ADKU/ResearchGPT_model_dataset huggingface dataset:
Comparative study and analysis on skin cancer detection using machine learning and deep learning algorithms
Authors: V. Auxilia Osvin Nancy, P. Prabhavathy, Meenakshi S. Arya, B. Shamreen Ahamed

Exposure to UV rays due to global warming can lead to sunburn and skin damage, ultimately resulting in skin cancer. Early prediction of this type of cancer is crucial. A detailed review in this paper explores various algorithms, including machine learning (ML) techniques as well as deep learning (DL) techniques. While deep learning strategies, particularly CNNs, are commonly employed for skin cancer identification and classification, there is also some usage of machine learning and hybrid approaches. These techniques have proven to be effective classifiers of skin lesions, offering promising results for early detection. The paper analyzes various researchers’ reviews on skin cancer diagnosis to identify a suitable methodology for improving diagnostic accuracy. A publicly available dataset of dermoscopic images retrieved from the ISIC archive has been trained and evaluated. Performance analysis is done, considering metrics such as test and validation accuracy. The results indicate that the RF(random forest) algorithm outperforms other machine learning algorithms in both scenarios, with accuracies of 58.57% without augmentation and 87.32% with augmentation. MobileNetv2, ensemble of Dense Net and Inceptionv3 exhibit superior performance. During training without augmentation, MobileNetv2 achieves an accuracy of 88.81%, while the ensemble model achieves an accuracy of 88.80%. With augmentation techniques applied, the accuracies improved to 97.58% and 97.50%, respectively. Furthermore, experiment with a customized convolutional neural network (CNN) model was also conducted, varying the number of layers and applying various hyperparameter tuning methodologies. Suitable architectures, including a CNN with 7 layers and batch normalization, a CNN with 5 layers, and a CNN with 3 layers were identified. These models achieved accuracies of 77.92%, 97.72%, and 98.02% on the raw data and augmentation datasets, respectively. The experimental results suggest that these techniques hold promise for integration into clinical settings, and further research and validation are necessary. The results highlight the effectiveness of transfer learning models, in achieving high accuracy rates. The findings support the future adoption of these techniques in clinical practice, pending further research and validation.
Interdisciplinary approach to identify language markers for post-traumatic stress disorder using machine learning and deep learning
Authors: Robin Quillivic, Frédérique Gayraud, Yann Auxéméry, Laurent Vanni, Denis Peschanski, Francis Eustache, Jacques Dayan, Salma Mesmoudi

Post-traumatic stress disorder (PTSD) lacks clear biomarkers in clinical practice. Language as a potential diagnostic biomarker for PTSD is investigated in this study. We analyze an original cohort of 148 individuals exposed to the November 13, 2015, terrorist attacks in Paris. The interviews, conducted 5–11 months after the event, include individuals from similar socioeconomic backgrounds exposed to the same incident, responding to identical questions and using uniform PTSD measures. Using this dataset to collect nuanced insights that might be clinically relevant, we propose a three-step interdisciplinary methodology that integrates expertise from psychiatry, linguistics, and the Natural Language Processing (NLP) community to examine the relationship between language and PTSD. The first step assesses a clinical psychiatrist's ability to diagnose PTSD using interview transcription alone. The second step uses statistical analysis and machine learning models to create language features based on psycholinguistic hypotheses and evaluate their predictive strength. The third step is the application of a hypothesis-free deep learning approach to the classification of PTSD in our cohort. Results show that the clinical psychiatrist achieved a diagnosis of PTSD with an AUC of 0.72. This is comparable to a gold standard questionnaire (Area Under Curve (AUC) ≈ 0.80). The machine learning model achieved a diagnostic AUC of 0.69. The deep learning approach achieved an AUC of 0.64. An examination of model error informs our discussion. Importantly, the study controls for confounding factors, establishes associations between language and DSM-5 subsymptoms, and integrates automated methods with qualitative analysis. This study provides a direct and methodologically robust description of the relationship between PTSD and language. Our work lays the groundwork for advancing early and accurate diagnosis and using linguistic markers to assess the effectiveness of pharmacological treatments and psychotherapies.
Study of Q-learning and deep Q-network learning control for a rotary inverted pendulum system
Authors: Zied Ben Hazem

