ADKU/ResearchGPT_model_dataset huggingface dataset:
Ultrasound-based deep learning radiomics nomogram for differentiating mass mastitis from invasive breast cancer
Authors: Linyong Wu, Songhua Li, Chaojun Wu, Shaofeng Wu, Yan Lin, Dayou Wei

BackgroundThe purpose of this study is to develop and validate the potential value of the deep learning radiomics nomogram (DLRN) based on ultrasound to differentiate mass mastitis (MM) and invasive breast cancer (IBC).Methods50 cases of MM and 180 cases of IBC with ultrasound Breast Imaging Reporting and Data System 4 category were recruited (training cohort, n = 161, validation cohort, n = 69). Based on PyRadiomics and ResNet50 extractors, radiomics and deep learning features were extracted, respectively. Based on supervised machine learning methods such as logistic regression, random forest, and support vector machine, as well as unsupervised machine learning methods using K-means clustering analysis, the differences in features between MM and IBC were analyzed to develop DLRN. The performance of DLRN had been evaluated by receiver operating characteristic curve, calibration, and clinical practicality.ResultsSupervised machine learning results showed that compared with radiomics models, especially random forest models, deep learning models were better at recognizing MM and IBC. The area under the curve (AUC) of the validation cohort was 0.84, the accuracy was 0.83, the sensitivity was 0.73, and the specificity was 0.83. Compared to radiomics or deep learning models, DLRN even further improved discrimination ability (AUC of 0.90 and 0.90, accuracy of 0.83 and 0.88 for training and validation cohorts), which had better clinical benefits and good calibratability. In addition, the information heterogeneity of deep learning features in MM and IBC was validated again through unsupervised machine learning clustering analysis, indicating that MM had a unique features phenotype.ConclusionThe DLRN developed based on radiomics and deep learning features of ultrasound images has potential clinical value in effectively distinguishing between MM and IBC. DLRN breaks through visual limitations and quantifies more image information related to MM based on computers, further utilizing machine learning to effectively utilize this information for clinical decision-making. As DLRN becomes an autonomous screening system, it will improve the recognition rate of MM in grassroots hospitals and reduce the possibility of incorrect treatment and overtreatment.
Two-layer Ensemble of Deep Learning Models for Medical Image Segmentation
Authors: Truong Dang, Tien Thanh Nguyen, John McCall, Eyad Elyan, Carlos Francisco Moreno-García

One of the most important areas in medical image analysis is segmentation, in which raw image data is partitioned into structured and meaningful regions to gain further insights. By using Deep Neural Networks (DNN), AI-based automated segmentation algorithms can potentially assist physicians with more effective imaging-based diagnoses. However, since it is difficult to acquire high-quality ground truths for medical images and DNN hyperparameters require significant manual tuning, the results by DNN-based medical models might be limited. A potential solution is to combine multiple DNN models using ensemble learning. We propose a two-layer ensemble of deep learning models in which the prediction of each training image pixel made by each model in the first layer is used as the augmented data of the training image for the second layer of the ensemble. The prediction of the second layer is then combined by using a weight-based scheme which is found by solving linear regression problems. To the best of our knowledge, our paper is the first work which proposes a two-layer ensemble of deep learning models with an augmented data technique in medical image segmentation. Experiments conducted on five different medical image datasets for diverse segmentation tasks show that proposed method achieves better results in terms of several performance metrics compared to some well-known benchmark algorithms. Our proposed two-layer ensemble of deep learning models for segmentation of medical images shows effectiveness compared to several benchmark algorithms. The research can be expanded in several directions like image classification.
A Diffusion Equation for Improving the Robustness of Deep Learning Speckle Removal Model
Authors: Li Cheng, Yuming Xing, Yao Li, Zhichang Guo

Speckle removal aims to smooth noise while preserving image boundaries and texture information. In recent years, speckle removal models based on deep learning methods have attracted a lot of attention. However, it was found that these models are less robust to adversarial attacks. The adversarial attack makes the image recovery of deep learning methods significantly less effective when the speckle noise distribution is almost unchanged. In purpose of addressing the above problem, we propose a diffusion equation-based speckle removal model that can improve the robustness of deep learning algorithms in this paper. The model utilizes a deep learning image prior and an image grayscale detection operator together to construct the coefficient function of the diffusion equation. Among them, there is a high possibility that the deep learning image prior is inaccurate or even incorrect, but it will not affect the performance and the properties of the proposed diffusion equation model for noise removal. Moreover, we analyze the robustness of the proposed diffusion equation model in terms of theoretical and numerical properties. Experiments show that our proposed diffusion equation speckle removal model is not affected by adversarial attacks in any way and has stronger robustness.
Application of deep learning technique in next generation sequence experiments
Authors: Su Özgür, Mehmet Orman

