Key-Frame Extraction for Reducing Human Effort in Object Detection Training for Video Surveillance

Disease prediction based on modeling the correlations between compounded indicator factors is a widely used technique in high incidence chronic disease prevention diagnosis. Predictive models based on personal health information have been developed historically by using simple regression fitting over relatively few factors. Regression approaches have been favored in previous prediction modeling approaches because they are simplest and do not assume any non-linearity in the model for contributions of the chosen factors. In practice, many factors are correlated and have underlying non-linear relationships to the predicted outcome. Deep learning offers a means to construct a more complex modeling approach, along with automation and adaptation. The aim of this paper is to assess the ability of a deep learning model to predict the heart disease incidence using a common benchmark dataset (University of California, Irvine (UCI) dataset). The performance of deep learning model has been compared with four popular machine learning models (two linear and two nonlinear) in predicting the incidence of heart disease using data from 567 participants from two cohorts taken from UCI database. The deep learning model was able to achieve the best accuracy of 94% and an AUC score of 0.964 when compared to other models. The performance of deep learning and nonlinear machine learning models was significantly better compared to the linear machine learning models with increase in the dataset size.

Emotion recognition (ER) is crucial for understanding human behaviours, social interactions, and psychological well-being. Electroencephalography (EEG) has emerged as a promising tool for capturing the neural correlates of emotions. This work is a systematic review of articles in ER using EEG signals. A total of 120 articles from 1041 articles were selected based on PRISMA guidelines using defined inclusion and exclusion criteria, published between 2018 and 2024. This article aims to provide an in-depth understanding of the current landscape of ER from EEG signals utilizing deep learning (DL). This review offers valuable guidance for researchers and practitioners seeking more refined and reliable emotion classification systems. To explore the effectiveness of DL models in EEG-based ER, several potential DL models, such as convolutional neural network, long short-term memory (LSTM), gated recurrent unit (GRU), hybrid bidirectional LSTM (BiLSTM), bidirectional GRU, and advanced DL models such as convolutional recurrent neural network and EEG-Conformer models are applied to two popular datasets, SEED and GAMEEMO, respectively, to depict the full process of ER. Additionally, the performance of DL models is also compared with the performance of basic machine learning (ML) models such as SVM, k-nearest neighbors, logistic regression, and boosting algorithms such as AdaBoost, XGBoost and LightGBM. Through extensive experiments and performance evaluations, the performance of different models when applied to the datasets mentioned above is compared. The accuracy, precision, recall, and F1-scores are analysed to determine the most effective model for EEG-based ER. The findings of this study demonstrate that the performance of hybrid DL models is more efficacious than that of ML models. The best-performing model (BiLSTM) classified the emotions, with an accuracy of 90.54% when applied to the GAMEEMO dataset. This research contributes to the growing body of literature on ER and provides insights into the feasibility of using EEG signals to understand emotional states, and presents a structured roadmap for future exploration. The findings can aid in the development of more accurate and reliable ER systems, which can have wide-ranging applications in psychology, social sciences, and human-computer interactions.

