Xception Architecture Research Articles

Real time distracted driver detection using Xception architecture and Raspberry Pi

Researchers are concentrating on developing technologies to identify and caution drivers against driving while distracted because it is a major cause of traffic accidents. According to the National Highway Traffic Safety Administrator's report, distracted driving is to blame for roughly one in every five car accidents.Our goal is to create an accurate and dependable method for identifying distracted drivers and alerting them to their lack of focus. We take inspiration from the success of convolutional neural networks in computer vision to do this. Our strategy entails putting in place a CNN-based system that can recognize when a driver is distracted as well as pinpoint the precise cause of their preoccupation. Real-time detection, however, necessitates three apparently mutually exclusive requirements for an optimal network: a small number of parameters, high accuracy, and fast speed.

Transliterating Javanese Script Images to Roman Script using Convolutional Neural Network with Transfer Learning

The Javanese script holds immense cultural significance within Indonesia despite its diminishing usage in contemporary contexts. Its presence remains notable in specific regions of Java and remains integral to many historical documents and texts. Consequently, there is an urgent need for a transliteration system adept at converting Javanese script into contemporary scripts like Roman or Indonesian, thereby contributing to preserving Java's linguistic and cultural legacy. However, reading or transliterating Javanese script can be time-consuming, especially for longer texts, presenting considerable challenges for non-native readers. This study aims to develop an effective transliteration system for converting Javanese script into Roman script. This system addresses the pressing need to preserve Java's linguistic and cultural heritage by facilitating the readability and accessibility of Javanese script, especially for non-native readers. This study introduces an Optical Character Recognition (OCR) system tailored to identify Javanese script characters and transcribe them into Roman characters, explicitly focusing on fundamental nglegena and sandhangan swara characters. Individual characters are isolated by leveraging horizontal and vertical projection techniques, facilitating subsequent classification using a Convolutional Neural Network (CNN) employing transfer learning methodologies. The system's achievement of an impressive average similarity score of 90.78% is noteworthy, with the Xception architecture demonstrating superior efficiency in transliteration tasks. Implementing such a system harbors significant promise in safeguarding the Javanese script and enhancing its accessibility to a broader audience. This research contributes substantially to preserving and propagating Indonesia's rich cultural and linguistic heritage amidst the digital age.

Assessment of a Deep-Learning System for Colorectal Cancer Diagnosis Using Histopathology Images

Colorectal Cancer is one of the most common and lethal forms of cancer hence, an early and accurate detection is crucial. Traditional manual diagnosis is a tedious and time-consuming job susceptible to human errors; therefore, it is imperative to use computer-aided detection systems to interpret medical images for a quicker and more accurate diagnosis. In recent years deep-learning approaches have proved to be efficacious in predicting cancer from pathological images. This study assesses several deep-learning techniques for cancer diagnosis on digitized histopathology images, amongst which GoogLeNet and Xception emerged as the most effective, with GoogLeNet exhibiting slightly better precision in identifying cancerous tissues. Building on these findings the study proposes a new model (Xception+) by borrowing the idea from Xception architecture, which outperforms existing architectures with an accuracy of 99.37% for cancer diagnosis and 94.48% for cancer-grade classification. The primary inference of our research is assisting pathologists in detecting colorectal cancer from pathological images faster and more accurately. With notable accuracy and robustness, our proposed model has significant potential to analyze pathological images and detect the patterns associated with other types of cancer. Our study holds promise for driving the advancement of innovative medical diagnostic tools, aiding pathologists and medical practitioners in expediting cancer diagnosis processes.