In recent years, the widespread utilization of biological data processing technology has been driven by its cost-effectiveness. Consequently, next-generation sequencing (NGS) has become an integral component of biological research. NGS technologies enable the sequencing of billions of nucleotides in the entire genome, transcriptome, or specific target regions. This sequencing generates vast data matrices. Consequently, there is a growing demand for deep learning (DL) approaches, which employ multilayer artificial neural networks and systems capable of extracting meaningful information from these extensive data structures. In this study, the aim was to obtain optimized parameters and assess the prediction performance of deep learning and machine learning (ML) algorithms for binary classification in real and simulated whole genome data using a cloud-based system. The ART-simulated data and paired-end NGS (whole genome) data of Ch22, which includes ethnicity information, were evaluated using XGBoost, LightGBM, and DL algorithms. When the learning rate was set to 0.01 and 0.001, and the epoch values were updated to 500, 1000, and 2000 in the deep learning model for the ART simulated dataset, the median accuracy values of the ART models were as follows: 0.6320, 0.6800, and 0.7340 for epoch 0.01; and 0.6920, 0.7220, and 0.8020 for epoch 0.001, respectively. In comparison, the median accuracy values of the XGBoost and LightGBM models were 0.6990 and 0.6250 respectively. When the same process is repeated for Chr 22, the results are as follows: the median accuracy values of the DL models were 0.5290, 0.5420 and 0.5820 for epoch 0.01; and 0.5510, 0.5830 and 0.6040 for epoch 0.001, respectively. Additionally, the median accuracy values of the XGBoost and LightGBM models were 0.5760 and 0.5250, respectively. While the best classification estimates were obtained at 2000 epochs and a learning rate (LR) value of 0.001 for both real and simulated data, the XGBoost algorithm showed higher performance when the epoch value was 500 and the LR was 0.01. When dealing with class imbalance, the DL algorithm yielded similar and high Recall and Precision values. Conclusively, this study serves as a timely resource for genomic scientists, providing guidance on why, when, and how to effectively utilize deep learning/machine learning methods for the analysis of human genomic data.
Unsupervised deep learning for geometric feature detection and multilevel-multimodal image registration
Authors: Mohamed Lajili, Zakaria Belhachmi, Maher Moakher, Anis Theljani

Medical image registration is a crucial step in computer-assisted medical diagnosis, and has seen significant progress with the adoption of deep learning methods like convolutional neural networks (CNN). Creating a deep learning network for image registration is complex because humans can’t easily prepare or supervise the training data unless it’s very basic. This article presents an innovative approach to unsupervised deep learning-based multilevel image registration approach. We propose to develop a CNN to detect the geometric features, such as edges and thin structures, from images using a loss function derived from the Blake-Zisserman energy. This method enables the detection of discontinuities at different scales without relying on labeled data. Subsequently, we use this geometric information extracted from the input images, to define a second loss function and to perform our multimodal image registration process. Furthermore, we introduce a novel deep neural network architecture for multilevel image registration, offering enhanced precision and efficiency compared to traditional methods. Numerical simulations are employed to demonstrate the accuracy and relevance of our approach. We perform some numerical simulations to show the accuracy and the relevance of our approach for multimodal registration and its multilevel implementation.
Generative deep learning for data generation in natural hazard analysis: motivations, advances, challenges, and opportunities
Authors: Zhengjing Ma, Gang Mei, Nengxiong Xu

Data mining and analysis are critical for preventing or mitigating natural hazards. However, data availability in natural hazard analysis is experiencing unprecedented challenges due to economic, technical, and environmental constraints. Recently, generative deep learning has become an increasingly attractive solution to these challenges, which can augment, impute, or synthesize data based on these learned complex, high-dimensional probability distributions of data. Over the last several years, much research has demonstrated the remarkable capabilities of generative deep learning for addressing data-related problems in natural hazards analysis. Data processed by deep generative models can be utilized to describe the evolution or occurrence of natural hazards and contribute to subsequent natural hazard modeling. Here we present a comprehensive review concerning generative deep learning for data generation in natural hazard analysis. (1) We summarized the limitations associated with data availability in natural hazards analysis and identified the fundamental motivations for employing generative deep learning as a critical response to these challenges. (2) We discuss several deep generative models that have been applied to overcome the problems caused by limited data availability in natural hazards analysis. (3) We analyze advances in utilizing generative deep learning for data generation in natural hazard analysis. (4) We discuss challenges associated with leveraging generative deep learning in natural hazard analysis. (5) We explore further opportunities for leveraging generative deep learning in natural hazard analysis. This comprehensive review provides a detailed roadmap for scholars interested in applying generative models for data generation in natural hazard analysis.
A Novel Discrete Deep Learning–Based Cancer Classification Methodology
Authors: Marzieh Soltani, Mehdi Khashei, Negar Bakhtiarvand

