RETRACTED: BCNet: A Novel Network for Blood Cell Classification

Abstract

Aims: Most blood diseases, such as chronic anemia, leukemia (commonly known as blood cancer), and hematopoietic dysfunction, are caused by environmental pollution, substandard decoration materials, radiation exposure, and long-term use certain drugs. Thus, it is imperative to classify the blood cell images. Most cell classification is based on the manual feature, machine learning classifier or the deep convolution network neural model. However, manual feature extraction is a very tedious process, and the results are usually unsatisfactory. On the other hand, the deep convolution neural network is usually composed of massive layers, and each layer has many parameters. Therefore, each deep convolution neural network needs a lot of time to get the results. Another problem is that medical data sets are relatively small, which may lead to overfitting problems.Methods: To address these problems, we propose seven models for the automatic classification of blood cells: BCARENet, BCR5RENet, BCMV2RENet, BCRRNet, BCRENet, BCRSNet, and BCNet. The BCNet model is the best model among the seven proposed models. The backbone model in our method is selected as the ResNet-18, which is pre-trained on the ImageNet set. To improve the performance of the proposed model, we replace the last four layers of the trained transferred ResNet-18 model with the three randomized neural networks (RNNs), which are RVFL, ELM, and SNN. The final outputs of our BCNet are generated by the ensemble of the predictions from the three randomized neural networks by the majority voting. We use four multi-classification indexes for the evaluation of our model.Results: The accuracy, average precision, average F1-score, and average recall are 96.78, 97.07, 96.78, and 96.77%, respectively.Conclusion: We offer the comparison of our model with state-of-the-art methods. The results of the proposed BCNet model are much better than other state-of-the-art methods.

Improved segmentation of overlapping red blood cells on malaria blood smear images with TransUNet architecture

“…Moreover, this improved segmentation of RBCs enables the construction of end-to-end malaria classification algorithms where the segmentation is followed by the classification, and more reliable segmentation enhances the quality of the classification. Therefore, the proposed algorithm can be jointly used with one of the classifiers in the literature [9][10][11][12]25,26 for end-to-end malaria classification. Alternatively, in the case of a low number of samples, feature extraction followed by Fuzzy-SVM might be employed for classification, which was reported to perform well by Chowdhary et al 27 On the other hand, even though the Mask-RCNN algorithm, which was proposed in the literature by Loh et al, 17 is capable of end-to-end classification, its accuracy is not comparable to current classifier algorithms, [9][10][11][12] even with the exclusion of labeling healthy RBCs.…”

BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification

“…César Cheuque et al proposed the MLCNN detection of white blood cell Faster RCNN used to extract Region of interest later with Mobilenet based model is used to train the classification framework gained performance accuracy of 98.4% [ 35 ]. In continuation Next BCNet [ 36 ] to address the blood cell classification for three classes via transfer learning approach with ResNet18 as backbone model for learning and noted 96.78% accuracy. A deep learning based AI framework artificial intelligence-based microscopy image classifier for blood cell classification is proposed with transfer learning methods and realized an accuracy of 98.6% [ 37 ].…”

Small hand-designed convolutional neural networks outperform transfer learning in automated cell shape detection in confluent tissues

“…Historically developed to treat everyday images and perform tasks such as object detection or classification, the use of these algorithms has now penetrated deep into biological research. Examples of application of CNNs to microscopy images are rapidly being added in contexts as different as crowded cell environments [ 28 ], stem cell fate determination [ 29 ], single cell classification [ 30 , 31 ], neuronal imaging [ 32 ] and of course cell segmentation [ 33 – 35 ]. Some examples are also emerging where image analysis by machine learning algorithms have medically relevant roles [ 36 – 38 ].…”