Classification of blast cell type on acute myeloid leukemia (AML) based on image morphology of white blood cells

AML is one type of cancer of the blood and spinal cord. AML has a number of subtypes including M0 and M1. Both subtypes are distinguished by the dominant blast cell type in the WBC, the myeloblast cells, promyelocyte, and myelocyte. This makes the diagnosis process of leukemia subtype requires identification of blast cells in WBC. Automatic blast cell identification is widely developed but is constrained by the lack of data availability, and uneven distribution for each type of blast cell, resulting in problems of data imbalance. This makes the system developed has poor performance. This study aims to classify blast cell types in WBC identified AML-M0 and AML-M1. The method used is divided into two stages, first pre-processing, image segmentation and feature extraction. The second stage, perform resample, which is continued over sampling with SMOTE. The process is done until the amount of data obtained is relatively the same for each blast cell, then the process of elimination of data duplication, randomize, classification and performance measurement. The validation method used is k-fold cross-validation with k=10. Performance parameters used are sensitivity, specifyicity, accuracy, and AUC. The average performance resulting from classification of cell types in AML with Random Forest algorithm obtained 82.9% sensitivity, 92.1% specificity, 89.6% accuracy and 87.5% AUC. These results indicate a significant improvement compared to the system model without using SMOTE. The performance generated by reference to the AUC value, the proposed system model belongs to either category, so it can be used for further stages of leukemia subtype AML-M0 and AML-M1

Investigation of Different Classification Models to Determine the Presence of Leukemia in Peripheral Blood Image

The counting and classification of blood cells allows the evaluation and diagnosis of a vast number of diseases, such as the ALL - Acute Lymphocytic Leukemia, detected through the analysis of white blood cells (WBCs). Nowadays the morphological analysis of blood cells is performed manually by skilled operators, involving numerous drawbacks, such as slowness of the analysis and a non-standard accuracy, dependent on the operator skills. In literature there are only few examples of automated systems able to process a whole image in order to analyze and classify all the WBCs included. This paper presents a complete and fully automatic method for WBCs identification from microscopic images and an evaluation of different classification model to determine the presence of leukemia. Experimental results show that the proposed method is able to identify the cells carrying leukemia and consequently to determine whether a patient is suffering from this disease

Computer aided diagnosis algorithms for digital microscopy

Automatic analysis and information extraction from an image is still a highly chal- lenging research problem in the computer vision area, attempting to describe the image content with computational and mathematical techniques. Moreover the in- formation extracted from the image should be meaningful and as most discrimi- natory as possible, since it will be used to categorize its content according to the analysed problem. In the Medical Imaging domain this issue is even more felt because many important decisions that affect the patient care, depend on the use- fulness of the information extracted from the image. Manage medical image is even more complicated not only due to the importance of the problem, but also because it needs a fair amount of prior medical knowledge to be able to represent with data the visual information to which pathologist refer. Today medical decisions that impact patient care rely on the results of laboratory tests to a greater extent than ever before, due to the marked expansion in the number and complexity of offered tests. These developments promise to improve the care of patients, but the more increase the number and complexity of the tests, the more increases the possibility to misapply and misinterpret the test themselves, leading to inappropriate diagnosis and therapies. Moreover, with the increased number of tests also the amount of data to be analysed increases, forcing pathologists to devote much time to the analysis of the tests themselves rather than to patient care and the prescription of the right therapy, especially considering that most of the tests performed are just check up tests and most of the analysed samples come from healthy patients. Then, a quantitative evaluation of medical images is really essential to overcome uncertainty and subjectivity, but also to greatly reduce the amount of data and the timing for the analysis. In the last few years, many computer assisted diagno- sis systems have been developed, attempting to mimic pathologists by extracting features from the images. Image analysis involves complex algorithms to identify and characterize cells or tissues using image pattern recognition technology. This thesis addresses the main problems associated to the digital microscopy analysis in histology and haematology diagnosis, with the development of algorithms for the extraction of useful information from different digital images, but able to distinguish different biological structures in the images themselves. The proposed methods not only aim to improve the degree of accuracy of the analysis, and reducing time, if used as the only means of diagnoses, but also they can be used as intermediate tools for skimming the number of samples to be analysed directly from the pathologist, or as double check systems to verify the correct results of the automated facilities used today

Computer aided diagnosis algorithms for digital microscopy

Automatic analysis and information extraction from an image is still a highly chal- lenging research problem in the computer vision area, attempting to describe the image content with computational and mathematical techniques. Moreover the in- formation extracted from the image should be meaningful and as most discrimi- natory as possible, since it will be used to categorize its content according to the analysed problem. In the Medical Imaging domain this issue is even more felt because many important decisions that affect the patient care, depend on the use- fulness of the information extracted from the image. Manage medical image is even more complicated not only due to the importance of the problem, but also because it needs a fair amount of prior medical knowledge to be able to represent with data the visual information to which pathologist refer. Today medical decisions that impact patient care rely on the results of laboratory tests to a greater extent than ever before, due to the marked expansion in the number and complexity of offered tests. These developments promise to improve the care of patients, but the more increase the number and complexity of the tests, the more increases the possibility to misapply and misinterpret the test themselves, leading to inappropriate diagnosis and therapies. Moreover, with the increased number of tests also the amount of data to be analysed increases, forcing pathologists to devote much time to the analysis of the tests themselves rather than to patient care and the prescription of the right therapy, especially considering that most of the tests performed are just check up tests and most of the analysed samples come from healthy patients. Then, a quantitative evaluation of medical images is really essential to overcome uncertainty and subjectivity, but also to greatly reduce the amount of data and the timing for the analysis. In the last few years, many computer assisted diagno- sis systems have been developed, attempting to mimic pathologists by extracting features from the images. Image analysis involves complex algorithms to identify and characterize cells or tissues using image pattern recognition technology. This thesis addresses the main problems associated to the digital microscopy analysis in histology and haematology diagnosis, with the development of algorithms for the extraction of useful information from different digital images, but able to distinguish different biological structures in the images themselves. The proposed methods not only aim to improve the degree of accuracy of the analysis, and reducing time, if used as the only means of diagnoses, but also they can be used as intermediate tools for skimming the number of samples to be analysed directly from the pathologist, or as double check systems to verify the correct results of the automated facilities used today