This dissertation explores the critical role of loss functions in enhancing the predictive performance of deep machine learning models. Loss functions are an integral element of all the ongoing advances we witness daily in this domain. I design custom loss functions and their impacts on various machine learning tasks, particularly in computer vision. In the first stage of my research, I aim to improve the prediction performance of deep learning models by providing them with more precise feedback associated with task requirements. This led me to create the concept of assistive loss functions. My first proposed loss function, inspired by the Active Shape Model (ASM), is ASM Loss, designed to improve the detection of facial landmarks and head pose accuracy in facial images. Additionally, influenced by Knowledge Distillation (KD), I introduce KD-Loss, a loss designed specifically for regression tasks, focusing on detecting facial landmarks. Proceeding to the second phase of my research, I focus on regression problems. Building upon my prior work, I introduce Adaptive Coordinate-based Regression (ACR)-Loss, a context-aware loss function with face alignment implication. I provide a mechanism to dynamically measure how hard the localization of each landmark point is, and accordingly, ACR-Loss provides unique feedback to the model. Furthermore, I introduce Intensity-aware Categorical (IC)-Loss for detecting sagittal cervical spine landmark points in medical images. It effectively deploys characteristics from classification problems to enhance regression optimization. Both ACR-Loss and IC-Loss comprise contextual information, leading to a significant enhancement in localization precision. In the third phase, I turned my attention toward classification problems within the domain of deep machine learning. I introduce Adaptive Correlation-based (Ad-corre) Loss, designed to enhance the discriminative capabilities of deep learning models, with a specific focus on improving classification accuracy in automatic facial expression recognition. Furthermore, I introduce Informative (Info) Loss, a specialized loss function developed for medical applications, specifically aimed at distinguishing between Mild Cognitive Impairment and Normal Cognition groups based on transcriptions of interviews conducted as part of the I-CONECT study. Overall, I have proposed custom loss functions to improve the predictive performance in both regression and classification problems. Furthermore, I have introduced assistive loss functions, designed to enhance the performance of the main loss functions. The collective outcomes of this extensive exploration highlight the critical role of loss functions, adapting and optimizing them for various domains in deep machine learning. This research suggests and proposes a variety of methodologies for designing adaptive and context-aware loss functions within deep machine learning.

During past few years’ significant usage of video monitoring has been observed in several places. Video monitoring has always helped in improvement of security specific aspects. These systems provide major helping hand in monitoring through multiple object detection methods. However, there are certain challenges related to weather conditions which needs to be addressed. In view of this in this paper, we study multiple object detection in surveillance videos through hybrid deep gaussian mixture ensembles. This method integrates multiple deep gaussian mixture components to form ensembles which helps to develop multiple object detection solutions in surveillance videos. The computational system consists of ensemble of three major deep gaussian mixture components. The ensemble pipeline is formed with augmenting, bagging and stacking methods. The ensemble consists of hybrid object detection paradigm with steps background analysis, video pre-processing, data integration, object detection, foreground detection and post-processing. The computational pipeline is successfully experimented with ViSOR and CDnet 2014 benchmarked datasets. All results are validated with accuracy, precision, sensitivity or recall, specificity, F1-Score and RMSE metrics. Several comparative studies are performed with state-of-the-art methods as well as baselines. The experimental results demonstrate superiority of this method in comparison with other methods. Ablation studies have also been performed here with superior results. This system presents robustness in multiple object detection for real life challenges such as sudden illumination variation, shadow presence, long term occlusion and formation of dynamic backgrounds. It provides cost-effective, more profitable, efficient and sustainable real time multiple object detection solutions

Soil temperature (ST) plays an important role in agriculture and other fields, and has a close relationship with plant growth and development. Therefore, accurate ST prediction methods are widely needed. Deep learning (DL) models have been widely applied for ST prediction. However, the traditional DL models may fail to capture the spatiotemporal relationship due to its complex dependency under different related hydrologic variables. Hence, the DL models with Ensemble Empirical Mode Decomposition (EEMD) are proposed in this study. The proposed models can capture more complex spatiotemporal relationship after decomposing the ST into different intrinsic mode functions. Therefore, the performance of models is further improved. The results show that the performance of DL models with EEMD are better than that of corresponding DL models without EEMD. Moreover, EEMD-Conv3d has the best performance among all the experimental models. It has the highest R2 ranging from 0.9826 to 0.9893, the lowest RMSE ranging from 1.3096 to 1.6497 and the lowest MAE ranging from 0.9656 to 1.2056 in predicting ST at the lead time from one to five days. In addition, the lines between predicted ST and observed ST are closer to the ideal line (y = x) than other DL models. The results show that our EEMD-Conv3D can better capture spatiotemporal correlation and is an applicable method for predicting spatiotemporal ST.

