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A Hybrid Approach for Pap-Smear Cell Nucleus Extraction [chapter]

M. Orozco-Monteagudo, Hichem Sahli, Cosmin Mihai, A. Taboada-Crispi
2011 Lecture Notes in Computer Science  
The second phase, selects the best hierarchical level based on an unsupervised criterion, and refines the obtained segmentation by classifying the individual regions using a Support Vector Machine (SVM  ...  The first phase uses spectral, shape as well as the class membership to produce a nested hierarchical partition (hierarchy of segmentations).  ...  Segmentation of [10] (GEE), and a Pixel-based SVM classification (SVMP) [14] .  ... 
doi:10.1007/978-3-642-21587-2_19 fatcat:4kamiaf3s5borjwcgvcyz5if3u

Differential diagnosis of hereditary anemias from a fraction of blood drop by digital holography and hierarchical machine learning

Pasquale Memmolo
2022 Zenodo  
Here we show a differential screening system for hereditary anemias that relies on holographic imaging and artificial intelligence.  ...  Label-free holographic imaging is aided by a hierarchical machine learning decider that works even in the presence of a very limited dataset but is enough accurate for discerning between different anemia  ...  result with respect to the single-cell based classification approaches.  ... 
doi:10.5281/zenodo.6106015 fatcat:3gkqjtttgzhwpkgyasm5q7aylm

A Survey on Various Classification Techniques for Medical Image Data

Niranjan J.Chatap, Ashish Kr. Shrivastava
2014 International Journal of Computer Applications  
If we use one of the new classifier i.e. nearest neighbor and SVM it is quiet possible to detect the cancer cell from the blood cell counting.  ...  One of the best methods for classification techniques artificial neural network and SVM (Support Vector Machine).  ...  In their work blood cell image classification based on Hierarchical SVM were used.  ... 
doi:10.5120/17080-7528 fatcat:pc5n7awlknbwpddaf6val5pssy

Real-time stain-free classification of cancer cells and blood cells using interferometric phase microscopy and machine learning

Noga Nissim, Matan Dudaie, Itay Barnea, Natan T Shaked
2020 Cytometry Part A  
As a preliminary model for circulating tumor cells in the blood, following a preliminary label-free rapid enrichment stage based on the cell size, we applied our holographic imaging approach, providing  ...  The acquired images were processed and classified based on their morphology and quantitative phase features during the cell flow.  ...  For cell classification, we have employed machinelearning techniques that included PCA-based cell type representation followed by an SVM-based classification.  ... 
doi:10.1002/cyto.a.24227 pmid:32910546 fatcat:amogi7vl7nakpkff3zqndxun4e

Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia

Sarmad Shafique, Samabia Tehsin
2018 Computational and Mathematical Methods in Medicine  
Leukaemia is a form of blood cancer which affects the white blood cells and damages the bone marrow.  ...  This paper presents review of computer-aided diagnosis systems regarding their methodologies that include enhancement, segmentation, feature extraction, classification, and accuracy.  ...  SVM (support vector machine) Accuracy 93.57% MoradiAmin et al., 2016 Computer aided detection and classification of acute lymphoblastic leukaemia cell subtypes based on microscopic image analysis  ... 
doi:10.1155/2018/6125289 pmid:29681996 pmcid:PMC5851334 fatcat:n3w56rlfhncbtnshzbykotjnza

Biosensors and Machine Learning for Enhanced Detection, Stratification, and Classification of Cells: A Review [article]

Hassan Raji, Muhammad Tayyab, Jianye Sui, Seyed Reza Mahmoodi, Mehdi Javanmard
2021 arXiv   pre-print
Understanding how they function and differentiating cells from one another therefore is of paramount importance for disease diagnostics as well as therapeutics.  ...  Furthermore, Machine Learning has allowed for enhancement in analytical capabilities of these various biosensing modalities, especially the challenging task of classification of cells into various categories  ...  Subsequently, the cells in a single dye image were identified by the first SVM based on HOG features found in the image using a sliding window method.  ... 
arXiv:2101.01866v1 fatcat:rws7k3yp6ndmnlkqcvafmkgphi

Feature Extraction using CNN for Peripheral Blood Cells Recognition

Mohammed Ammar, Mostafa Daho, Khaled Harrar, Amel Laidi
2021 EAI Endorsed Transactions on Scalable Information Systems  
OBJECTIVES: In this work, a hybrid model based on CNN features extraction and machine learning classifiers were proposed to improve peripheral blood cell image classification.  ...  INTRODUCTION: The diagnosis of hematological diseases is based on the morphological differentiation of the peripheral blood cell types.  ...  Related works Many works have been proposed for white blood cells' classification based on deep learning methods.  ... 
doi:10.4108/eai.20-10-2021.171548 fatcat:jcl2m7kpprdk7f3iqpp6j3cb3q

Quantitative-Morphological and Cytological Analyses in Leukemia [chapter]

Cecília Lantos, Steven M. Kornblau, Amina A. Qutub
2018 Hematology - Latest Research and Clinical Advances  
Leukemia is classified into major types based on the rate of cancerous cell growth and cell lineage: chronic or acute and myeloid or lymphoid leukemia.  ...  Furthermore, digital pathology methods integrated with advances in machine learning enable new diagnostic features from leukemia patients' histological and cytological slides and optimize patient classification  ...  Leukemia is detected based on the number, type, and proportion of various cell types in the blood.  ... 
doi:10.5772/intechopen.73675 fatcat:lzzrx5nwijb5nno4lky6pftz44

