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.

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.

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.

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.

scholarly journals Acute Lymphoblastic Leukemia Presenting as Fanconi Syndrome

2018 ◽  
Vol 11 (1) ◽  
pp. 63-67
Author(s):  
Tatsunori Yoshida ◽  
Hiroshi Tsujimoto ◽  
Takayuki Ichikawa ◽  
Shinji Kounami ◽  
Hiroyuki Suzuki
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Renal Tubular Acidosis ◽  
Fanconi Syndrome ◽  
Blood Smear ◽  
Lymphoblastic Leukemia ◽  
Peripheral Blood Smear ◽  
Leukemic Cells ◽  
Laboratory Examinations ◽  
Renal Tubular ◽  
Electrolyte Abnormalities

Acute lymphoblastic leukemia (ALL) presenting as Fanconi syndrome (FS) is extremely rare. Here, we report a case of ALL presenting as bilateral nephromegaly following FS. A 2-year-old girl was unexpectedly diagnosed with bilateral nephromegaly. After 2 weeks, she developed general fatigue, thirst, and polyuria. Laboratory examinations revealed renal tubular acidosis, hypokalemia, hypophosphatemia, and aminoaciduria, and FS was diagnosed. Replacement of bicarbonate and potassium did not improve her condition. Two weeks after the onset of FS, leukemic cells appeared on a peripheral blood smear, and the patient was diagnosed with precursor B-cell ALL presenting as nephromegaly and FS. Chemotherapy brought about a prompt resolution of acidosis and electrolyte abnormalities, without renal dysfunction. The patient remains well 4 years after the onset of the disease. Although extremely rare, FS should be recognized as one of the emerging renal complications of ALL.

scholarly journals Staging Melanocytic Skin Neoplasms Using High-Level Pixel-Based Features

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1443
Author(s):  
Mai Ramadan Ibraheem ◽  
Shaker El-Sappagh ◽  
Tamer Abuhmed ◽  
Mohammed Elmogy
Keyword(s):  
Area Under The Curve ◽  
Malignant Neoplasm ◽  
Texture Features ◽  
Feature Space ◽  
Skin Neoplasms ◽  
Support Vector ◽  
Svm Classifier ◽  
Abnormal Growth ◽  
Malignant Skin ◽  
High Level

The formation of malignant neoplasm can be seen as deterioration of a pre-malignant skin neoplasm in its functionality and structure. Distinguishing melanocytic skin neoplasms is a challenging task due to their high visual similarity with different types of lesions and the intra-structural variants of melanocytic neoplasms. Besides, there is a high visual likeliness level between different lesion types with inhomogeneous features and fuzzy boundaries. The abnormal growth of melanocytic neoplasms takes various forms from uniform typical pigment network to irregular atypical shape, which can be described by border irregularity of melanocyte lesion image. This work proposes analytical reasoning for the human-observable phenomenon as a high-level feature to determine the neoplasm growth phase using a novel pixel-based feature space. The pixel-based feature space, which is comprised of high-level features and other color and texture features, are fed into the classifier to classify different melanocyte neoplasm phases. The proposed system was evaluated on the PH2 dermoscopic images benchmark dataset. It achieved an average accuracy of 95.1% using a support vector machine (SVM) classifier with the radial basis function (RBF) kernel. Furthermore, it reached an average Disc similarity coefficient (DSC) of 95.1%, an area under the curve (AUC) of 96.9%, and a sensitivity of 99%. The results of the proposed system outperform the results of other state-of-the-art multiclass techniques.

scholarly journals PENGARUH KEMOTERAPI TERHADAP PROFIL HEMATOLOGI PADA PENDERITA LEUKEMIA LIMFOBLASTIK AKUT

e-CliniC ◽  
2013 ◽  
Vol 1 (2) ◽  
Author(s):  
Eunike Pinontoan ◽  
Max Mantik ◽  
Novie Rampengan
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Retrospective Cohort ◽  
Lymphoblastic Leukemia ◽  
Normal Cells ◽  
Pediatric Department ◽  
Abnormal Growth ◽  
Study Results ◽  
Definition Of ◽  
The Right ◽  
Leukemia In Children

Abstract Leukemia atau lebih dikenal kanker pada darah atau sumsum tulang merupakan pertumbuhan sel-sel abnormal tidak terkontrol (sel neoplasma) yang berasal dari hasil mutasi sel normal Kejadian leukemia setiap tahun sekitar 3,5 kasus dari 100.000 anak dibawah 15 tahun. Leukemia pada anak terdiri dari dua tipe yaitu : Leukemia Limfoblastik Akut (LLA) 82% dan Leukemia Mieloblastik Akut (LMA) 18%.  Puncak kejadian LLA pada usia 2-5 ta       hun. Perbandingan penderita perempuan dan laki-laki ialah 1,3:1,5. Data rekam medik BLU RSUP Prof.dr.R.D. Kandou sepanjang tahun 2008-2012, jumlah penderita leukemia limfoblastik akut (LLA) ada sekitar 60 anak  yang rawat inap di bagian IKA Prof.Dr.R.D.Kandou Manado. Tujuan: Penelitian ini bertujuan untuk menganalisis pengaruh terapi medis (kemoterapi) terhadap profil hematologi pada penderita Leukemia Limfoblastik Akut (LLA) yang rawat inap di bagian IKA-BLU RSUP Prof.dr.R.D.Kandou. Metode Jenis penelitian  merupakan penelitian analitik dengan menggunakan desain kohort retrospektif. Hasil: Hasil penelitian menunjukkan bahwa profil hematologi penderita LLA yang dirawat di Bagian IKA RS Prof. Dr. R.D Kandou Manado mengalami perubahan setiap minggu.Kata kunci: Leukemia limfoblastik akut, profil hematologi.    Abstract Leukemia is commonly known as blood or the bone marrow cancer. The definition of leukemia is an abnormal growth of cells (neoplasm cells) that derived from the mutation of normal cells. The incidence of the leukemia is about 3.5 cases in 100.000 children under 15 years per year. Leukemia in children is divided into two types which are: Acute Lymphoblastic Leukemia (ALL) 82% and Acute Myeloblastic Leukemia (AML) 18%. The age two to five years is the right usually age of the incidence of LLA.  The ratio between girls and boys is 1.3:1.5. The medical record in BLU RSUP Prof. Dr. R.D. Kandou showed that between 2008 and 2012 the total inpatient children with LLA in the Pediatric Department of   RSUP Prof. Dr. R.D. Kandou is 60 children. Goal: The goal of this research was to analyze the effect of medical therapy (chemotherapy) on the hematology profile in the LLA patients who were inpatient in the Pediatric Department of RSUP Prof. Dr. R.D. Kandou. Methods: This was a analytic research with the design of retrospective cohort study. Results: This research shows that the hematology profile of LLA patients who were inpatient in the Pediatric Department of RSUP Prof. Dr. R.D. Kandou was weekly changed. Key words: Acute Lymphoblastic Leukemia, hematology profile

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