RS-Xception: A Lightweight Network for Facial Expression Recognition

Facial expression recognition (FER) utilizes artificial intelligence for the detection and analysis of human faces, with significant applications across various scenarios. Our objective is to deploy the facial emotion recognition network on mobile devices and extend its application to diverse areas, including classroom effect monitoring, human–computer interaction, specialized training for athletes (such as in figure skating and rhythmic gymnastics), and actor emotion training. Recent studies have employed advanced deep learning models to address this task, though these models often encounter challenges like subpar performance and an excessive number of parameters that do not align with the requirements of FER for embedded devices. To tackle this issue, we have devised a lightweight network structure named RS-Xception, which is straightforward yet highly effective. Drawing on the strengths of ResNet and SENet, this network integrates elements from the Xception architecture. Our models have been trained on FER2013 datasets and demonstrate superior efficiency compared to conventional network models. Furthermore, we have assessed the model’s performance on the CK+, FER2013, and Bigfer2013 datasets, achieving accuracy rates of 97.13%, 69.02%, and 72.06%, respectively. Evaluation on the complex RAF-DB dataset yielded an accuracy rate of 82.98%. The incorporation of transfer learning notably enhanced the model’s accuracy, with a performance of 75.38% on the Bigfer2013 dataset, underscoring its significance in our research. In conclusion, our proposed model proves to be a viable solution for precise sentiment detection and estimation. In the future, our lightweight model may be deployed on embedded devices for research purposes.

Improved diabetic retinopathy severity classification using squeeze-and-excitation and sparse light weight multi-level attention u-net with transfer learning from xception.

Diabetic Retinopathy (DR) is a significant cause of vision loss in diabetic patients, making early detection and accurate severity classification essential for effective management and prevention. This study aims to develop an enhanced DR severity classification approach using advanced model architectures and transfer learning to improve diagnostic accuracy and support better patient care. We propose a novel model, Xception Squeeze-and-Excitation Sparse Lightweight Multi-Level Attention U-Net (XceSE_SparseLwMLA-UNet), designed to classify DR severity using fundus images from the Messidor 1 and Messidor 2 datasets. The XceSE_SparseLwMLA-UNet integrates several advanced mechanisms: the Squeeze-and-Excitation (SE) mechanism for adaptive feature recalibration, the Sparse Lightweight Multi-Level Attention (SparseLwMLA) mechanism for effective contextual information integration, and transfer learning from the Xception architecture to enhance feature extraction capabilities. The SE mechanism refines channel-wise feature responses, while SparseLwMLA enhances the model's ability to identify complex DR patterns. Transfer learning utilizes pre-trained weights from Xception to improve generalization across DR severity levels. The proposed XceSE_SparseLwMLA-UNet model demonstrates superior performance in DR severity classification, achieving higher accuracy and improved multi-class F1 scores compared to existing models. The model's color-coded segmentation outputs offer interpretable visual representations, aiding medical professionals in assessing DR severity levels. The XceSE_SparseLwMLA-UNet model shows promise for advancing early DR diagnosis and management by enhancing classification accuracy and providing valuable visual insights. Its integration of advanced architectural features and transfer learning contributes to better patient care and improved visual health outcomes.

Convolutional neural networks to assess bergamot essential oil content in the field from smartphone images

The essential oil (EO) extracted from bergamot peel (Citrus bergamia, Risso et Poiteau) is appreciated in perfumery and gastronomy. Notably, 90 % of the bergamot EO production is concentrated in the Province of Reggio Calabria (Southern Italy) under a protected designation of origin (PDO). The early estimation of EO content in fruits is fundamental to help farmers in their decision at harvesting period. The application of advanced modelling techniques based on artificial intelligence and digital device technology can contribute to this goal. This study proposes a method to estimate the EO content of fruits in the field using classification and regression models based on a deep learning approach in two cultivars: cv. “Fantastico” and cv. “Femminello”. The first step was to capture images of the fruit in the Red, Green, and Blue colours (RGB) using a mid-range smartphone camera and a portable inspection chamber designed and developed for this study. The acquisition of the images was carried out in the field. The fruits were collected and transported to the laboratory, where the EO was extracted using steam hydrodistillation. Custom-built convolutional neural networks (CNN) and three transfer learning models (VGG-16, VGG-19, and Xception architectures) were trained and applied for classification (among different discrete levels of oil content) and regression (to predict the EO content). The classification results showed an accuracy of 0.795 and 0.797 on the test samples of the two cultivars separately, while the best regression model achieved a minimum mean squared error of 0.12 and 0.04 for each cultivar, respectively. The results showed the effectiveness of the approach tested and how modelling each variety independently can lead to better performance for the CNNs tested.

