River Thames and IJNS

River Thames is the longest river in England and the second-longest in the United Kingdom. Rafting along the River Thames with a length of 346 kilometres is demanding; hence, a group of double-bladed paddling can help ease the rifting. In a paper we published in IJNS, we had co-authors from three countries. Each author in the team helped us raft through the River Thames. [Opening paragraph

Fruit category classification by fractional Fourier entropy with rotation angle vector grid and stacked sparse autoencoder

AimFruit category classification is important in factory packing and transportation, price prediction, dietary intake, and so forth.MethodsThis study proposed a novel artificial intelligence system to classify fruit categories. First, 2D fractional Fourier entropy with rotation angle vector grid was used to extract features from fruit images. Afterwards, a five-layer stacked sparse autoencoder was used as the classifier.ResultsTen runs on the test set showed our method achieved a micro-averaged F1 score of 95.08% for an 18-category fruit dataset.ConclusionOur method gives better micro-averaged F1 score than 10 state-of-the-art approaches.</div

Fingerspelling Recognition by 12-Layer CNN with Stochastic Pooling

Fingerspelling is a method of spelling words via hand movements. This study aims to propose a novel fingerspelling recognition system. We use 1320 fingerspelling images in our dataset. Our method is based on the convolutional neural network (CNN) model. We propose a 12-layer CNN as the backbone. Particularly, stochastic pooling (SP) is used to help solve the problems caused by max pooling or average pooling. In addition, an improved 20-way data augmentation method is proposed to circumvent overfitting. Our method is dubbed CNNSP. The results show that our CNNSP method achieved a micro-averaged F1 (MAF) score of 90.04 ± 0.82%. In contrast, the MAFs of l2-pooling, average pooling, and max pooling are 86.21 ± 1.12%, 87.54 ± 1.39%, and 89.07 ± 0.78%, respectively. Our CNNSP attains better results than eight state-of-the-art fingerspelling recognition methods. Besides, the SP is better than l2-pooling, average pooling, and max pooling.</p

Community-Acquired Pneumonia Recognition by Wavelet Entropy and Cat Swarm Optimization

Community-acquired pneumonia (CAP) is a type of pneumonia acquired outside the hospital. To recognize CAP more efficiently and more precisely, we propose a novel method—wavelet entropy (WE) is used as the feature extractor, and cat swarm optimization (shortened as CSO) is used to train an artificial neural network (ANN). Our method is abbreviated as WE-ANN-CSO. This proposed WE-ANN-CSO algorithm yields a sensitivity of 91.64 ± 0.99%, a specificity of 90.64 ± 2.11%, a precision of 90.96 ± 1.81%, an accuracy of 91.14 ± 1.12%, an F1 score of 91.29 ± 1.04%, an MCC of 82.31 ± 2.22%, an FMI of 91.29 ± 1.03%, and an AUC of 0.9527. This proposed WE-ANN-CSO algorithm provides better performances than four state-of-the-art approaches.</p

SAFNet: A deep spatial attention network with classifier fusion for breast cancer detection

Breast cancer is a top dangerous killer for women. An accurate early diagnosis of breast cancer is the primary step for treatment. A novel breast cancer detection model called SAFNet is proposed based on ultrasound images and deep learning. We employ a pre-trained ResNet-18 embedded with the spatial attention mechanism as the backbone model. Three randomized network models are trained for prediction in the SAFNet, which are fused by majority voting to produce more accurate results. A public ultrasound image dataset is utilized to evaluate the generalization ability of our SAFNet using 5-fold cross-validation. The simulation experiments reveal that the SAFNet can produce higher classification results compared with four existing breast cancer classification methods. Therefore, our SAFNet is an accurate tool to detect breast cancer that can be applied in clinical diagnosis

ELMGAN: A GAN-based efficient lightweight multi-scale-feature-fusion multi-task model

Cell segmentation and counting is a time-consuming and important experimental step in traditional biomedical research. Many current counting methods are Point-based methods which require exact cell locations. However, there are few such cell datasets with detailed object coordinates. Most existing cell datasets only have the total number of cells and a global segmentation annotation. To effectively use existing datasets, we divide the cell counting task into the cell's number prediction and cell segmentation. We propose a GAN-based efficient lightweight multi-scale-feature-fusion multi-task model (ELMGAN). To coordinate the learning of these two tasks, we propose a Norm-Combined Hybrid loss function (NH loss) and use the method of the generative adversarial network to train our networks. We propose a new Fold Beyond-nearest Upsampling method (FBU) in our lightweight and fast multi-scale-feature-fusion multi-task generator (LFMMG), which is twice as fast as the traditional interpolation upsampling method. We use multi-scale feature fusion technology to improve the quality of segmentation images. LFMMG reduces the number of parameters by nearly 50% compared with U-Net and gets better performance on cell segmentation. Compared with the traditional GAN model, our method improves the speed of image processing by nearly ten times. In addition, we also propose a Coordinated Multitasking Training Discriminator (CMTD) to refine the accuracy of the details of the features. Our method achieves non-Point-based counting that no longer needs to annotate the exact position of each cell in the image during the training and achieves excellent results in cell counting and segmentation

