TAYLOR–MONARCH BUTTERFLY OPTIMIZATION-BASED SUPPORT VECTOR MACHINE FOR ACUTE LYMPHOBLASTIC LEUKEMIA CLASSIFICATION WITH BLOOD SMEAR MICROSCOPIC IMAGES

2021 ◽  
pp. 2150041
Author(s):  
G. MERCY BAI ◽  
P. VENKADESH
Keyword(s):  
Support Vector Machine ◽  
Acute Lymphoblastic Leukemia ◽  
Blood Smear ◽  
Lymphoblastic Leukemia ◽  
Automatic Segmentation ◽  
Texture Features ◽  
Monarch Butterfly ◽  
Support Vector ◽  
Training Time ◽  
Monarch Butterfly Optimization

Acute lymphoblastic leukemia (ALL) is a serious hematological neoplasis that is characterized by the development of immature and abnormal growth of lymphoblasts. However, microscopic examination of bone marrow is the only way to achieve leukemia detection. Various methods are developed for automatic leukemia detection, but these methods are costly and time-consuming. Hence, an effective leukemia detection approach is designed using the proposed Taylor–monarch butterfly optimization-based support vector machine (Taylor–MBO-based SVM). However, the proposed Taylor–MBO is designed by integrating the Taylor series and MBO, respectively. The sparking process is designed to perform the automatic segmentation of blood smear images by estimating optimal threshold values. By extracting the features, such as texture features, statistical, and grid-based features from the segmented smear image, the performance of classification is increased with less training time. The kernel function of SVM is enabled to perform the leukemia classification such that the proposed Taylor–MBO algorithm accomplishes the training process of SVM. However, the proposed Taylor–MBO-based SVM obtained better performance using the metrics, such as accuracy, sensitivity, and specificity, with 94.5751, 95.526, and 94.570%, respectively.

scholarly journals Acute Lymphoblastic Leukemia Classification with Blood Smear Microscopic Images Using Taylor-MBO based SVM

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 02) ◽  
pp. 357-366
Author(s):  
Nashwan Jasim Hussein
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Blood Smear ◽  
Lymphoblastic Leukemia ◽  
Automatic Segmentation ◽  
Texture Features ◽  
Monarch Butterfly ◽  
Support Vector ◽  
Training Time ◽  
Monarch Butterfly Optimization ◽  
Abnormal Growth

Acute lymphoblastic leukemia (ALL) is a serious hematological neoplasis that is characterized by the development of immature and abnormal growth of lymphoblasts. However, microscopic examination of bone marrow is the only way to achieve leukemia detection. Hence, an effective leukemia detection approach is designed using the proposed Taylor-Monarch Butterfly Optimization based Support Vector Machine (Taylor-MBO based SVM). However, the proposed Taylor-MBO is designed by the integration of Taylor series and Monarch Butterfly Optimization (MBO), respectively. The sparking process is designed to perform automatic segmentation of blood smear image by estimating optimal threshold values. By extracting the features, such as texture features, statistical and grid-based features from the segmented smear image, the performance of classification is increased with less training time However, the proposed Taylor-MBO based SVM obtained better performance using the metrics, such as accuracy, sensitivity, and specificity with the values of 94.5751%, 95.526%, and 94.570%, respectively.

Selected Shape and Texture Features for Automatic Detection of Acute Lymphoblastic Leukemia

2018 ◽  
pp. 162-183
Author(s):  
Vanika Singhal ◽  
Preety Singh
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Lymphoblastic Leukemia ◽  
Principal Component ◽  
Texture Features ◽  
Automatic Detection ◽  
Support Vector ◽  
Shape Features ◽  
Geometrical Properties ◽  
Correlation Based Feature Selection ◽  
Human Interpretation

Acute Lymphoblastic Leukemia is a cancer of blood caused due to increase in number of immature lymphocyte cells. Detection is done manually by skilled pathologists which is time consuming and depends on the skills of the pathologist. The authors propose a methodology for discrimination of a normal lymphocyte cell from a malignant one by processing the blood sample image. Automatic detection process will reduce the diagnosis time and not be limited by human interpretation. The lymphocyte images are classified based on two types of extracted features: shape and texture. To identify prominent shape features, Correlation based Feature Selection is applied. Principal Component Analysis is applied on the texture features to reduce their dimensionality. Support Vector Machine is used for classification. It is observed that 16 shape features are able to give a classification accuracy of 92.3% and that changes in the geometrical properties of the nucleus emerge as significant features contributing towards detecting a malignant lymphocyte.

