Proceedings ArticleDOI
A semiautomated approach using GUI for the detection of red blood cells
Suvojit Acharjee,Shubhro Chakrabartty,Mohd. Iqbal Alam,Nilanjan Dey,V. Santhi,Amira S. Ashour +5 more
- pp 525-529
TLDR
A new approach is presented for semi-automatically count of the RBCs that outperformed the automated system in terms of the executing time and is supported by a GUI to facilitate the pathologist interaction with the proposed system.Abstract:
Counting of Red Blood cell (RBC) is a significant measure that helps to diagnose specific diseases. Manual pathological RBCs counting process by experienced specialist is extremely tedious, time consuming, and imprecise which may be prone to high chance of error. Due to recent advancement, automated detection of red blood cell using image processing techniques is gaining popularity. In order to increase the accuracy of the results, it is preferred to accommodate experienced specialist in the RBC counting. In this paper, a new approach is presented for semi-automatically count of the RBCs. The user can specify the dimension of RBC by dragging two points over the image and then apply the Hough transform to detect the oval and biconcave shape of RBC with the specified diameter. The proposed semi-automatic system outperformed the automated system in terms of the executing time. In addition, the proposed system is supported by a GUI to facilitate the pathologist interaction with the proposed system.read more
Citations
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Journal ArticleDOI
Machine learning approach of automatic identification and counting of blood cells
TL;DR: The authors present a machine learning approach for automatic identification and counting of three types of blood cells using ‘you only look once’ (YOLO) object detection and classification algorithm and found that the learned models are generalised.
Journal ArticleDOI
Enhanced Directed Differential Evolution Algorithm for Solving Constrained Engineering Optimization Problems
TL;DR: An enhanced DE algorithm (EDDE) that utilizes the information given by good individuals and bad individuals in the population that maintains effectively the exploration/exploitation balance is introduced.
Journal ArticleDOI
An Automated Method for Counting Red Blood Cells using Image Processing
TL;DR: Technique has been introduced to count the RBCs automatically and images are classified on the basis of color, texture and morphology, namely elliptocytes, echinocytes, tear drop cells and macrocytes, which achieves overall accuracy of 91.667% and is computationally very efficient.
Journal ArticleDOI
Classification of β -Thalassemia Carriers From Red Blood Cell Indices Using Ensemble Classifier
Saima Sadiq,Muhammad Usman Khalid,Mui-Zzud-Din,Saleem Ullah,Waqar Aslam,Arif Mehmood,Gyu Sang Choi,Byung-Won On +7 more
TL;DR: In this article, an ensemble of three machine learning algorithms (Support Vector Machine, Gradient Boosting Machine, and Random Forest) was used to detect β-Thalassemia carriers.
Journal ArticleDOI
Complete Blood Cell Detection and Counting Based on Deep Neural Networks
TL;DR: A deep neural network-based architecture to accurately detect and count blood cells on blood smear images and shows that the models can recognize blood cells accurately when blood cells are not heavily overlapping.
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Proceedings ArticleDOI
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