Classification of Wildlife Based on Transfer Learning

We present a multigrid-in-channels (MGIC) approach that tackles the quadratic growth of the number of parameters with respect to the number of channels in standard convolutional neural networks (CNNs). Thereby our approach addresses the redundancy in CNNs that is also exposed by the recent success of lightweight CNNs. Lightweight CNNs can achieve comparable accuracy to standard CNNs with fewer parameters; however, the number of weights still scales quadratically with the CNN’s width. Our MGIC architectures replace each CNN block with an MGIC counterpart that utilizes a hierarchy of nested grouped convolutions of small group size to address this. Hence, our proposed architectures scale linearly with respect to the network’s width while retaining full coupling of the channels as in standard CNNs. Our extensive experiments on image classification, segmentation, and point cloud classification show that applying this strategy to different architectures like ResNet and MobileNetV3 reduces the number of parameters while obtaining similar or better accuracy.

Convolutional Neural Network (CNN) is a deep learning algorithm that is widely used to identify and classify a digital image object. In this study the Convolutional Neural Network (CNN) is used as an algorithm that functions to identify leaf types (certain plants) based on images obtained from a public dataset provider named Daun Jamu Indonesia. The existence of image characteristics causes the assistance process to require a more detailed feature selection process. Therefore the CNN method is used in order to solve the problem. The Convolutional Neural Network (CNN) method is capable of performing image recognition by minimizing feature extraction. CNN is also reliable in processing unstructured data because it uses a multi-layered structure of artificial reasoning networks. The image recognition process is carried out by looking for the shape of the model that matches the processed data in order to get the best results. In this study, the augmentation process was carried out on the training data and validation data so that overfitting does not occur in the Convolutional Neural Network (CNN). The results obtained in this study indicate that the Convolutional Neural Network (CNN) method can identify leaf types with a measured accuracy rate of 92% using the Confusion Matrix evaluation method. It is hoped that this research can be used as a reference for the use of the Convolutional Neural Network (CNN) method for image data, especially plant leaf types.

An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea

Breast cancer is the dominant cancer among women as it accounts for about one-quarter of all cancer cases in females. The digitized images of Hematoxylin and Eosin (H&E) stained slides of breast cancer specimens carry valuable diagnostic information. However, inspecting these slides manually is a non-trivial task prone to subjective interpretation. Digital pathology (DP) and artificial intelligence (AI) open an opportunity for objective interpretation of the image data. It is challenging to automate the segmentation process in the whole slide images due to the visual complexity of tissue appearance without the need for tedious and time-consuming fine annotations. Many algorithms classify the tissue regions into different types instead of segmenting them, as the classification algorithms require coarse annotations that are easier to acquire. In this paper, we propose a new segmentation framework that combines the simple non-iterative clustering algorithm with a standard convolutional neural network (CNN) classifier to segment whole slide images into different tissue types. In addition, a graph-based post-processing step is applied to improve the framework segmentation performance further. The results show promising improvement to the CNN classifier based coarse segmentation, which would give better feasibility to quantify and study tissues’ mutual relationships.

Standard convolutional neural networks (CNNs) have large amounts of data redundancy, and the same accuracy can be obtained even in lower bit weights instead of floating-point representation. Most CNNs have to be developed and executed on high-end GPU-based workstations, for which it is hard to transplant the existing implementations onto portable edge FPGAs because of the limitation of on-chip block memory storage size and battery capacity. In this paper, we present adaptive pointwise convolution and 2D convolution joint network (AP2D-Net), an ultra-low power and relatively high throughput system combined with dynamic precision weights and activation. Our system has high performance, and we make a trade-off between accuracy and power efficiency by adopting unmanned aerial vehicle (UAV) object detection scenarios. We evaluate our system on the Zynq UltraScale+ MPSoC Ultra96 mobile FPGA platform. The target board can get the real-time speed of 30 fps under 5.6 W, and the FPGA on-chip power is only 0.6 W. The power efficiency of our system is 2.8× better than the best system design on a Jetson TX2 GPU and 1.9× better than the design on a PYNQ-Z1 SoC FPGA.

Deep Learning has shown great success in many fields, however, transferring this potential to remote sensing imagery interpretation is still a challenging task due to the special data properties, e.g., low Signal-to-Noise Ratio (SNR), high variation, etc. In this work, a lifting scheme based deep model is presented for remote sensing imagery classification. The main idea underlying this scheme is that, an innovative strategy is adopted to decompose the input image into two compact and low-resolution components, and these components are then fed into a standard Convolutional Neural Network (CNN) for classification task. More precisely, (1) one decomposed component is devoted to enhancing the latent patterns and simultaneously attenuating the random variation in the input, and (2) the other component is used to capture the local structural information in the input. The experimental results show that the lifting deep model is computationally efficient and has promising potential, improving the classification accuracy by about 5.7% and obtaining 2.69 x speed-up compared with the counterpart CNN.