Aims: This study aimed to explore the effect of training set diversity on the performance of deep learning models for predicting early gastric cancer (EGC) under endoscopy. Methods: Images of EGC and non-cancerous lesions under narrow-band imaging (ME-NBI) and magnifying blue laser imaging (ME-BLI) were retrospectively collected. Training set 1 was composed of 150 non-cancerous and 309 EGC ME-NBI images, training set 2 was composed of 1505 non-cancerous and 309 EGC ME-BLI images, and training set 3 was the combination of training set 1 and 2. Test set 1 was composed of 376 non-cancerous and 1052 EGC ME-NBI images, test set 2 consisted of 529 non-cancerous and 71 EGC ME-BLI images, and test set 3 was the combination of test set 1 and test set 2. Three deep learning models, convolutional neural network (CNN) 1, CNN 2 and CNN 3 (CNN 1, CNN 2 and CNN 3 were independently trained using training set 1, training set 2 and training set 3, respectively), were constructed, and their performances on each test set were respectively evaluated. One hundred and thirty-eight ME-NBI videos and 17 ME-BLI videos were further collected to evaluate and compare the performance of each model in real time. Results: On the whole, the performance of CNN 3 was the best. The accuracy (Acc), sensitivity (Sn), specificity (Sp) and area under the curve (AUC) of test set 1 in CNN 3 were 87.89% (1255/1428), 90.96% (342/376), 86.79% (913/1052) and 94.60%, respectively. The Acc, Sn, Sp and AUC of test set 2 in CNN 3 were 95% (570/600), 97.92% (518/529), 73.24% (52/71) and 90.93% respectively. The Acc, Sn, Sp and AUC of test set 3 in CNN 3 were 89.99% (1825/2028), 95.03% (860/905), 85.93% (965/1123) and 94.89%, respectively. The performance of CNN 3 was also the best in videos test set. The Acc, Sn and Sp of videos test set in CNN 3 were 91.03% (142/156), 90.58% (125/138) and 94.44% (17/18), respectively. Conclusions: The deep learning model with the most diverse training data has the best diagnostic effect.

COVID-19 is highly infectious and causes acute respiratory disease. Machine learning (ML) and deep learning (DL) models are vital in detecting disease from computerized chest tomography (CT) scans. The DL models outperformed the ML models. For COVID-19 detection from CT scan images, DL models are used as end-to-end models. Thus, the performance of the model is evaluated for the quality of the extracted feature and classification accuracy. There are four contributions included in this work. First, this research is motivated by studying the quality of the extracted feature from the DL by feeding these extracted to an ML model. In other words, we proposed comparing the end-to-end DL model performance against the approach of using DL for feature extraction and ML for the classification of COVID-19 CT scan images. Second, we proposed studying the effect of fusing extracted features from image descriptors, e.g., Scale-Invariant Feature Transform (SIFT), with extracted features from DL models. Third, we proposed a new Convolutional Neural Network (CNN) to be trained from scratch and then compared to the deep transfer learning on the same classification problem. Finally, we studied the performance gap between classic ML models against ensemble learning models. The proposed framework is evaluated using a CT dataset, where the obtained results are evaluated using five different metrics The obtained results revealed that using the proposed CNN model is better than using the well-known DL model for the purpose of feature extraction. Moreover, using a DL model for feature extraction and an ML model for the classification task achieved better results in comparison to using an end-to-end DL model for detecting COVID-19 CT scan images. Of note, the accuracy rate of the former method improved by using ensemble learning models instead of the classic ML models. The proposed method achieved the best accuracy rate of 99.39%.

Pathological is crucial to cancer diagnosis. Usually, Pathologists draw their conclusion based on observed cell and tissue structure on histology slides. Rapid development in machine learning, especially deep learning have established robust and accurate classifiers. They are being used to analyze histopathological slides and assist pathologists in diagnosis. Most machine learning systems rely heavily on annotated data sets to gain experiences and knowledge to correctly and accurately perform various tasks such as classification and segmentation. This work investigates different granularity of annotations in histopathological data set including image-wise, bounding box, ellipse-wise, and pixel-wise to verify the influence of annotation in pathological slide on deep learning models. We design corresponding experiments to test classification and segmentation performance of deep learning models based on annotations with different annotation granularity. In classification, state-of-the-art deep learning-based classifiers perform better when trained by pixel-wise annotation dataset. On average, precision, recall and F1-score improves by 7.87%, 8.83% and 7.85% respectively. Thus, it is suggested that finer granularity annotations are better utilized by deep learning algorithms in classification tasks. Similarly, semantic segmentation algorithms can achieve 8.33% better segmentation accuracy when trained by pixel-wise annotations. Our study shows not only that finer-grained annotation can improve the performance of deep learning models, but also help extracts more accurate phenotypic information from histopathological slides. Intelligence systems trained on granular annotations may help pathologists inspecting certain regions for better diagnosis. The compartmentalized prediction approach similar to this work may contribute to phenotype and genotype association studies.