The rotary inverted pendulum system (RIPS) is an underactuated mechanical system with highly nonlinear dynamics and it is difficult to control a RIPS using the classic control models. In the last few years, reinforcement learning (RL) has become a popular nonlinear control method. RL has a powerful potential to control systems with high non-linearity and complex dynamics, such as RIPS. Nevertheless, RL control for RIPS has not been well studied and there is limited research on the development and evaluation of this control method. In this paper, RL control algorithms are developed for the swing-up and stabilization control of a single-link rotary inverted pendulum (SLRIP) and compared with classic control methods such as PID and LQR. A physical model of the SLRIP system is created using the MATLAB/Simscape Toolbox, the model is used as a dynamic simulation in MATLAB/Simulink to train the RL agents. An agent trainer system with Q-learning (QL) and deep Q-network learning (DQNL) is proposed for the data training. Furthermore, agent actions are actuating the horizontal arm of the system and states are the angles and velocities of the pendulum and the horizontal arm. The reward is computed according to the angles of the pendulum and horizontal arm. The reward is zero when the pendulum attends the upright position. The RL algorithms are used without a deep understanding of the classical controllers and are used to implement the agent. Finally, the outcome indicates the effectiveness of the QL and DQNL algorithms compared to the conventional PID and LQR controllers.
Air combat maneuver decision based on deep reinforcement learning with auxiliary reward
Authors: Tingyu Zhang, Yongshuai Wang, Mingwei Sun, Zengqiang Chen

For air combat maneuvering decision, the sparse reward during the application of deep reinforcement learning limits the exploration efficiency of the agents. To address this challenge, we propose an auxiliary reward function considering the impact of angle, range, and altitude. Furthermore, we investigate the influences of the network nodes, layers, and the learning rate on decision system, and reasonable parameter ranges are provided, which can serve as a guideline. Finally, four typical air combat scenarios demonstrate good adaptability and effectiveness of the proposed scheme, and the auxiliary reward significantly improves the learning ability of deep Q network (DQN) by leading the agents to explore more intently. Compared with the original deep deterministic policy gradient and soft actor critic algorithm, the proposed method exhibits superior exploration capability with higher reward, indicating that the trained agent can adapt to different air combats with good performance.
Deep learning prediction of renal anomalies for prenatal ultrasound diagnosis
Authors: Olivier X. Miguel, Emily Kaczmarek, Inok Lee, Robin Ducharme, Alysha L. J. Dingwall-Harvey, Ruth Rennicks White, Brigitte Bonin, Richard I. Aviv, Steven Hawken, Christine M. Armour, Kevin Dick, Mark C. Walker

Deep learning algorithms have demonstrated remarkable potential in clinical diagnostics, particularly in the field of medical imaging. In this study, we investigated the application of deep learning models in early detection of fetal kidney anomalies. To provide an enhanced interpretation of those models’ predictions, we proposed an adapted two-class representation and developed a multi-class model interpretation approach for problems with more than two labels and variable hierarchical grouping of labels. Additionally, we employed the explainable AI (XAI) visualization tools Grad-CAM and HiResCAM, to gain insights into model predictions and identify reasons for misclassifications. The study dataset consisted of 969 ultrasound images from unique patients; 646 control images and 323 cases of kidney anomalies, including 259 cases of unilateral urinary tract dilation and 64 cases of unilateral multicystic dysplastic kidney. The best performing model achieved a cross-validated area under the ROC curve of 91.28% ± 0.52%, with an overall accuracy of 84.03% ± 0.76%, sensitivity of 77.39% ± 1.99%, and specificity of 87.35% ± 1.28%. Our findings emphasize the potential of deep learning models in predicting kidney anomalies from limited prenatal ultrasound imagery. The proposed adaptations in model representation and interpretation represent a novel solution to multi-class prediction problems.
A review of deep learning and Generative Adversarial Networks applications in medical image analysis
Authors: D. N. Sindhura, Radhika M. Pai, Shyamasunder N. Bhat, Manohara M. M. Pai

Nowadays, computer-aided decision support systems (CADs) for the analysis of images have been a perennial technique in the medical imaging field. In CADs, deep learning algorithms are widely used to perform tasks like classification, identification of patterns, detection, etc. Deep learning models learn feature representations from images rather than handcrafted features. Hence, deep learning models are quickly becoming the state-of-the-art method to achieve good performances in different computer-aided decision-support systems in medical applications. Similarly, deep learning-based generative models called Generative Adversarial Networks (GANs) have recently been developed as a novel method to produce realistic-looking synthetic data. GANs are used in different domains, including medical imaging generation. The common problems, like class imbalance and a small dataset, in healthcare are well addressed by GANs, and it is a leading area of research. Segmentation, reconstruction, detection, denoising, registration, etc. are the important applications of GANs. So in this work, the successes of deep learning methods in segmentation, classification, cell structure and fracture detection, computer-aided identification, and GANs in synthetic medical image generation, segmentation, reconstruction, detection, denoising, and registration in recent times are reviewed. Lately, the review article concludes by raising research directions for DL models and GANs in medical applications.
Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images
Authors: Juhwan Lee, Justin N. Kim, Luis A. P. Dallan, Vladislav N. Zimin, Ammar Hoori, Neda S. Hassani, Mohamed H. E. Makhlouf, Giulio Guagliumi, Hiram G. Bezerra, David L. Wilson