Demosaicking is reconstructing a RGB image from the mosaicked image recorded by a single sensor with a color filter array (CFA), which has achieved great progress via deep learning. Some works on joint CFA design and demosaicking via deep learning have been presented. However, almost all existing approaches focus only on optimizing filter arrangements without considering the used spectral sensitivity functions (SSFs). The commonly used SSFs are to mimic human trichromatic perception, which are not optimal for deep-learning-based de-mosaicking. In this paper, we simultaneously learn the CFA and demosaicking using a deep convolutional neural network, where both the filter arrangement and used SSFs are optimized. By modeling the applying of SSFs as a convolutional layer with physical constraints, we formulate our joint learning approach as an end-to-end autoencoder, which can be trained as the standard convolutional neural networks. Our approach allows the designing of CFA patterns with arbitrary sizes, where the filter arrangements can be predefined or not. We demonstrate the effectiveness and advantages of our approach by comparing with fixed Gaussian functions and various predefined filter arrangements.

The kidney biopsy based diagnosis of Lupus Nephritis (LN) is characterized by low inter-observer agreement, with misdiagnosis being associated with increased patient morbidity and mortality. Although various Computer Aided Diagnosis (CAD) systems have been developed for other nephrohistopathological applications, little has been done to accurately classify kidneys based on their kidney level Lupus Glomerulonephritis (LGN) scores. The successful implementation of CAD systems has also been hindered by the diagnosing physician's perceived classifier strengths and weaknesses, which has been shown to have a negative effect on patient outcomes. We propose an Uncertainty-Guided Bayesian Classification (UGBC) scheme that is designed to accurately classify control, class I/II, and class III/IV LGN (3 class) at both the glomerular-level classification task (26,634 segmented glomerulus images) and the kidney-level classification task (87 MRL/lpr mouse kidney sections). Data annotation was performed using a high throughput, bulk labeling scheme that is designed to take advantage of Deep Neural Network's (or DNNs) resistance to label noise. Our augmented UGBC scheme achieved a 94.5% weighted glomerular-level accuracy while achieving a weighted kidney-level accuracy of 96.6%, improving upon the standard Convolutional Neural Network (CNN) architecture by 11.8% and 3.5% respectively.

Electro-optical (EO) sensors and synthetic aperture radars (SAR) have long been used in automatic target recognition (ATR) classifiers. However, maintaining robust target ID performance with ATRs trained on data from a single EO or SAR sensor across a wide range of operating conditions is challenging due to the variability in the sun illumination and surface orientation of targets, respectively. Fusion of ATR classifiers trained on EO and SAR data can improve target ID performance. In this paper, we implement multiple fusion algorithms at the decision level and the feature level using classifiers trained on a standard convolutional neural network (CNN) architecture. Under favorable conditions – when the train and test data have the same operating conditions – many fusion algorithms offer significant gain at lower image resolution compared to a single sensor at higher resolution. However, as the operating conditions become more disjoint, only the most robust fusion techniques show significant performance gain. A feature-level fusion algorithm developed by researchers at Sandia National Laboratories and modified for use in this paper showed the most robustness to disjoint operating conditions. Moreover, this algorithm was robust to geometric angle variation and therefore could be used with sensors mounted on different platforms.

Coronavirus (COVID-19) is a pandemic, which caused suddenly unexplained pneumonia cases and caused a devastating effect on global public health. Computerized tomography (CT) is one of the most effective tools for COVID-19 screening. Since some specific patterns such as bilateral, peripheral, and basal predominant ground-glass opacity, multifocal patchy consolidation, crazy-paving pattern with a peripheral distribution can be observed in CT images and these patterns have been declared as the findings of COVID-19 infection. For patient monitoring, diagnosis and segmentation of COVID-19, which spreads into the lung, expeditiously and accurately from CT, will provide vital information about the stage of the disease. In this work, we proposed a SegNet-based network using the attention gate (AG) mechanism for the automatic segmentation of COVID-19 regions in CT images. AGs can be easily integrated into standard convolutional neural network (CNN) architectures with a minimum computing load as well as increasing model precision and predictive accuracy. Besides, the success of the proposed network has been evaluated based on dice, Tversky, and focal Tversky loss functions to deal with low sensitivity arising from the small lesions. The experiments were carried out using a fivefold cross-validation technique on a COVID-19 CT segmentation database containing 473 CT images. The obtained sensitivity, specificity, and dice scores were reported as 92.73%, 99.51%, and 89.61%, respectively. The superiority of the proposed method has been highlighted by comparing with the results reported in previous studies and it is thought that it will be an auxiliary tool that accurately detects automatic COVID-19 regions from CT images.