Classification is one of the most well-known data mining branches used in diverse domains and fields. In the literature, many different classification techniques, such as statistical/intelligent, linear/nonlinear, fuzzy/crisp, shallow/deep, and single/hybrid, have been developed to cover data and systems with different characteristics. Intelligent classification approaches, especially deep learning classifiers, due to their unique features to provide accurate and efficient results, have recently attracted a lot of attention. However, in the learning process of the intelligent classifiers, a continuous distance-based cost function is used to estimate the connection weights, though the goal function in classification problems is discrete and using a continuous cost function in their learning process is unreasonable and inefficient. In this paper, a novel discrete learning–based methodology is proposed to estimate the connection weights of intelligent classifiers more accurately. In the proposed learning process, they are discretely adjusted and at once jumped to the target. This is in contrast to conventional continuous learning algorithms in which the connection weights are continuously adjusted and step by step near the target. In the present research, the proposed methodology is exemplarily applied to the deep neural network (DNN), which is one of the most recognized deep classification approaches, with a solid mathematical foundation and strong practical results in complex problems. Although the proposed methodology is just implemented on the DNN, it is a general methodology that can be similarly applied to other shallow and deep intelligent classification models. It can be generally demonstrated that the performance of the proposed discrete learning–based DNN (DIDNN) model, due to its consistency property, will not be worse than the conventional ones. The proposed DIDNN model is exemplarily evaluated on some well-known cancer classification benchmarks to illustrate the efficiency of the proposed model. The empirical results indicate that the proposed model outperforms the conventional versions of the selected deep approach in all data sets. Based on the performance analysis, the DIDNN model can improve the performance of the classic version by approximately 3.39%. Therefore, using this technique is an appropriate and effective alternative to conventional DNN-based models for classification purposes.
Deep reinforcement learning for inverting earthquake focal mechanism and its potential application to marine earthquakes
Authors: Wenhuan Kuang, Zhihui Zou, Junhui Xing, Wei Wei

Earthquake data are one of the key means by which to explore our planet. At a large scale, the layered structure of the Earth is revealed by the seismic waves of natural earthquakes that go deep into its inner core. At a local scale, seismology for exploration has successfully been employed to discover massive fossil energies. As the volume of recorded seismic data becomes greater, intelligent methods for processing such a volume of data are eagerly anticipated. In particular, earthquake focal mechanisms are important for assessing the severity of tsunamis, characterizing seismogenic faults, and investigating the stress perturbations that follow a major earthquake. Here, we report a novel deep reinforcement learning method for inverting the earthquake focal mechanism. Unlike more typical deep learning applications, which require a large training dataset, a deep reinforcement learning system learns by itself. We demonstrate the validity and efficacy of the proposed deep reinforcement learning method by applying it to the Mw 7.1 mainshock of the Ridgecrest earthquakes in southern California. In the foreseeable future, deep learning technologies may greatly contribute to our understanding of the oceanographic process. The proposed method may help us understand the mechanism of marine earthquakes.
Advancements in hybrid approaches for brain tumor segmentation in MRI: a comprehensive review of machine learning and deep learning techniques
Authors: Ravikumar Sajjanar, Umesh D. Dixit, Vittalkumar K Vagga