Medical intelligence using PPG signals and hybrid learning at the edge to detect fatigue in physical activities

The educational environment plays a vital role in the development of students who participate in athletic pursuits both in terms of their physical health and their ability to detect fatigue. As a result of recent advancements in deep learning and biosensors benefitting from edge computing resources, we are now able to monitor the physiological fatigue of students participating in sports in real time. These devices can then be used to analyze the data using contemporary technology. In this paper, we present an innovative deep learning framework for forecasting fatigue in athletic students following physical exercise. It addresses the issue of lack of precision computational models and extensive data analysis in current approaches to monitoring students’ physical activity. In our study, we classified fatigue and non-fatigue based on photoplethysmography (PPG) signals. Several deep learning models are compared in the study. Using limited training data, determining the optimal parameters for PPG presents a significant challenge. For datasets containing many data points, several models were trained using PPG signals: a deep residual network convolutional neural network (ResNetCNN) ResNetCNN, an Xception architecture, a bidirectional long short-term memory (BILSTM), and a combination of these models. Training and testing datasets were assigned using a fivefold cross validation approach. Based on the testing dataset, the model demonstrated a proper classification accuracy of 91.8%.

Deep migration learning-based recognition of diseases and insect pests in Yunnan tea under complex environments

BackgroundThe occurrence, development, and outbreak of tea diseases and pests pose a significant challenge to the quality and yield of tea, necessitating prompt identification and control measures. Given the vast array of tea diseases and pests, coupled with the intricacies of the tea planting environment, accurate and rapid diagnosis remains elusive. In addressing this issue, the present study investigates the utilization of transfer learning convolution neural networks for the identification of tea diseases and pests. Our objective is to facilitate the accurate and expeditious detection of diseases and pests affecting the Yunnan Big leaf kind of tea within its complex ecological niche.ResultsInitially, we gathered 1878 image data encompassing 10 prevalent types of tea diseases and pests from complex environments within tea plantations, compiling a comprehensive dataset. Additionally, we employed data augmentation techniques to enrich the sample diversity. Leveraging the ImageNet pre-trained model, we conducted a comprehensive evaluation and identified the Xception architecture as the most effective model. Notably, the integration of an attention mechanism within the Xeption model did not yield improvements in recognition performance. Subsequently, through transfer learning and the freezing core strategy, we achieved a test accuracy rate of 98.58% and a verification accuracy rate of 98.2310%.ConclusionsThese outcomes signify a significant stride towards accurate and timely detection, holding promise for enhancing the sustainability and productivity of Yunnan tea. Our findings provide a theoretical foundation and technical guidance for the development of online detection technologies for tea diseases and pests in Yunnan.

An Adaptive Xception Model for Classification of Brain Tumors

Classification of different brain tumors is challenging due to unpredictable variations in intra-inter-classes. Unlike existing methods which are not effective for images of complex backgrounds, the proposed work aims at accurate classification of diverse types of brain tumors such that an appropriate model can be used for disease identification. This study considers glioma, meningioma, no tumor, and pituitary tumors for classification. To achieve an accurate classification, we explore the Xception architecture layer, which involves flattening, dropout, and dense layer operations. The model extracts features based on shapes, spatial relationships, and structure of the image, discriminating between the different brain tumor images. The model is evaluated on a dataset of 7023 MRI images for classification. The results of a large dataset and comparative study with the existing methods show that the proposed method is better than state of the art in terms of classification rate. Specifically, our method achieves more than a 90% average classification rate, which is better than state of the art. The results on noisy and blurred datasets show that the proposed model is robust to noise and blur.

COVID-19 Detection from Computed Tomography Images Using Slice Processing Techniques and a Modified Xception Classifier.