Thin-cap fibroatheroma (TCFA) is a prominent risk factor for plaque rupture. Intravascular optical coherence tomography (IVOCT) enables identification of fibrous cap (FC), measurement of FC thicknesses, and assessment of plaque vulnerability. We developed a fully-automated deep learning method for FC segmentation. This study included 32,531 images across 227 pullbacks from two registries (TRANSFORM-OCT and UHCMC). Images were semi-automatically labeled using our OCTOPUS with expert editing using established guidelines. We employed preprocessing including guidewire shadow detection, lumen segmentation, pixel-shifting, and Gaussian filtering on raw IVOCT (r,θ) images. Data were augmented in a natural way by changing θ in spiral acquisitions and by changing intensity and noise values. We used a modified SegResNet and comparison networks to segment FCs. We employed transfer learning from our existing much larger, fully-labeled calcification IVOCT dataset to reduce deep-learning training. Postprocessing with a morphological operation enhanced segmentation performance. Overall, our method consistently delivered better FC segmentation results (Dice: 0.837 ± 0.012) than other deep-learning methods. Transfer learning reduced training time by 84% and reduced the need for more training samples. Our method showed a high level of generalizability, evidenced by highly-consistent segmentations across five-fold cross-validation (sensitivity: 85.0 ± 0.3%, Dice: 0.846 ± 0.011) and the held-out test (sensitivity: 84.9%, Dice: 0.816) sets. In addition, we found excellent agreement of FC thickness with ground truth (2.95 ± 20.73 µm), giving clinically insignificant bias. There was excellent reproducibility in pre- and post-stenting pullbacks (average FC angle: 200.9 ± 128.0°/202.0 ± 121.1°). Our fully automated, deep-learning FC segmentation method demonstrated excellent performance, generalizability, and reproducibility on multi-center datasets. It will be useful for multiple research purposes and potentially for planning stent deployments that avoid placing a stent edge over an FC.
Deep palmprint recognition algorithm based on self-supervised learning and uncertainty loss
Authors: Rui Fan, Xiaohong Han

With the rapid development of deep learning technology, an increasing number of people are adopting palmprint recognition algorithms based on deep learning for identity authentication. However, these algorithms are susceptible to factors such as palm placement, light source, and insufficient data sampling, resulting in poor recognition accuracy. To address these issues, this paper proposes a new end-to-end deep palmprint recognition algorithm (SSLAUL), which introduces self-supervised representation learning based on contextual prediction, utilizing unlabeled palmprint data for pre-training before introducing the trained parameters into the downstream model for fine-tuning. An uncertainty loss function is introduced into the downstream model, using the homoskedastic uncertainty as a benchmark to do adaptive weight adjustment for different loss functions dynamically. Channel and spatial attention mechanisms are also introduced to extract highly discriminative local features. In this paper, the algorithm is validated on publicly available IITD, CASIA, and PolyU palmprint datasets. The method always achieves the best recognition performance compared to other state-of-the-art algorithms.
Challenges and practices of deep learning model reengineering: A case study on computer vision
Authors: Wenxin Jiang, Vishnu Banna, Naveen Vivek, Abhinav Goel, Nicholas Synovic, George K. Thiruvathukal, James C. Davis