Thermography Based Breast Cancer Detection Using Texture Features and Support Vector Machine

2010 ◽  
Vol 36 (3) ◽  
pp. 1503-1510 ◽  
Author(s):  
U. Rajendra Acharya ◽  
E. Y. K. Ng ◽  
Jen-Hong Tan ◽  
S. Vinitha Sree
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Cancer Detection ◽  
Texture Features ◽  
Breast Cancer Detection ◽  
Support Vector

A CLINICOPATHOLOGICAL STUDY OF ACUTE AND CHRONIC LEUKEMIA IN A TERTIARY CARE HOSPITAL

2021 ◽  
pp. 72-74
Author(s):  
Sarat Das ◽  
Prasanta Kr. Baruah ◽  
Sandeep Khakhlari ◽  
Gautam Boro
Keyword(s):  
Acute Myeloid Leukemia ◽  
Acute Lymphoblastic Leukemia ◽  
Peripheral Blood ◽  
Myeloid Leukemia ◽  
Blood Smear ◽  
Lymphoblastic Leukemia ◽  
Peripheral Blood Smear ◽  
Care Hospital ◽  
Cytochemical Study ◽  
Acute Myeloid

Introduction: Leukemias are neoplastic proliferations of haematopoietic stem cells and form a major proportion of haematopoietic neoplasms that are diagnosed worldwide. Typing of leukemia is essential for effective therapy because prognosis and survival rate are different for each type and sub-type Aims: this study was carried out to determine the frequency of acute and chronic leukemias and to evaluate their clinicopathological features. Methods: It was a hospital based cross sectional study of 60 patients carried out in the department of Pathology, JMCH, Assam over a period of one year between February 2018 and January 2019. Diagnosis was based on peripheral blood count, peripheral blood smear and bone marrow examination (as on when available marrow sample) for morphology along with cytochemical study whenever possible. Results: In the present study, commonest leukemia was Acute myeloid leukemia (AML, 50%) followed by Acute lymphoblastic leukemia (ALL 26.6%), chronic myeloid leukemia (CML, 16.7%) and chronic lymphocytic leukemia (CLL, 6.7%). Out of total 60 cases, 36 were male and 24 were female with Male:Female ratio of 1.5:1. Acute lymphoblastic leukemia was the most common type of leukemia in the children and adolescents. Acute Myeloid leukemia was more prevalent in adults. Peripheral blood smear and bone Conclusion: marrow aspiration study still remains the important tool along with cytochemistry, immunophenotyping and cytogenetic study in the diagnosis and management of leukemia.

Search on Method of Netwok Intrusion Detection Based on Support Vector Machine with Pre-Extracting Support Vector

2013 ◽  
Vol 655-657 ◽  
pp. 1787-1790
Author(s):  
Sheng Chen Yu ◽  
Li Min Sun ◽  
Yang Xue ◽  
Hui Guo ◽  
Xiao Ju Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Algorithm ◽  
Support Vector ◽  
Distance Ratio ◽  
Training Time ◽  
Training Examples ◽  
Center Distance

Intrusion detection algorithm based on support vector machine with pre-extracting support vector is proposed which combines the center distance ratio and classification algorithm. Given proper thresholds, we can use the support vector as a substitute for the training examples. Then the scale of dataset is decreased and the performance of support vector machine is improved in the detection rate and the training time. The experiment result has shown that the intrusion detection system(IDS) based on support vector machine with pre-extracting support needs less training time under the same detection performance condition.

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