This paper extends our previous method for COVID-19 diagnosis, proposing an enhanced solution for detecting COVID-19 from computed tomography (CT) images using a lean transfer learning-based model. To decrease model misclassifications, two key steps of image processing were employed. Firstly, the uppermost and lowermost slices were removed, preserving sixty percent of each patient's slices. Secondly, all slices underwent manual cropping to emphasize the lung areas. Subsequently, resized CT scans (224 × 224) were input into an Xception transfer learning model with a modified output. Both Xception's architecture and pretrained weights were leveraged in the method. A big and rigorously annotated database of CT images was used to verify the method. The number of patients/subjects in the dataset is more than 5000, and the number and shape of the slices in each CT scan varies greatly. Verification was made both on the validation partition and on the test partition of unseen images. Results on the COV19-CT database showcased not only improvement from our previous solution and the baseline but also comparable performance to the highest-achieving methods on the same dataset. Further validation studies could explore the scalability and adaptability of the developed methodologies across diverse healthcare settings and patient populations. Additionally, investigating the integration of advanced image processing techniques, such as automated region of interest detection and segmentation algorithms, could enhance the efficiency and accuracy of COVID-19 diagnosis.

A novel approach of brain-computer interfacing (BCI) and Grad-CAM based explainable artificial intelligence: Use case scenario for smart healthcare

BackgroundIn order to push the frontiers of brain-computer interface (BCI) and neuron-electronics, this research presents a novel framework that combines cutting-edge technologies for improved brain-related diagnostics in smart healthcare. This research offers a ground-breaking application of transparent strategies to BCI, promoting openness and confidence in brain-computer interactions and taking inspiration from Grad-CAM (Gradient-weighted Class Activation Mapping) based Explainable Artificial Intelligence (XAI) methodology. The landscape of healthcare diagnostics is about to be redefined by the integration of various technologies, especially when it comes to illnesses related to the brain. New methodA novel approach has been proposed in this study comprising of Xception architecture which is trained on imagenet database following transfer learning process for extraction of significant features from magnetic resonance imaging dataset acquired from publicly available distinct sources as an input and linear support vector machine has been used for distinguishing distinct classes.Afterwards, gradient-weighted class activation mapping has been deployed as the foundation for explainable artificial intelligence (XAI) for generating informative heatmaps, representing spatial localization of features which were focused to achieve model’s predictions. ResultsThus, the proposed model not only provides accurate outcomes but also provides transparency for the predictions generated by the Xception network to diagnose presence of abnormal tissues and avoids overfitting issues. Hyperparameters along with performance-metrics are also obtained while validating the proposed network on unseen brain MRI scans to ensure effectiveness of the proposed network. Comparison with existing methods and conclusionsThe integration of Grad-CAM based explainable artificial intelligence with deep neural network namely Xception offers a significant impact in diagnosing brain tumor disease while highlighting the specific regions of input brain MRI images responsible for making predictions. In this study, the proposed network results in 98.92% accuracy, 98.15% precision, 99.09% sensitivity, 98.18% specificity and 98.91% dice-coefficient while identifying presence of abnormal tissues in the brain. Thus, Xception model trained on distinct dataset following transfer learning process offers remarkable diagnostic accuracy and linear support vector act as a classifier to provide efficient classification among distinct classes. In addition, the deployed explainable artificial intelligence approach helps in revealing the reasoning behind predictions made by deep neural network having black-box nature and provides a clear perspective to assist medical experts in achieving trustworthiness and transparency while diagnosing brain tumor disease in the smart healthcare

A Novel Optimized Colonic adenocarcinoma Detection using Deep Transfer Learning Approach with XceptionTS Model

Colonic adenocarcinoma is a major contributor to global mortality, highlighting the crucial need for efficient detection and classification techniques. This research presents a new method called XceptionTS for classifying and detecting colon cancer using colonoscopy pictures. The XceptionTS method utilizes deep transfer learning techniques by leveraging the Xception model architecture. Nonlinear Mean Filtering (NMF) is used as a noise reduction method in image processing to improve the quality of colonoscopy pictures. We combine the MobileNetV2 and ResNet-50 models for healthcare image segmentation and feature extraction, respectively. The XceptionTS classifier efficiently gives accurate class labels to medical photos by combining Tabu Search Optimization with the strong Xception architecture. The assessment of the effectiveness of XceptionTS model is done using a dataset of 1560 colonoscopy images. An extensive comparison study is undertaken by analyzing the efficacy of our suggested approach with existing research. The XceptionTS system outperforms previous methodologies in colon cancer classification and detection tasks, showing higher accuracy and robustness according to experimental results. Our findings indicate that the XceptionTS technique shows potential as an advanced tool to increase the effectiveness of Colonic adenocarcinoma diagnosis, which could lead to better patient outcomes and healthcare management.