ContextMany engineering organizations are reimplementing and extending deep neural networks from the research community. We describe this process as deep learning model reengineering. Deep learning model reengineering — reusing, replicating, adapting, and enhancing state-of-the-art deep learning approaches — is challenging for reasons including under-documented reference models, changing requirements, and the cost of implementation and testing.ObjectivePrior work has characterized the challenges of deep learning model development, but as yet we know little about the deep learning model reengineering process and its common challenges. Prior work has examined DL systems from a “product” view, examining defects from projects regardless of the engineers’ purpose. Our study is focused on reengineering activities from a “process” view, and focuses on engineers specifically engaged in the reengineering process.MethodOur goal is to understand the characteristics and challenges of deep learning model reengineering. We conducted a mixed-methods case study of this phenomenon, focusing on the context of computer vision. Our results draw from two data sources: defects reported in open-source reeengineering projects, and interviews conducted with practitioners and the leaders of a reengineering team. From the defect data source, we analyzed 348 defects from 27 open-source deep learning projects. Meanwhile, our reengineering team replicated 7 deep learning models over two years; we interviewed 2 open-source contributors, 4 practitioners, and 6 reengineering team leaders to understand their experiences.ResultsOur results describe how deep learning-based computer vision techniques are reengineered, quantitatively analyze the distribution of defects in this process, and qualitatively discuss challenges and practices. We found that most defects (58%) are reported by re-users, and that reproducibility-related defects tend to be discovered during training (68% of them are). Our analysis shows that most environment defects (88%) are interface defects, and most environment defects (46%) are caused by API defects. We found that training defects have diverse symptoms and root causes. We identified four main challenges in the DL reengineering process: model operationalization, performance debugging, portability of DL operations, and customized data pipeline. Integrating our quantitative and qualitative data, we propose a novel reengineering workflow.ConclusionsOur findings inform several conclusion, including: standardizing model reengineering practices, developing validation tools to support model reengineering, automated support beyond manual model reengineering, and measuring additional unknown aspects of model reengineering.
Hybrid deep learning approach to improve classification of low-volume high-dimensional data
Authors: Pegah Mavaie, Lawrence Holder, Michael K. Skinner

BackgroundThe performance of machine learning classification methods relies heavily on the choice of features. In many domains, feature generation can be labor-intensive and require domain knowledge, and feature selection methods do not scale well in high-dimensional datasets. Deep learning has shown success in feature generation but requires large datasets to achieve high classification accuracy. Biology domains typically exhibit these challenges with numerous handcrafted features (high-dimensional) and small amounts of training data (low volume).MethodA hybrid learning approach is proposed that first trains a deep network on the training data, extracts features from the deep network, and then uses these features to re-express the data for input to a non-deep learning method, which is trained to perform the final classification.ResultsThe approach is systematically evaluated to determine the best layer of the deep learning network from which to extract features and the threshold on training data volume that prefers this approach. Results from several domains show that this hybrid approach outperforms standalone deep and non-deep learning methods, especially on low-volume, high-dimensional datasets. The diverse collection of datasets further supports the robustness of the approach across different domains.ConclusionsThe hybrid approach combines the strengths of deep and non-deep learning paradigms to achieve high performance on high-dimensional, low volume learning tasks that are typical in biology domains.
Deep learning-based classification and application test of multiple crop leaf diseases using transfer learning and the attention mechanism
Authors: Yifu Zhang, Qian Sun, Ji Chen, Huini Zhou

Crop diseases are among the major natural disasters in agricultural production that seriously restrict the growth and development of crops, threatening food security. Timely classification, accurate identification, and the application of methods suitable for the situation can effectively prevent and control crop diseases, improving the quality of agricultural products. Considering the huge variety of crops, diseases, and differences in the characteristics of diseases during each stage, the current convolutional neural network models based on deep learning need to meet the higher requirement of classifying crop diseases accurately. It is necessary to introduce a new architecture scheme to improve the recognition effect. Therefore, in this study, we optimized the deep learning-based classification model for multiple crop leaf diseases using combined transfer learning and the attention mechanism, the modified model was deployed in the smartphone for testing. Dataset that containing 10 types of crops, 61 types of diseases, and different degrees was established, the algorithm structure based on ResNet50 was designed using transfer learning and the SE attention mechanism. The classification performances of different improvement methods were compared by model training. Result indicates that the average accuracy of the proposed TL-SE-ResNet50 model is increased by 7.7%, reaching 96.32%. The model was also integrated and implemented in the smartphone and the test result of the application reaches 94.8%, and the average response time is 882 ms. The improved model proposed has a good effect on the identification of diseases and their condition of multiple crops, and the application can meet the portable usage needs of farmers. This study can provide reference for more crop disease management research in agricultural production.
Sparse subspace clustering incorporated deep convolutional transform learning for hyperspectral band selection
Authors: Anurag Goel, Angshul Majumdar

This work delves into a research area with a limited number of studies, that of convolutional filter-learning based clustering. Since clustering is an unsupervised formulation, one cannot formulate it around a conventional convolutional neural network since the latter is inherently supervised. This work, therefore, builds upon the recently developed framework of deep convolutional transform learning. The sparse subspace clustering formulation is embedded into deep convolutional transform learning to get an end-to-end clustering architecture. The proposed approach has been applied to the problem of hyperspectral band selection. Comparison with a variety of state-of-the-art techniques in this domain shows that our proposed approach improves over them in terms of the classification metrics.