Magnetic resonance imaging (MRI) brain tumour segmentation is essential for the diagnosis, planning, and follow-up of patients with brain tumours. In an effort to increase efficiency and accuracy, a number of machine learning and deep learning algorithms have been developed over time to automate the segmentation process. Hybrid strategies, which include the advantages of both machine learning and deep learning, have become more and more popular as viable options. This in-depth analysis covers the developments in hybrid techniques for MRI segmentation of brain tumours. The essential ideas of machine learning and deep learning approaches are then covered, with an emphasis on their individual advantages and disadvantages. After that, the review explores the numerous hybrid strategies put out in the literature. In hybrid approaches, various phases of the segmentation pipeline are combined with machine learning and deep learning techniques. Pre-processing, feature extraction, and post-processing are examples of these phases. The paper examines at various combinations of methods utilised at these phases, such as segmentation using deep learning models and feature extraction utilising conventional machine learning algorithms. The implementation of ensemble approaches, which integrate forecasts from various models to improve segmentation accuracy, is also explored. The research study also examines the properties of freely accessible brain tumour datasets, which are essential for developing and testing hybrid models. To address the difficulties of generalisation and robustness in brain tumour segmentation, it emphasises the necessity of vast, varied, and annotated datasets. Additionally, by contrasting them with conventional machine learning and deep learning techniques, the review analyses the effectiveness of hybrid approaches reported in the literature. This comprehensive research provides information on recent advancements in hybrid techniques for MRI segmenting brain tumours. It emphasises the potential for merging deep learning and machine learning methods to enhance the precision and effectiveness of brain tumour segmentation, ultimately assisting in improving patient diagnosis and treatment planning.
Analysis of English Classroom Teaching Behavior Mode in Environmental Protection Field Based on Deep Learning
Authors: Yuanyuan Li

Learning is to use algorithms to enable machines to learn rules from a large amount of historical data, so as to intelligently identify new samples or predict the future. Deep learning can promote students’ understanding of knowledge, conduct in-depth processing of new knowledge, integrate it with the original knowledge, and apply it to new situations, solve intelligent audio–visual listening from the perspective of deep learning, and focus on cultivating students’ in-depth learning ability and individual differences in innovative thinking. As the main position of ecological education, schools should effectively strengthen the publicity and education of ecological ideas and low-carbon concepts, and integrate them into education and teaching to effectively improve students’ awareness of environmental protection. This study aims to explore the effectiveness of flipped classroom teaching model based on deep learning. Therefore, from the perspective of deep learning, this paper combs the theory of deep learning, constructs a new model of smart classroom, and provides ideas and directions for model reform. In this study, the flipped classroom teaching model based on deep learning was applied to English teaching, and an 8-week teaching experiment was conducted. In addition, this paper believes that it is of great practical significance to carry out environmental protection education with the help of English teaching.
Diagnosis of EV Gearbox Bearing Fault Using Deep Learning-Based Signal Processing
Authors: Kicheol Jeong, Chulwoo Moon

The gearbox of an electric vehicle operates under the high load torque and axial load of electric vehicles. In particular, the bearings that support the shaft of the gearbox are subjected to several tons of axial load, and as the mileage increases, fault occurs on bearing rolling elements frequently. Such bearing fault has a serious impact on driving comfort and vehicle safety, however, bearing faults are diagnosed by human experts nowadays, and algorithm-based electric vehicle bearing fault diagnosis has not been implemented. Therefore, in this paper, a deep learning-based bearing vibration signal processing method to diagnose bearing fault in electric vehicle gearboxes is proposed. The proposed method consists of a deep neural network learning stage and an application stage of the pre-trained neural network. In the deep neural network learning stage, supervised learning is carried out based on two acceleration sensors. In the neural network application stage, signal processing of a single accelerometer signal is performed through a pre-trained neural network. In conclusion, the pre-trained neural network makes bearing fault signals stand out and can utilize these signals to extract frequency characteristics of bearing fault.
Deep Q-learning with hybrid quantum neural network on solving maze problems
Authors: Hao-Yuan Chen, Yen-Jui Chang, Shih-Wei Liao, Ching-Ray Chang

Quantum computing holds great potential for advancing the limitations of machine learning algorithms to handle higher dimensions of data and reduce overall training parameters in deep learning (DL) models. This study uses a trainable variational quantum circuit (VQC) on a gate-based quantum computing model to investigate the potential for quantum benefit in a model-free reinforcement learning problem. Through a comprehensive investigation and evaluation of the current model and capabilities of quantum computers, we designed and trained a novel hybrid quantum neural network based on the latest Qiskit and PyTorch framework. We compared its performance with a full-classical CNN with and without an incorporated VQC. Our research provides insights into the potential of deep quantum learning to solve a maze problem and, potentially, other reinforcement learning problems. We conclude that reinforcement learning problems can be practical with reasonable training epochs. Moreover, a comparative study of full-classical and hybrid quantum neural networks is discussed to understand these two approaches’ performance, advantages, and disadvantages to deep Q-learning problems, especially on larger-scale maze problems larger than 4\(\times \)4.