Automated Medicinal Plant Identification and Use Case Generation: A Convolutional Neural Network-Xception Approach

Abstract: In the realm of botany, pharmacology, and herbal medicine, the identification of medicinal plants stands as a crucial yet labor-intensive endeavor. This project presents a pioneering approach to automate plant identification through the utilization of advanced deep learning techniques, specifically employing the Xception architecture within a Convolutional Neural Network (CNN). The methodology involves the acquisition of plant images, followed by preprocessing and dimension reduction to 299x299 pixels. Subsequently, the model undergoes rigorous training, achieving commendable accuracies of approximately 85% in training, 93% in validation, and 94% in testing phases. The trained model is then deployed through a user-friendly web interface, allowing seamless interaction for uploading images and receiving comprehensive plant classification results. Leveraging Flask for backend integration, this system bridges the gap between cutting-edge technology and botanical expertise, enabling researchers, botanists, pharmacologists, and herbalists to expedite plant identification processes with heightened accuracy and efficiency. Furthermore, this innovation not only streamlines identification tasks but also provides detailed insights into the diverse applications and attributes of medicinal plants, thereby fostering advancements in research, conservation, and utilization for the betterment of human health and well-being..

AI As the New Age Estimator: Pioneering Customized Facial Surgery Outcomes

Abstract Goals/Purpose The imperative for precision in aesthetic surgery necessitates a robust framework for evaluating the impact of facial interventions on perceived age. Our study introduces a cutting-edge AI model aimed at discerning an individual's perceived age from facial characteristics. This tool is designed to augment the assessment of various plastic surgery procedures, facilitating the tailoring of interventions to each patient's unique facial aging pattern. Methods/Technique We harnessed a deep convolutional neural network (DCNN), pre-trained on the extensive ImageNet dataset, and further refined using 523,051 pre-annotated facial images from the IMBD-WIKI database, normalized as per the Mathias et al. face detection paradigm. Faces were processed into a 299x299 pixel matrix, maintaining a 40% margin around the face for uniformity. The Xception architecture was employed for its advanced feature extraction capabilities. The model was refined and tested against a diverse set of 100 patient faces from the Mayo Clinic's database, categorized by demographic and procedural data. The AI model employed regression analysis and softmax probability for precise age estimation. Results/Complications The AI model exhibited a remarkable accuracy rate of 92.5% in age estimation for pre procedural patients, with a standard deviation of 3.2 years. It significantly outperformed traditional methods in identifying fine-grained age-related features. The AI model discerned an average perceived age reduction of 3.5 years across all patients post-procedure, with a notable variance among different types of surgeries. Certain procedures, such as rhytidectomy and blepharoplasty, showed a more pronounced age-reduction effect. Conclusion The AI model presents an accurate and objective method for quantifying perceived age, serving as a significant benchmark in facial aesthetic evaluation. By illustrating measurable age reduction following various procedures, with some surgeries yielding more substantial changes in perceived age, the model stands as a testament to the effectiveness of plastic surgery interventions. The precision of our model in predicting age pre- and post-procedure underscores its potential to assist surgeons in custom-tailoring surgeries to individual aging patterns. This innovation is poised to refine the decision-making process in aesthetic surgery, ensuring treatments are aligned with the desired outcomes for rejuvenation and patient-specific needs, ultimately advancing the frontier of personalized plastic surgery.

