Semantic Segmentation of Anaemic RBCs Using Multilevel Deep Convolutional Encoder-Decoder Network
Pixel-level analysis of blood images plays a pivotal role in diagnosing blood-related diseases, especially Anaemia. These analyses mainly rely on an accurate diagnosis of morphological deformities like shape, size, and precise pixel counting. In traditional segmentation approaches, instance or objec...
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| Veröffentlicht in: | IEEE access Jg. 9; S. 161326 - 161341 |
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| Abstract | Pixel-level analysis of blood images plays a pivotal role in diagnosing blood-related diseases, especially Anaemia. These analyses mainly rely on an accurate diagnosis of morphological deformities like shape, size, and precise pixel counting. In traditional segmentation approaches, instance or object-based approaches have been adopted that are not feasible for pixel-level analysis. The convolutional neural network (CNN) model required a large dataset with detailed pixel-level information for the semantic segmentation of red blood cells in the deep learning domain. In current research work, we address these problems by proposing a multi-level deep convolutional encoder-decoder network along with two state-of-the-art healthy and Anaemic-RBC datasets. The proposed multi-level CNN model preserved pixel-level semantic information extracted in one layer and then passed to the next layer to choose relevant features. This phenomenon helps to precise pixel-level counting of healthy and anaemic-RBC elements along with morphological analysis. For experimental purposes, we proposed two state-of-the-art RBC datasets, i.e., Healthy-RBCs and Anaemic-RBCs dataset. Each dataset contains 1000 images, ground truth masks, relevant, complete blood count (CBC), and morphology reports for performance evaluation. The proposed model results were evaluated using crossmatch analysis with ground truth mask by finding IoU, individual training, validation, testing accuracies, and global accuracies using a 05-fold training procedure. This model got training, validation, and testing accuracies as 0.9856, 0.9760, and 0.9720 on the Healthy-RBC dataset and 0.9736, 0.9696, and 0.9591 on an Anaemic-RBC dataset. The IoU and BFScore of the proposed model were 0.9311, 0.9138, and 0.9032, 0.8978 on healthy and anaemic datasets, respectively. |
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| AbstractList | Pixel-level analysis of blood images plays a pivotal role in diagnosing blood-related diseases, especially Anaemia. These analyses mainly rely on an accurate diagnosis of morphological deformities like shape, size, and precise pixel counting. In traditional segmentation approaches, instance or object-based approaches have been adopted that are not feasible for pixel-level analysis. The convolutional neural network (CNN) model required a large dataset with detailed pixel-level information for the semantic segmentation of red blood cells in the deep learning domain. In current research work, we address these problems by proposing a multi-level deep convolutional encoder-decoder network along with two state-of-the-art healthy and Anaemic-RBC datasets. The proposed multi-level CNN model preserved pixel-level semantic information extracted in one layer and then passed to the next layer to choose relevant features. This phenomenon helps to precise pixel-level counting of healthy and anaemic-RBC elements along with morphological analysis. For experimental purposes, we proposed two state-of-the-art RBC datasets, i.e., Healthy-RBCs and Anaemic-RBCs dataset. Each dataset contains 1000 images, ground truth masks, relevant, complete blood count (CBC), and morphology reports for performance evaluation. The proposed model results were evaluated using crossmatch analysis with ground truth mask by finding IoU, individual training, validation, testing accuracies, and global accuracies using a 05-fold training procedure. This model got training, validation, and testing accuracies as 0.9856, 0.9760, and 0.9720 on the Healthy-RBC dataset and 0.9736, 0.9696, and 0.9591 on an Anaemic-RBC dataset. The IoU and BFScore of the proposed model were 0.9311, 0.9138, and 0.9032, 0.8978 on healthy and anaemic datasets, respectively. |
| Author | Shahzad, Muhammad Shaikh, Israr Ahmed Shirazi, Syed Hamad Umar, Arif Iqbal |
| Author_xml | – sequence: 1 givenname: Muhammad orcidid: 0000-0003-4971-4875 surname: Shahzad fullname: Shahzad, Muhammad organization: Department of Information Technology, Hazara University Mansehra, Dhodial, Pakistan – sequence: 2 givenname: Arif Iqbal orcidid: 0000-0001-9088-3422 surname: Umar fullname: Umar, Arif Iqbal email: drarif.hu@gmail.com organization: Department of Information Technology, Hazara University Mansehra, Dhodial, Pakistan – sequence: 3 givenname: Syed Hamad surname: Shirazi fullname: Shirazi, Syed Hamad organization: Department of Information Technology, Hazara University Mansehra, Dhodial, Pakistan – sequence: 4 givenname: Israr Ahmed surname: Shaikh fullname: Shaikh, Israr Ahmed organization: Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistan |
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| SubjectTerms | Anemia Artificial neural networks Blood blood cells Cells (biology) CNN Coders Computer architecture Convolutional neural networks Datasets deep learning Encoders-Decoders Erythrocytes Image segmentation Medical imaging Morphology multi-level deep convolutional encoder-decoder network Performance evaluation Pixels segmentation analysis Semantic segmentation Semantics Shape Training |
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| Title | Semantic Segmentation of Anaemic RBCs Using Multilevel Deep Convolutional Encoder-Decoder Network |
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