ObjectiveThis study aims to tackle the critical challenge of adapting deep learning (DL) models for deployment in real-world healthcare settings, specifically focusing on catastrophic forgetting due to distribution shifts between hospital and non-hospital environments. Metabolic syndrome (MetS) is susceptible to misdiagnosis by DL models due to distribution shifts. This work demonstrates the potential of continual learning (CL) to enhance model performance in MetS identification across diverse settings.Materials and MethodsWe utilized 3 healthcare datasets from 2 distinct settings: Medical Information Mart for Intensive Care (MIMIC; from a hospital setting), National Health and Nutrition Examination Survey (NHANES), and a proprietary dataset (both from non-hospital settings). We proposed a robust MetS identification pipeline with CL strategies and evaluated its effectiveness in mitigating catastrophic forgetting while maintaining high predictive performance under distribution shift.ResultsThe CL method outperformed the control (sequential training without any strategies) method. The CL method reached a cumulative area under the ROC curve (AUROC) of 0.85 and area under the precision-recall curve of 0.65 on the combined test set. Moreover, training order proved critical: models trained from hospital to non-hospital settings achieved a 7.6% improvement in AUROC, increasing from 0.79 to 0.85, compared to the reverse order.DiscussionOur results demonstrate the potential of CL for applications across healthcare settings, particularly between hospital and non-hospital settings. We also discuss the impact of training order on the results.ConclusionThe proposed CL model effectively mitigates catastrophic forgetting, enhancing the overall performance of DL models. Our results underscore the prospect of CL methods in developing medical DL models and maintaining scalability across diverse healthcare settings.

Area under the curve may hide poor generalisation to external datasets

Deep neural networks are widely used in the field of image processing for micromachines, such as in 3D shape detection in microelectronic high-speed dispensing and object detection in microrobots. It is already known that hyperparameters and their interactions impact neural network model performance. Taking advantage of the mathematical correlations between hyperparameters and the corresponding deep learning model to adjust hyperparameters intelligently is the key to obtaining an optimal solution from a deep neural network model. Leveraging these correlations is also significant for unlocking the “black box” of deep learning by revealing the mechanism of its mathematical principle. However, there is no complete system for studying the combination of mathematical derivation and experimental verification methods to quantify the impacts of hyperparameters on the performances of deep learning models. Therefore, in this paper, the authors analyzed the mathematical relationships among four hyperparameters: the learning rate, batch size, dropout rate, and convolution kernel size. A generalized multiparameter mathematical correlation model was also established, which showed that the interaction between these hyperparameters played an important role in the neural network’s performance. Different experiments were verified by running convolutional neural network algorithms to validate the proposal on the MNIST dataset. Notably, this research can help establish a universal multiparameter mathematical correlation model to guide the deep learning parameter adjustment process.

Deep learning (DL) is appearing as a powerful tool for spectral data modelling. In many cases, the DL models have been shown to outperform the classical chemometric spectral data modelling approaches. However, a main challenge with the DL models is the limited model interpretability as there are no scores and loadings generated in DL models. Scores and loadings are key parts of chemometric modelling as they facilitate the interpretation of the models. Furthermore, most of the time in the reported literature, the performance of DL models is compared with basic chemometric approaches with less attention being paid to optimizing these chemometric models. This study aims to test the hypothesis that proper chemometric modelling of spectral data can lead to performance equivalent to that of DL models while having all the useful information such as scores, loadings, and regression coefficients to support model interpretation. To test this, a case study is presented for the prediction of nitrogen content in rapeseed (Brassica napus L.) by Vis-NIR spectroscopy. On the classical chemometric side, two recently developed pre-processing fusion approaches, i.e. sequential pre-processing through orthogonalization (SPORT) and parallel pre-processing through orthogonalization (PORTO) were used. On the DL side, the previously published with DL modelling results for the same data set were used as the benchmark. Such a comparison was valid as the chemometric analysis was performed on the same calibration and test sets as used previously for the DL modelling. Results showed that the sequential and parallel learning approaches attained the same accuracy as that of the previously reported DL procedure on the same data set. The information related to scores, loading and regression coefficients could be used for model interpretation.

