection of Leukemia Using Image Processing

2018 ◽  
Vol 8 (4) ◽  
pp. 61
Author(s):  
A. Sivasangari ◽  
G. Sasikumar
Keyword(s):  
Image Processing ◽  
Statistical Analysis ◽  
Early Detection ◽  
Visual Inspection ◽  
Causes Of Death ◽  
Cure Rate ◽  
Blood Disorders ◽  
Detection And Diagnosis ◽  
The Moment ◽  
Early Detection And Diagnosis

Leukemia   disease   is one   of    the   leading   causes   of death   among   human. Its  cure  rate and  prognosis   depends   mainly   on  the  early  detection   and  diagnosis  of   the  disease. At  the  moment, identification  of  blood  disorders  is  through   visual  inspection  of  microscopic  images  by  examining  changes  like  texture, geometry, colour  and   statistical  analysis  of  images . This  project  aims  to  preliminary  of  developing  a  detection  of  leukemia  types  using   microscopic  blood  sample using MATLAB. Images  are  used  as  they  are  cheap  and  do  not  expensive  for testing  and  lab  equipment.

scholarly journals Classification of texture using random box counting and binarization methods

2021 ◽  
Vol 10 (1) ◽  
pp. 533-540
Author(s):  
Wijdan Jaber AL-kubaisy ◽  
Maha Mahmood
Keyword(s):  
Image Processing ◽  
Fractal Dimension ◽  
Blood Cells ◽  
Multifractal Analysis ◽  
Vision System ◽  
White Blood Cells ◽  
Real Life ◽  
Self Similarity ◽  
Box Counting

The heterogeneous texture classifications with the complexity of structures provide variety of possibilities in image processing, as an example of the multifractal analysis features. The task of texture analysis is a highly significant field of study in the field of machine vision. Most of the real-life surfaces exhibit textures and an efficiently modelled vision system must have the ability to deal with this variety of surfaces. A considerable number of surfaces maintain a self-similarity quality as well as statistical roughness at different scales. Fractals could provide a great deal of advantages; also, they are popular in the process of modelling these properties in the tasks related to the field of image processing. With two distinct methods, this paper presents classification of texture using random box counting and binarization methods calculate the estimation measures of the fractal dimension BCM. There methods are the banalization and random selecting boxes. The classification of the white blood cells is presented in this paper based on the texture if it is normal or abnormal with the use of a number of various methods.

scholarly journals HDG-select: A novel GUI based application for gene selection and classification in high dimensional datasets

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0246039
Author(s):  
Shilan S. Hameed ◽  
Rohayanti Hassan ◽  
Wan Haslina Hassan ◽  
Fahmi F. Muhammadsharif ◽  
Liza Abdul Latiff
Keyword(s):  
Machine Learning ◽  
User Interface ◽  
Graphical User Interface ◽  
Efficient Algorithm ◽  
Gene Selection ◽  
High Dimensional ◽  
Competitive Performance ◽  
User Friendly ◽  
High Dimensional Datasets

The selection and classification of genes is essential for the identification of related genes to a specific disease. Developing a user-friendly application with combined statistical rigor and machine learning functionality to help the biomedical researchers and end users is of great importance. In this work, a novel stand-alone application, which is based on graphical user interface (GUI), is developed to perform the full functionality of gene selection and classification in high dimensional datasets. The so-called HDG-select application is validated on eleven high dimensional datasets of the format CSV and GEO soft. The proposed tool uses the efficient algorithm of combined filter-GBPSO-SVM and it was made freely available to users. It was found that the proposed HDG-select outperformed other tools reported in literature and presented a competitive performance, accessibility, and functionality.

scholarly journals Automated Detection of White Blood Cells Cancer Diseases

2019 ◽  
Vol 8 (6S) ◽  
pp. 366-369
Keyword(s):  
Side Effects ◽  
Random Forest ◽  
Blood Cells ◽  
White Blood Cells ◽  
Random Forest Classifier ◽  
Automated Detection ◽  
Learning Approach ◽  
Malignant Growth ◽  
Cancer Diseases

Mechanized analysis of white platelets malignant growth infections, for example, Leukemia and Myeloma is a difficult biomed-ical inquire about point. Our methodology introduces out of the blue another best in class application that helps with diagnosing the white platelets infections. we break these sicknesses into two classifications, every classification contains like side effects infections that may confound in diagnosing. In light of the specialist's determination, one of two methodologies is actualized. Each methodology is connected on one of the two maladies classification by processing distinctive highlights. At last, Random Forest classifier is connected for ultimate choice. The proposed methodology means to early disclosure of white platelets malignancy, decrease the misdiagnosis cases notwithstanding improve the framework learning approach. In addition, permitting the specialists just to have the last tuning on the outcome acquired from the framework. The proposed methodology accomplished an exactness of 93% in the principal classification and 95% in the second class.

Classification of wheat seeds using image processing and fuzzy clustered random forest

2020 ◽  
Vol 16 (2) ◽  
pp. 123
Author(s):  
Parminder Singh ◽  
Anand Nayyar ◽  
Simranjeet Singh ◽  
Avinash Kaur
Keyword(s):  
Image Processing ◽  
Random Forest ◽  
Wheat Seeds
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