Wafer map analysis is a key step in semiconductor manufacturing process. The various gross failing area (GFA) patterns on wafer maps are helpful to identify the root causes of failures. In this work, a semi-supervised classification framework is proposed for wafer map analysis. We use inline defect wafer map as the example, especially for GFA pattern classification. After data preprocessing and selection, Ladder network is adopted here to classify wafer maps compared with a standard convolutional neural network (CNN) model on two real-world datasets. The results illustrate that Ladder network is consistently and substantially better than CNN model across various training data percentages by effective utilization of the unlabeled data.

Abstract. Crowd counting, a critical component in the management and safety planning of large gatherings and public spaces, is essential for ensuring smooth event operations and preventing potential overcrowding issues. While the standard convolutional neural network (CNN) based model performs well in head counting tasks, it has certain drawbacks when applied to complex scenarios. With the rapid development of artificial intelligence, Transformer models that rely on self-attention mechanisms, as Swin Transformer, have demonstrated exceptional performance in visual tasks, such as image classification, and segmentation in recent times. This study examines the experimental findings of Swin Transformer's head counting tasks and contrasts them with the CNN-based model. Mean Absolute Error (MAE) and Mean Square Error (MSE) evaluation indicators show that the Transformer model outperforms the classic CNN model in terms of generalization abilities when dealing with complicated scenarios. Future research work will increase the diversity of data sets and focus on optimizing model structure and improving training efficiency.

PurposeClimate-induced damage is a pressing problem for the preservation of cultural properties. Their physical deterioration is often the cumulative effect of different environmental hazards of variable intensity. Among these, fluctuations of temperature and relative humidity may cause nonrecoverable physical changes in building envelopes and artifacts made of hygroscopic materials, such as wood. Microclimatic fluctuations may be caused by several factors, including the presence of many visitors within the historical building. Within this framework, the current work is focused on detecting events taking place in two Norwegian stave churches, by identifying the fluctuations in temperature and relative humidity caused by the presence of people attending the public events.Design/methodology/approachThe identification of such fluctuations and, so, of the presence of people within the churches has been carried out through three different methods. The first is an unsupervised clustering algorithm here termed “density peak,” the second is a supervised deep learning model based on a standard convolutional neural network (CNN) and the third is a novel ad hoc engineering feature approach “unexpected mixing ratio (UMR) peak.”FindingsWhile the first two methods may have some instabilities (in terms of precision, recall and normal mutual information [NMI]), the last one shows a promising performance in the detection of microclimatic fluctuations induced by the presence of visitors.Originality/valueThe novelty of this work stands in using both well-established and in-house ad hoc machine learning algorithms in the field of heritage science, proving that these smart approaches could be of extreme usefulness and could lead to quick data analyses, if used properly.

In this paper, we present a new deep learning-based approach for histopathology image classification. Our method is built upon standard convolutional neural networks (CNNs), and incorporates two separate attention modules for more effective feature learning. In particular, the attention modules infer the attention maps along different dimensions, which help focus the CNNs on critical image regions, as well as highlight discriminative feature channels while suppressing the irrelevant information with respect to the classification task. The attention modules are light-weight, and enhances the feature representation with small extra computational overhead. Experimental results on the publicly available BreakHis dataset demonstrate that our method outperforms the state-of-the-arts by a large margin.

Network traffic classification aims to recognize different application or traffic types by analyzing received data packets. This paper presents a neural network model with deep and parallel network-in-network (NIN) structures for classifying encrypted network traffic. Comparing with standard convolutional neural networks (CNN), NIN adopts a micro network after each convolution layer to enhance local modeling. Besides, NIN utilizes a global average pooling instead of traditional fully connected layers before final classification, which reduces the number of model parameters significantly. In our proposed method, deep NIN models with multiple MLP convolutional layers are built to map fixed-length packet vectors towards application or traffic labels. Furthermore, a parallel decision strategy of building two sub-networks to process packet header and packet body separately is designed considering that they may carry different kinds of clues for classification. The results of our experiments on the “ISCX VPN-nonVPN” encrypted traffic dataset show that NIN models can achieve a better balance between classification accuracy and model complexity than conventional CNNs. The parallel decision strategy can further improve the accuracy of using single NIN model for encrypted network traffic classification. Finally, the test set F1 scores of 0.983 and 0.985 are achieved for traffic characterization and application identification respectively.