Detection of leukemia in human blood sample based on microscopic images

Abstract : At the moment, identification of blood disorders is through visual inspection of microscopic images of blood cells. From the identification of blood disorders, it can lead to classification of certain diseases related to blood. This paper describes a preliminary study of developing a detection of leukemia types using microscopic blood sample images. Analyzing through images is very important as from images, diseases can be detected and diagnosed at earlier stage. From there, further actions like controlling, monitoring and prevention of diseases can be done. Images are used as they are cheap and do not require expensive testing and lab equipments. The system will focus on white blood cells disease, leukemia. The system will use features in microscopic images and examine changes on texture, geometry, color and statistical analysis. Changes in these features will be used as a classifier input. A literature review has been done and Reinforcement Learning is proposed to classify types of leukemia. A little discussion about issues involved by researchers also has been prepared.

 EXISTING SYSTEM :

 Leukemia occurs when lot of abnormal white blood cells produced by the bone marrow. Hematologist makes use of microscopic study of human blood, which leads to need of methods, including microscopic color imaging, segmentation, classification and clustering that can allow identification of patients suffering from Leukemia. The microscopic images will be inspected visually by hematologists and the process is time consuming and tiring. The automatic image processing system is urgently needed and can overcome related constraints in visual inspection. The proposed system will be on microscopic images to detect Leukemia. The early and fast identification of Leukemia greatly aids in providing the appropriate treatment. Initial segmentation is done using Statistical parameters such as mean, standard deviation which segregates white blood cells from other blood components i.e. erythrocytes and platelets. Geometrical features such as area, perimeter of the white blood cell nucleusis investigated for diagnostic prediction of Leukemia.

 DISADVANTAGE :

 ? The number of counting blood cells will then be used to calculate the ratio of blood cells for leukemia detection. ? Cytogenetic test takes blood or bone marrow to help identify individual chromosomes. It shows abnormalities in chromosomes, which help to diagnosis and identify the type of leukemia. Results are usually available within 3 weeks.

 PROPOSED SYSTEM :

 The proposed method is successfully applied to a large number of images, showing promising results for varying image quality. Different image processing algorithms such as Image Enhancement, Thresholding, Mathematical morphology and Labeling are implemented using Lab VIEW and MATLAB.

 ADVANTAGE :

 ? The proposed method and the result showed that the method managed to Obtain qualitatively good segmentation results. ? Accuracy is high ? High reliability ? There is no chance of error occurrence ? .Speed of output extraction is fast

Deep Learning Covid-19 outbreak

MACHINE LEARNING UNDERSTANDING DEPTH

MACHINE LEARNING UNDERSTANDING DEPTH

MACHINE LEARNING 3-D DECONVOLUTIONAL NETWORKS

MACHINE LEARNING INTER-TURN SHORT-CIRCUIT

MACHINE LEARNING BASED ON JOURNALING

MACHINE LEARNING FUSING SELL-SIDE ANALYST

MACHINE LEARNING FAIRNESS IN SEMI-SUPERVISED LEARNING

MACHINE LEARNING ENRICHING THE TRANSFER LEARNING

MACHINE LEARNING AUTOMATED SCREENING SYSTEM

MACHINE LEARNING AN ONLINE TRANSFER LEARNING

MACHINE LEARNING THE HIDDEN SEXUAL MINORITIES

MACHINE LEARNING DEPTH ESTIMATION

MACHINE LEARNING A SYSTEMATIC LITERATURE

MACHINE LEARNING FREEDOM IN MANUAL TEXT

MACHINE LEARNING A SYSTEMATIC REVIEW

MACHINE LEARNING key based security

MACHINE LEARNING Deep Learning

MACHINE LEARNING EMOJI TEXT

MACHINE LEARNING EDGE COMPUTING MULTI-OBJECTIVE