DSNN: A DenseNet-Based SNN for Explainable Brain Disease Classification

Aims: Brain diseases refer to intracranial tissue and organ inflammation, vascular diseases, tumors, degeneration, malformations, genetic diseases, immune diseases, nutritional and metabolic diseases, poisoning, trauma, parasitic diseases, etc. Taking Alzheimer's disease (AD) as an example, the number of patients dramatically increases in developed countries. By 2025, the number of elderly patients with AD aged 65 and over will reach 7.1 million, an increase of nearly 29% over the 5.5 million patients of the same age in 2018. Unless medical breakthroughs are made, AD patients may increase from 5.5 million to 13.8 million by 2050, almost three times the original. Researchers have focused on developing complex machine learning (ML) algorithms, i.e., convolutional neural networks (CNNs), containing millions of parameters. However, CNN models need many training samples. A small number of training samples in CNN models may lead to overfitting problems. With the continuous research of CNN, other networks have been proposed, such as randomized neural networks (RNNs). Schmidt neural network (SNN), random vector functional link (RVFL), and extreme learning machine (ELM) are three types of RNNs.Methods: We propose three novel models to classify brain diseases to cope with these problems. The proposed models are DenseNet-based SNN (DSNN), DenseNet-based RVFL (DRVFL), and DenseNet-based ELM (DELM). The backbone of the three proposed models is the pre-trained "customize" DenseNet. The modified DenseNet is fine-tuned on the empirical dataset. Finally, the last five layers of the fine-tuned DenseNet are substituted by SNN, ELM, and RVFL, respectively.Results: Overall, the DSNN gets the best performance among the three proposed models in classification performance. We evaluate the proposed DSNN by five-fold cross-validation. The accuracy, sensitivity, specificity, precision, and F1-score of the proposed DSNN on the test set are 98.46% +/- 2.05%, 100.00% +/- 0.00%, 85.00% +/- 20.00%, 98.36% +/- 2.17%, and 99.16% +/- 1.11%, respectively. The proposed DSNN is compared with restricted DenseNet, spiking neural network, and other state-of-the-art methods. Finally, our model obtains the best results among all models.Conclusions: DSNN is an effective model for classifying brain diseases.Hope Foundation for Cancer Research, UK RM60G0680Royal Society International Exchanges Cost Share Award, UK RP202G0230Medical Research Council Confidence in Concept Award, UK MC_PC_17171British Heart Foundation Accelerator Award, UK AA/18/3/34220Sino-UK Industrial Fund, UK RP202G0289Global Challenges Research Fund (GCRF), UK P202PF11LIAS Pioneering Partnerships award, UK P202ED10Data Science Enhancement Fund, UK P202RE237Guangxi Key Laboratory of Trusted Software kx20190

BCNet: A Novel Network for Blood Cell Classification

The paper was partially supported by: Royal Society International Exchanges Cost Share Award, United Kingdom (RP202G0230); Medical Research Council Confidence in Concept Award, United Kingdom (MC_PC_17171); Hope Foundation for Cancer Research, United Kingdom (RM60G0680); British Heart Foundation Accelerator Award, United Kingdom (AA/18/3/34220); Sino-United Kingdom Industrial Fund, United Kingdom (RP202G0289); Global Challenges Research Fund (GCRF), United Kingdom (P202PF11); Guangxi Key Laboratory of Trusted Software (kx201901).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 multiclassification 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.Royal Society International Exchanges Cost Share Award RP202G0230Medical Research Council Confidence in Concept Award, United Kingdom MC_PC_17171Hope Foundation for Cancer Research, United Kingdom RM60G0680British Heart Foundation Accelerator Award, United Kingdom AA/18/3/34220Sino-United Kingdom Industrial Fund, United Kingdom RP202G0289Global Challenges Research Fund (GCRF), United Kingdom P202PF11Guangxi Key Laboratory of Trusted Software kx20190

BCNet: A Novel Network for Blood Cell Classification

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

Preoperative Strength Training for Clinical Outcomes Before and After Total Knee Arthroplasty: A Systematic Review and Meta-Analysis

BackgroundThere is an increasing interest in preoperative strength training for promoting post-operative rehabilitation, but the effectiveness of preoperative strength training for clinical outcomes after total knee arthroplasty (TKA) remains controversial.ObjectiveThis study aims to systematically evaluate the effect of preoperative strength training on clinical outcomes before and after TKA.MethodsWe systematically searched PubMed, Cochrane Library, Web of Science, and EMBASE databases from the inception to November 17, 2021. The meta-analysis was performed to evaluate the effects of preoperative strength training on clinical outcomes before and after TKA.ResultsSeven randomized controlled trials (RCTs) were included (n = 306). Immediately before TKA, the pooled results showed significant improvements in pain, knee function, functional ability, stiffness, and physical function in the strength training group compared with the control group, but not in strength (quadriceps), ROM, and WOMAC (total). Compared with the control group, the results indicated strength training had a statistically significant improvement in post-operative knee function, ROM, and functional ability at less than 1 month and 3 months, and had a statistically significant improvement in post-operative strength (quadriceps), stiffness, and WOMAC (total) at 3 months, and had a statistically significant improvement in post-operative pain at 6 months. However, the results indicated strength training had no statistically significant improvement in post-operative strength (quadriceps) at less than 1 month, 6, and 12 months, had no statistically significant improvement in post-operative pain at less than 1 month, 3, and 12 months, had no statistically significant improvement in post-operative knee function at 6 and 12 months, and had no statistically significant improvement in post-operative physical function at 3 months.ConclusionsPreoperative strength training may be beneficial to early rehabilitation after TKA, but the long-term efficacy needs to be further determined. At the same time, more caution should be exercised when interpreting the clinical efficacy of preoperative strength training for TKA

