https://iris.unica.it/bitstream/11584/94592/2/AIM2014_open.pdf

Leucocyte classification for leukaemia detection using image processing techniques

Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification

Leukemia is a fatal disease of white blood cells which affects the blood and bone marrow in human body. We deployed deep convolutional neural network for automated detection of acute lymphoblastic leukemia and classification of its subtypes into 4 classes, that is, L1, L2, L3, and Normal which were mostly neglected in previous literature. In contrary to the training from scratch, we deployed pretrained AlexNet which was fine-tuned on our data set. Last layers of the pretrained network were replaced with new layers which can classify the input images into 4 classes. To reduce overtraining, data augmentation technique was used. We also compared the data sets with different color models to check the performance over different color images. For acute lymphoblastic leukemia detection, we achieved a sensitivity of 100%, specificity of 98.11%, and accuracy of 99.50%; and for acute lymphoblastic leukemia subtype classification the sensitivity was 96.74%, specificity was 99.03%, and accuracy was 96.06%. Unlike the standard methods, our proposed method was able to achieve high accuracy without any need of microscopic image segmentation.

https://journals.sagepub.com/doi/pdf/10.1177/1533033818802789

Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks.

Acute Leukemia is a life-threatening disease common both in children and adults that can lead to death if left untreated. Acute Lymphoblastic Leukemia (ALL) spreads out in children's bodies rapidly and takes the life within a few weeks. To diagnose ALL, the hematologists perform blood and bone marrow examination. Manual blood testing techniques that have been used since long time are often slow and come out with the less accurate diagnosis. This work improves the diagnosis of ALL with a computer-aided system, which yields accurate result by using image processing and deep learning techniques. This research proposed a method for the classification of ALL into its subtypes and reactive bone marrow (normal) in stained bone marrow images. A robust segmentation and deep learning techniques with the convolutional neural network are used to train the model on the bone marrow images to achieve accurate classification results. Experimental results thus obtained and compared with the results of other classifiers Naive Bayesian, KNN, and SVM. Experimental results reveal that the proposed method achieved 97.78% accuracy. The obtained results exhibit that the proposed approach could be used as a tool to diagnose Acute Lymphoblastic Leukemia and its sub-types that will definitely assist pathologists.

/pdf/classification-of-acute-lymphoblastic-leukemia-using-deep-d8899nt5dh.pdf

Classification of acute lymphoblastic leukemia using deep learning

The detection and classification of white blood cells (WBCs, also known as Leukocytes) is a hot issue because of its important applications in disease diagnosis. Nowadays the morphological analysis of blood cells is operated manually by skilled operators, which results in some drawbacks such as slowness of the analysis, a non-standard accuracy, and the dependence on the operator’s skills. Although there have been many papers studying the detection of WBCs or classification of WBCs independently, few papers consider them together. This paper proposes an automatic detection and classification system for WBCs from peripheral blood images. It firstly proposes an algorithm to detect WBCs from the microscope images based on the simple relation of colors R, B and morphological operation. Then a granularity feature (pairwise rotation invariant co-occurrence local binary pattern, PRICoLBP feature) and SVM are applied to classify eosinophil and basophil from other WBCs firstly. Lastly, convolution neural networks are used to extract features in high level from WBCs automatically, and a random forest is applied to these features to recognize the other three kinds of WBCs: neutrophil, monocyte and lymphocyte. Some detection experiments on Cellavison database and ALL-IDB database show that our proposed detection method has better effect almost than iterative threshold method with less cost time, and some classification experiments show that our proposed classification method has better accuracy almost than some other methods.