MIDCAN: A multiple input deep convolutional attention network for Covid-19 diagnosis based on chest CT and chest X-ray

BackgroundCOVID-19 has caused 3.34m deaths till 13/May/2021. It is now still causing confirmed cases and ongoing deaths every day.MethodThis study investigated whether fusing chest CT with chest X-ray can help improve the AI's diagnosis performance. Data harmonization is employed to make a homogeneous dataset. We create an end-to-end multiple-input deep convolutional attention network (MIDCAN) by using the convolutional block attention module (CBAM). One input of our model receives 3D chest CT image, and other input receives 2D X-ray image. Besides, multiple-way data augmentation is used to generate fake data on training set. Grad-CAM is used to give explainable heatmap.ResultsThe proposed MIDCAN achieves a sensitivity of 98.10±1.88%, a specificity of 97.95±2.26%, and an accuracy of 98.02±1.35%.ConclusionOur MIDCAN method provides better results than 8 state-of-the-art approaches. We demonstrate the using multiple modalities can achieve better results than individual modality. Also, we demonstrate that CBAM can help improve the diagnosis performance.</div

WVALE: Weak variational autoencoder for localisation and enhancement of COVID-19 lung infections

Background and objective: The COVID-19 pandemic is a major global health crisis of this century. The use of neural networks with CT imaging can potentially improve clinicians’ efficiency in diagnosis. Previous studies in this field have primarily focused on classifying the disease on CT images, while few studies targeted the localisation of disease regions. Developing neural networks for automating the latter task is impeded by limited CT images with pixel-level annotations available to the research community. Methods: This paper proposes a weakly-supervised framework named “Weak Variational Autoencoder for Localisation and Enhancement” (WVALE) to address this challenge for COVID-19 CT images. This framework includes two components: anomaly localisation with a novel WVAE model and enhancement of supervised segmentation models with WVALE. Results: The WVAE model have been shown to produce high-quality post-hoc attention maps with fine borders around infection regions, while weak supervision segmentation shows results comparable to conventional supervised segmentation models. The WVALE framework can enhance the performance of a range of supervised segmentation models, including state-of-art models for the segmentation of COVID-19 lung infection. Conclusions: Our study provides a proof-of-concept for weakly supervised segmentation and an alternative approach to alleviate the lack of annotation, while its independence from classification & segmentation frameworks makes it easily integrable with existing systems

TBNet: a context-aware graph network for tuberculosis diagnosis

Background and objectiveTuberculosis (TB) is an infectious bacterial disease. It can affect the human lungs, brain, bones, and kidneys. Pulmonary tuberculosis is the most common. This airborne bacterium can be transmitted with the droplets by coughing and sneezing. So far, the most convenient and effective method for diagnosing TB is through medical imaging. Computed tomography (CT) is the first choice for lung imaging in clinics because the conditions of the lungs can be interpreted from CT images. However, manual screening poses an enormous burden for radiologists, resulting in high inter-observer variances. Hence, developing computer-aided diagnosis systems to implement automatic TB diagnosis is an emergent and significant task for researchers and practitioners. This paper proposed a novel context-aware graph neural network called TBNet to detect TB from chest CT imagesMethodsTraditional convolutional neural networks can extract high-level image features to achieve good classification performance on the ImageNet dataset. However, we observed that the spatial relationships between the feature vectors are beneficial for the classification because the feature vector may share some common characteristics with its neighboring feature vectors. To utilize this context information for the classification of chest CT images, we proposed to use a feature graph to generate context-aware features. Finally, a context-aware random vector functional-link net served as the classifier of the TBNet to identify these context-aware features as TB or normalResultsThe proposed TBNet produced state-of-the-art classification performance for detecting TB from healthy samples in the experimentsConclusionsOur TBNet can be an accurate and effective verification tool for manual screening in clinical diagnosis.</div

PBTNet: A New Computer-Aided Diagnosis System for Detecting Primary Brain Tumors

Brain tumors are among the leading human killers. There are over 120 different types of brain tumors, but they mainly fall into two groups: primary brain tumors and metastatic brain tumors. Primary brain tumors develop from normal brain cells. Early and accurate detection of primary brain tumors is vital for the treatment of this disease. Magnetic resonance imaging is the most common method to diagnose brain diseases, but the manual interpretation of the images suffers from high inter-observer variance. In this paper, we presented a new computer-aided diagnosis system named PBTNet for detecting primary brain tumors in magnetic resonance images. A pre-trained ResNet-18 was selected as the backbone model in our PBTNet, but it was fine-tuned only for feature extraction. Then, three randomized neural networks, Schmidt neural network, random vector functional-link, and extreme learning machine served as the classifiers in the PBTNet, which were trained with the features and their labels. The final predictions of the PBTNet were generated by the ensemble of the outputs from the three classifiers. 5-fold cross-validation was employed to evaluate the classification performance of the PBTNet, and experimental results demonstrated that the proposed PBTNet was an effective tool for the diagnosis of primary brain tumors