Diabetic Retinopathy Screening using Machine Learning for Hierarchical Classification

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Later, feature based hierarchical classification is performed for detection of different stages of the disease.  ...  This method is based on the same logical steps as followed by the ophthalmologists and hence assures more accurate classification results.  ...  The classification is done based on values at the output layer. The outputs obtained for various stages are shown in Fig 2 . IV.  ... 
doi:10.35940/ijitee.j9277.0881019 fatcat:a7zec2ykrjemrcfro626rcgyuq

Supervised Machine Learning Based Multi-Task Artificial Intelligence Classification of Retinopathies

Alam, Le, Lim, Chan, Yao
2019 Journal of Clinical Medicine  
In this study, we demonstrate supervised machine learning based multi-task OCTA classification.  ...  Quantitative OCTA features, including blood vessel tortuosity (BVT), blood vascular caliber (BVC), vessel perimeter index (VPI), blood vessel density (BVD), foveal avascular zone (FAZ) area (FAZ-A), and  ...  Multi-Task Classification The SVM classifier performed the classification tasks in a hierarchical manner.  ... 
doi:10.3390/jcm8060872 pmid:31216768 pmcid:PMC6617139 fatcat:xeuqksn3sna63f3m2d3wzrhegi

Automated Acute Lymphoblastic Leukemia Detection System using Microscopic Images

Abdul Ghafoor, Muhammad Riaz, Komal Nain Sukhia
2019 IET Image Processing  
The proposed scheme is based on pre-processing and segmentation of white blood cell nuclei using expectation maximisation algorithm, feature extraction, feature selection using principal component analysis  ...  and classification using sparse representation.  ...  Automated hybrid hierarchical classification method [17] helps to differentiate cancerous cells from the non-cancerous ones and further distinguish between the various subtypes of ALL.  ... 
doi:10.1049/iet-ipr.2018.5471 fatcat:5nuuw5dusfhjjnohpdakfago2a

Classification of inflammatory bowel diseases by means of Raman spectroscopic imaging of epithelium cells

Christiane Bielecki, Thomas W. Bocklitz, Michael Schmitt, Christoph Krafft, Claudio Marquardt, Akram Gharbi, Thomas Knösel, Andreas Stallmach, Juergen Popp
2012 Journal of Biomedical Optics  
We report on a Raman microspectroscopic characterization of the inflammatory bowel diseases (IBD) Crohn's disease (CD) and ulcerative colitis (UC).  ...  The automatic design of both classification steps (visualization of the tissue morphology and molecular classification of IBD) paves the way for an objective clinical diagnosis of IBD by means of Raman  ...  Here the radial-base kernel is utilized and the SVMs for multiclass tasks are combined by the one-against-one scheme.  ... 
doi:10.1117/1.jbo.17.7.076030 pmid:22894513 fatcat:x2uxw3ar7zdqlix6gmr7vlhohq

Supervised machine learning based multi-task artificial intelligence classification of retinopathies [article]

Minhaj Alam, David Le, Jennifer I. Lim, R.V.P. Chan, Xincheng Yao
2019 arXiv   pre-print
In this study, we demonstrate supervised machine learning based multi-task OCTA classification.  ...  Quantitative OCTA features, including blood vessel tortuosity (BVT), blood vascular caliber (BVC), vessel perimeter index (VPI), blood vessel density (BVD), foveal avascular zone (FAZ) area (FAZ-A), and  ...  Multi-task classification The SVM classifier performs the classification tasks in a hierarchical manner.  ... 
arXiv:1905.04224v1 fatcat:bvbp6dvefncdnpvbrai7vz553m

Classification of White Blood Cells using Bispectral Invariant Features of Nuclei Shape

Khamael Al-Dulaimi, Vinod Chandran, Jasmine Banks, Inmaculada Tomeo-Reyes, Kien Nguyen
2018 2018 Digital Image Computing: Techniques and Applications (DICTA)  
ACKNOWLEDGEMENT The authors are thankful to Department of Information Technology -Università degli Studi di Milano, Wadsworth Center and cellavision databases who provided us with WBC images.  ...  In [11] , supervised classification is used for distinguishing between WBCs types based on hierarchical topological feature extraction using inception and ResNet architectures and a consecutive deep learning  ...  Fig. 1 : 1 Steps of automated classification of white blood cells.  ... 
doi:10.1109/dicta.2018.8615762 dblp:conf/dicta/Al-DulaimiCBTN18 fatcat:2nsonrhxnndx7e4e5lmquzyrnq

Alzheimer disease stages identification based on correlation transfer function system using resting-state functional magnetic resonance imaging

Doaa Mousa, Nourhan Zayed, Inas A. Yassine, Stephen D. Ginsberg
2022 PLoS ONE  
The proposed framework employed the SVM classifier in two different methodologies, hierarchical and flat multi-classification schemes, to differentiate between the different AD stages for early detection  ...  Additionally, we explored the regions, showing significant changes based on the CorrTF extracted features' strength among different AD stages.  ...  The proposed hierarchical multi-classification scheme was selected based on the performance of the binary SVM classifier for each pair of classes, as reported in Table 3 .  ... 
doi:10.1371/journal.pone.0264710 pmid:35413053 pmcid:PMC9004771 fatcat:2myrhvsjzbh3jer23xohrnuyvq
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