Offline Handwriting Writer Identification using Depth-wise Separable Convolution with Siamese Network

Offline handwriting writer identification has significant implications for forensic investigations and biometric authentication. Handwriting, as a distinctive biometric trait, provides insights into individual identity. Despite advancements in handcrafted algorithms and deep learning techniques, the persistent challenges related to intra-variability and inter-writer similarity continue to drive research efforts. In this study, we build on well-separated convolution architectures like the Xception architecture, which has proven to be robust in our previous research comparing various deep learning architectures such as MobileNet, EfficientNet, ResNet50, and VGG16, where Xception demonstrated minimal training-validation disparities for writer identification. Expanding on this, we use a model based on similarity or dissimilarity approaches to identify offline writers' handwriting, known as the Siamese Network, that incorporates the Xception architecture. Similarity or dissimilarity measurements are based on the Manhattan or L1 distance between representation vectors of each input pair. We train publicly available IAM and CVL datasets; our approach achieves accuracy rates of 99.81% for IAM and 99.88% for CVL. The model was evaluated using evaluation metrics, which revealed only two error predictions in the IAM dataset, resulting in 99.75% accuracy, and five error predictions for CVL, resulting in 99.57% accuracy. These findings modestly surpass existing achievements, highlighting the potential inherent in our methodology to enhance writer identification accuracy. This study underscores the effectiveness of integrating the Siamese Network with depth-wise separable convolution, emphasizing the practical implications for supporting writer identification in real-world applications.

Deep learning model to predict Ki-67 expression of breast cancer using digital breast tomosynthesis.

Developing a deep learning (DL) model for digital breast tomosynthesis (DBT) images to predict Ki-67 expression. The institutional review board approved this retrospective study and waived the requirement for informed consent from the patients. Initially, 499 patients (mean age: 50.5years, range: 29-90years) referred to our hospital for breast cancer were participated, 126 patients with pathologically confirmed breast cancer were selected and their Ki-67 expression measured. The Xception architecture was used in the DL model to predict Ki-67 expression levels. The high Ki-67 vs low Ki-67 expression diagnostic performance of our DL model was assessed by accuracy, sensitivity, specificity, areas under the receiver operating characteristic curve (AUC), and by using sub-datasets divided by the radiological characteristics of breast cancer. The average accuracy, sensitivity, specificity, and AUC were 0.912, 0.629, 0.985, and 0.883, respectively. The AUC of the four subgroups separated by radiological findings for the mass, calcification, distortion, and focal asymmetric density sub-datasets were 0.890, 0.750, 0.870, and 0.660, respectively. Our results suggest the potential application of our DL model to predict the expression of Ki-67 using DBT, which may be useful for preoperatively determining the treatment strategy for breast cancer.

Comparative Study on Different CNN Architectures Developed on Microstructural Classification in Al-Si Alloys

Recent advances in artificial intelligence have opened up new avenues for microstructure characterization, notably in metallic materials. Physical and mechanical properties generally depend on the microstructure of the metallic material. On the other hand, microstructural characterization takes time and calls for specific techniques that don’t always lead to conclusive results quickly. To address this issue, this research focuses on the application of artificial intelligence approaches to microstructural categorization. We demonstrate the advantages of the AI approach using an example of Al-Si alloy, a material that is widely employed in a variety of industries. To specify a suitable convolutional neural network (CNN) approach for the microstructural classification of the Al-Si alloy, CNN models were trained and compared using DenseNet201, Inception v3, InceptionResNetV2, ResNet152V2, VGG16, and Xception architectures. Resulting from the comparison, it was determined that the developed supervised transfer learning model can execute the microstructural classification of Al-Si alloy microstructural images. This paper is an attempt to advance methods of microstructure recognition/classification/characterization by using Deep Learning approaches. The significance of the established model is demonstrated and its accordance with the literature data. Also, necessity is shown of developing material models and optimization through systematic microstructural investigation, production conditions, and material attributes.

A comparison of deep transfer learning backbone architecture techniques for printed text detection of different font styles from unstructured documents.

Object detection methods based on deep learning have been used in a variety of sectors including banking, healthcare, e-governance, and academia. In recent years, there has been a lot of attention paid to research endeavors made towards text detection and recognition from different scenesor images of unstructured document processing. The article's novelty lies in the detailed discussion and implementation of the various transfer learning-based different backbone architectures for printed text recognition. In this research article, the authors compared the ResNet50, ResNet50V2, ResNet152V2, Inception, Xception, and VGG19 backbone architectures with preprocessing techniques as data resizing, normalization, and noise removal on a standard OCR Kaggle dataset. Further, the top three backbone architectures selected based on the accuracy achieved and then hyper parameter tunning has been performed to achieve more accurate results. Xception performed well compared with the ResNet, Inception, VGG19, MobileNet architectures by achieving high evaluation scores with accuracy (98.90%) and min loss (0.19). As per existing research in this domain, until now, transfer learning-based backbone architectures that have been used on printed or handwritten data recognition are not well represented in literature. We split the total dataset into 80 percent for training and 20 percent for testing purpose and then into different backbone architecture models with the same number of epochs, and found that the Xception architecture achieved higher accuracy than the others. In addition, the ResNet50V2 model gave us higher accuracy (96.92%) than the ResNet152V2 model (96.34%).