Improving soybean (Glycine max L. (Merr.)) yield is crucial for strengthening national food security. Predicting soybean yield is essential to maximize the potential of crop varieties. Non-destructive methods are needed to estimate yield before crop maturity. Various approaches, including the pod-count method, have been used to predict soybean yield, but they often face issues with the crop background color. To address this challenge, we explored the application of a depth camera to real-time filtering of RGB images, aiming to enhance the performance of the pod-counting classification model. Additionally, this study aimed to compare object detection models (YOLOV7 and YOLOv7-E6E) and select the most suitable deep learning (DL) model for counting soybean pods. After identifying the best architecture, we conducted a comparative analysis of the model's performance by training the DL model with and without background removal from images. Results demonstrated that removing the background using a depth camera improved YOLOv7's pod detection performance by 10.2% precision, 16.4% recall, 13.8% mAP@50, and 17.7% mAP@0.5:0.95 score compared to when the background was present. Using a depth camera and the YOLOv7 algorithm for pod detection and counting yielded a mAP@0.5 of 93.4% and mAP@0.5:0.95 of 83.9%. These results indicated a significant improvement in the DL model's performance when the background was segmented, and a reasonably larger dataset was used to train YOLOv7.

To develop a deep learning model for pressure injury stages classification based on real-world photographs and compare its performance with that of clinical nurses to seek the opportunity of its application in clinical settings. This was a retrospective observational study using a deep learning model. A plastic surgeon and two wound care nurses labelled a set of pressure injury images. We applied several modern Convolutional Neural Networks architectures and compared the performances with those of clinical nurses. We retrospectively analysed the electronic medical records of hospitalized patients between January 2019 and April 2021. A set of 2464 pressure injury images were compiled and analysed. Using EfficientNet, in classifying pressure injury images, the macro F1-score was calculated to be 0.8941, and the average performance of two experienced nurses was reported as 0.8781. A deep learning model for classifying pressure injury images by stages was successfully developed, and the performance of the model was compared with that of experienced nurses. The classification model developed in this study is expected to help less-experienced nurses or those working in under-resourced healthcare settings determine the stages of pressure injury. Our deep learning model can minimize discrepancies in nurses' assessment of classifying pressure injury stages. Follow-up studies on improving the performance of deep learning models using modern techniques and clinical usability will lead to improved quality of care among patients with pressure injury. Patients or the public were not involved in our research's design, conduct, reporting or dissemination plans because this was a retrospective study that used electronic medical records.

Pneumatization of turbinates, also known as concha bullosa (CB), is associated with nasal septal deviation and sinonasal pathologies. This study aims to evaluate the performance of deep learning models in detecting CB in coronal cone-beam computed tomography (CBCT) images. Standardized coronal images were obtained from 203 CBCT scans (83 with CB and 119 without CB) from the radiology archives of a dental teaching hospital. These scans underwent preprocessing through a hybridized contrast enhancement (CE) method using discrete wavelet transform (DWT). Of the 203 CBCT images, 162 were randomly assigned to the training set and 41 to the testing set. Initially, the images were enhanced using a CE technique before being input into pre-trained deep learning models, namely ResNet50, ResNet101, and MobileNet. The features extracted by each model were then flattened and input into a random forest (RF) classifier. In the subsequent phase, the CE technique was refined by incorporating DWT. CE-DWT-ResNet101-RF demonstrated the highest performance, achieving an accuracy of 91.7% and an area under the curve (AUC) of 98%. In contrast, CE-MobileNet-RF recorded the lowest accuracy at 82.46% and an AUC of 92%. The highest precision, recall, and F1 score (all 92%) were observed for CE-DWT-ResNet101-RF. Deep learning models demonstrated high accuracy in detecting CB in CBCT images. However, to confirm these results, further studies involving larger sample sizes and various deep learning models are required.