Automatic detection and classification of leukocytes using convolutional neural networks

Leukemia is a malignant neoplasm of the blood or bone marrow that affects both children and adults and remains a leading cause of death around the world. Acute lymphoblastic leukemia (ALL) is the most common type of leukemia and is more common among children and young adults. ALL diagnosis through microscopic examination of the peripheral blood and bone marrow tissue samples is performed by hematologists and has been an indispensable technique long since. However, such visual examinations of blood samples are often slow and are also limited by subjective interpretations and less accurate diagnosis. The objective of this work is to improve the ALL diagnostic accuracy by analyzing morphological and textural features from the blood image using image processing. This paper aims at proposing a quantitative microscopic approach toward the discrimination of lymphoblasts (malignant) from lymphocytes (normal) in stained blood smear and bone marrow samples and to assist in the development of a computer-aided screening of ALL. Automated recognition of lymphoblasts is accomplished using image segmentation, feature extraction, and classification over light microscopic images of stained blood films. Accurate and authentic diagnosis of ALL is obtained with the use of improved segmentation methodology, prominent features, and an ensemble classifier, facilitating rapid screening of patients. Experimental results are obtained and compared over the available image data set. It is observed that an ensemble of classifiers leads to 99 % accuracy in comparison with other standard classifiers, i.e., naive Bayesian (NB), K-nearest neighbor (KNN), multilayer perceptron (MLP), radial basis functional network (RBFN), and support vector machines (SVM).

An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images

Pathological image analysis plays a significant role in effective disease diagnostics. Quantitative microscopy has supplemented clinicians with accurate results for diagnosis of dreaded diseases such as leukemia, hepatitis, AIDS, psoriasis. In this paper we present a texture based approach for automated leukemia detection. Acute lymphocytic leukemia (ALL) is a malignant disease characterized by the accumulation of lymphoblast in the bone marrow. Texture features of the blood nucleus are investigated for diagnostic prediction of ALL. Other shape features are also extracted to classify a lymphocytic cell in the blood image into normal lymphocyte or lymphoblast (blasts). Initial segmentation is done using K-means clustering which segregates leukocytes or white blood cells (WBC) from other blood components i.e. erythrocytes and platelets. The results of K-means are used for evaluating individual cell shape, texture and other features for final detection of leukemia. A total of 108 blood smear images were considered for feature extraction and final performance evaluation is validated with the results of a hematologist.

Automated leukemia detection in blood microscopic images using statistical texture analysis

Microscopic examination of stained blood slides is an indispensable technique for hematological disease recognition. Diagnosis based on human visual interpretation is often subjected to inter and intra observer variations, slowness, tiredness and operator experience. Accurate and authentic diagnosis of hematological neoplasia is essential in the planning of suitable surgery and chemotherapy. This paper aims at proposing a fast and simple framework for lymphocyte image segmentation. Accurate segmentation of lymphocyte is essential as it facilitates automated leukemia detection in blood microscopic images. In this paper image segmentation is considered as a pixel classification problem and a novel neural architecture is employed to classify each pixel into cytoplasm, nucleus or background region. The network tuned for a set of images works well for other similar stained blood images. Comparative analysis with other standard techniques reveals that the proposed scheme outperforms its counterparts in terms of nucleus and cytoplasm extraction. In this work, a neural network based lymphocyte image segmentation scheme is designed for automated leukemia detection. Desired segmentation accuracy in terms of nucleus and cytoplasm extraction is always high in automated disease recognition system and is achieved through the proposed scheme.

Lymphocyte image segmentation using functional link neural architecture for acute leukemia detection

Leukaemias are neoplastic proliferations of haemopoietic cells which affect both children and adults and remain one of the leading causes of death around the world. Early diagnosis and classification of such malignant disorders are necessary, and it has always been a challenge in the field of haematology and laboratory medicine. Accurate and authentic diagnosis of leukaemia is essential for the confirmation of the disease, prognostic classification and effective treatment planning. Visual microscopic examination of blood slides is considered as an indispensable diagnostic technique for the screening and classification of leukaemia across continents. However, manual investigation is often slow and limited by subjective assimilation and reduced diagnostic precision. In this study, we investigate the use of image morphometry and pattern recognition techniques for subtyping leukaemic lymphoblasts as per French–American–British classification. Reliable classification results were obtained using the robust segm...

Sanghamitra Satpathy

Papers

An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images

Automated leukemia detection in blood microscopic images using statistical texture analysis

Lymphocyte image segmentation using functional link neural architecture for acute leukemia detection

Automated morphometric classification of acute lymphoblastic leukaemia in blood microscopic images using an ensemble of classifiers