A deep learning approach for projection and body-side classification in musculoskeletal radiographs

BackgroundThe growing prevalence of musculoskeletal diseases increases radiologic workload, highlighting the need for optimized workflow management and automated metadata classification systems. We developed a large-scale, well-characterized dataset of musculoskeletal radiographs and trained deep learning neural networks to classify radiographic projection and body side.MethodsIn this IRB-approved retrospective single-center study, a dataset of musculoskeletal radiographs from 2011 to 2019 was retrieved and manually labeled for one of 45 possible radiographic projections and the depicted body side. Two classification networks were trained for the respective tasks using the Xception architecture with a custom network top and pretrained weights. Performance was evaluated on a hold-out test sample, and gradient-weighted class activation mapping (Grad-CAM) heatmaps were computed to visualize the influential image regions for network predictions.ResultsA total of 13,098 studies comprising 23,663 radiographs were included with a patient-level dataset split, resulting in 19,183 training, 2,145 validation, and 2,335 test images. Focusing on paired body regions, training for side detection included 16,319 radiographs (13,284 training, 1,443 validation, and 1,592 test images). The models achieved an overall accuracy of 0.975 for projection and 0.976 for body-side classification on the respective hold-out test sample. Errors were primarily observed in projections with seamless anatomical transitions or non-orthograde adjustment techniques.ConclusionsThe deep learning neural networks demonstrated excellent performance in classifying radiographic projection and body side across a wide range of musculoskeletal radiographs. These networks have the potential to serve as presorting algorithms, optimizing radiologic workflow and enhancing patient care.Relevance statementThe developed networks excel at classifying musculoskeletal radiographs, providing valuable tools for research data extraction, standardized image sorting, and minimizing misclassifications in artificial intelligence systems, ultimately enhancing radiology workflow efficiency and patient care.Key points• A large-scale, well-characterized dataset was developed, covering a broad spectrum of musculoskeletal radiographs.• Deep learning neural networks achieved high accuracy in classifying radiographic projection and body side.• Grad-CAM heatmaps provided insight into network decisions, contributing to their interpretability and trustworthiness.• The trained models can help optimize radiologic workflow and manage large amounts of data.Graphical

Species identification through deep learning and geometrical morphology in oaks (Quercus spp.): Pros and cons.

Plant phenotypic characteristics, especially leaf morphology of leaves, are an important indicator for species identification. However, leaf shape can be extraordinarily complex in some species, such as oaks. The great variation in leaf morphology and difficulty of species identification in oaks have attracted the attention of scientists since Charles Darwin. Recent advances in discrimination technology have provided opportunities to understand leaf morphology variation in oaks. Here, we aimed to compare the accuracy and efficiency of species identification in two closely related deciduous oaks by geometric morphometric method (GMM) and deep learning using preliminary identification of simple sequence repeats (nSSRs) as a prior. A total of 538 Asian deciduous oak trees, 16 Q. aliena and 23 Q. dentata populations, were firstly assigned by nSSRs Bayesian clustering analysis to one of the two species or admixture and this grouping served as a priori identification of these trees. Then we analyzed the shapes of 2328 leaves from the 538 trees in terms of 13 characters (landmarks) by GMM. Finally, we trained and classified 2221 leaf-scanned images with Xception architecture using deep learning. The two species can be identified by GMM and deep learning using genetic analysis as a priori. Deep learning is the most cost-efficient method in terms of time-consuming, while GMM can confirm the admixture individuals' leaf shape. These various methods provide high classification accuracy, highlight the application in plant classification research, and are ready to be applied to other morphology analysis.