ROENet: A ResNet-Based Output Ensemble for Malaria Parasite Classification

(1) Background: People may be infected with an insect-borne disease (malaria) through the blood input of malaria-infected people or the bite of Anopheles mosquitoes. Doctors need a lot of time and energy to diagnose malaria, and sometimes the results are not ideal. Many researchers use CNN to classify malaria images. However, we believe that the classification performance of malaria parasites can be improved. (2) Methods: In this paper, we propose a novel method (ROENet) to automatically classify malaria parasite on the blood smear. The backbone of ROENet is the pre-trained ResNet-18. We use randomized neural networks (RNNs) as the classifier in our proposed model. Three RNNs are used in ROENet, which are random vector functional link (RVFL), Schmidt neural network (SNN), and extreme learning machine (ELM). To improve the performance of ROENet, the results of ROENet are the ensemble outputs from three RNNs. (3) Results: We evaluate the proposed ROENet by five-fold cross-validation. The specificity, F1 score, sensitivity, and accuracy are 96.68 ± 3.81%, 95.69 ± 2.65%, 94.79 ± 3.71%, and 95.73 ± 2.63%, respectively. (4) Conclusions: The proposed ROENet is compared with other state-of-the-art methods and provides the best results of these methods

ROENet: A ResNet-Based Output Ensemble for Malaria Parasite Classification

(1) Background: People may be infected with an insect-borne disease (malaria) through the blood input of malaria-infected people or the bite of Anopheles mosquitoes. Doctors need a lot of time and energy to diagnose malaria, and sometimes the results are not ideal. Many researchers use CNN to classify malaria images. However, we believe that the classification performance of malaria parasites can be improved. (2) Methods: In this paper, we propose a novel method (ROENet) to automatically classify malaria parasite on the blood smear. The backbone of ROENet is the pre-trained ResNet-18. We use randomized neural networks (RNNs) as the classifier in our proposed model. Three RNNs are used in ROENet, which are random vector functional link (RVFL), Schmidt neural network (SNN), and extreme learning machine (ELM). To improve the performance of ROENet, the results of ROENet are the ensemble outputs from three RNNs. (3) Results: We evaluate the proposed ROENet by five-fold cross-validation. The specificity, F1 score, sensitivity, and accuracy are 96.68 ± 3.81%, 95.69 ± 2.65%, 94.79 ± 3.71%, and 95.73 ± 2.63%, respectively. (4) Conclusions: The proposed ROENet is compared with other state-of-the-art methods and provides the best results of these methods

LncRNA DLEU1 contributes to colorectal cancer progression via activation of KPNA3

Abstract Background Accumulating evidences show that long noncoding RNAs (lncRNA) play essential roles in the development and progression of various malignancies. However, their functions remains poorly understood and many lncRNAs have not been defined in colorectal cancer (CRC). In this study, we investigated the role of DLEU1 in CRC. Methods Quantitative real-time PCR was used to detect the expression of DLEU1 and survival analysis was adopted to explore the association between DLEU1 expression and the prognosis of CRC patients. CRC cells were stably transfected with lentivirus approach and cell proliferation, migration, invasion and cell apoptosis, as well as tumorigenesis in nude mice were performed to assess the effects of DLEU1 in BCa. Biotin-coupled probe pull down assay, RNA immunoprecipitation and Fluorescence in situ hybridization assays were conducted to confirm the relationship between DLEU1 and SMARCA1. Results Here we revealed that DLEU1 was crucial for activation of KPNA3 by recruiting SMARCA1, an essential subunit of the NURF chromatin remodeling complex, in CRC. DLEU1 was indispensible for the deposition of SMARCA1 at the promoter of KPNA3 gene. Increased expression of DLEU1 and KPNA3 was observed in human CRC tissues. And higher expression of DLEU1 or KPNA3 in patients indicates lower survival rate and poorer prognosis. DLEU1 knockdown remarkably inhibited CRC cell proliferation, migration and invasion in vitro and in vivo while overexpressing KPNA3 in the meantime reversed it. Conclusions Our results identify DLEU1 as a key regulator by a novel DLEU1/SMARCA1/KPNA3 axis in CRC development and progression, which may provide a potential biomarker and therapeutic target for the management of CRC

DLBCNet: A Deep Learning Network for Classifying Blood Cells

Background: Blood is responsible for delivering nutrients to various organs, which store important health information about the human body. Therefore, the diagnosis of blood can indirectly help doctors judge a person’s physical state. Recently, researchers have applied deep learning (DL) to the automatic analysis of blood cells. However, there are still some deficiencies in these models. Methods: To cope with these issues, we propose a novel network for the multi-classification of blood cells, which is called DLBCNet. A new specifical model for blood cells (BCGAN) is designed to generate synthetic images. The pre-trained ResNet50 is implemented as the backbone model, which serves as the feature extractor. The extracted features are fed to the proposed ETRN to improve the multi-classification performance of blood cells. Results: The average accuracy, average sensitivity, average precision, average specificity, and average f1-score of the proposed model are 95.05%, 93.25%, 97.75%, 93.72%, and 95.38%, accordingly. Conclusions: The performance of the proposed model surpasses other state-of-the-art methods in reported classification results