Chest X-ray Dataset Research Articles

Introducing SPINE: A Holistic Approach to Synthetic Pulmonary Imaging Evaluation Through End-to-End Data and Model Management.

In the evolving field of medical imaging and machine learning (ML), this paper introduces a novel framework for evaluating synthetic pulmonary imaging aiming to assess synthetic data quality and applicability. Our study concentrates on synthetic X-ray chest images, crucial for diagnosing respiratory diseases. We employ SPINE (Synthetic Pulmonary Imaging Evaluation) framework, a threefold synthetic images evaluation method including expert domain assessment, statistical data analysis and adversarial evaluation. In order to replicate and validate our methodology, we followed an End-to-End data and model management process which begins with a dataset of Normal and Pneumonia chest X-rays, generating synthetic images using Generative Adversarial Networks (GANs) and training a baseline classifier, essential in the adversarial evaluation axis, testing synthetic images against real data assessing their predictive value. The critical outcome of our approach is the post-market analysis of synthetic images. This innovative method evaluates synthetic images using clinical, statistical, and scientific criteria independently from traditional generation performance metrics. This independent evaluation provides deep insights into the clinical and research effectiveness of the synthetic data. By ensuring these images mirror real data's statistical properties and maintain clinical accuracy, our framework establishes a new standard for the ethical and reliable use of synthetic data in medical imaging and research.

ResNet-50 vs. EfficientNet-B0: Multi-Centric Classification of Various Lung Abnormalities Using Deep Learning

Lung abnormalities are among the significant contributors to morbidity and mortality worldwide. It induces symptoms like coughing, sneezing, fever, breathlessness, etc., which, if left untreated, may lead to death. In current clinical practice, chest X-ray (CXR) images are widely preferred to diagnose different lung abnormalities. However, the pathological tests are time-consuming, expensive and require domain experts. On the other hand, diagnosis through CXR images is manual and subject to inter-observer and intra-observer variability. The recent advancement in deep learning (DL) algorithms may be employed to address these challenges. However, the selection of correct algorithms along with finetuned parameters is challenging. In this study, we comprehensively compared the performance of two state-of-the-art DL algorithms, Resnet-50 and Efficient-B0. These two models are pervasively used in literature and have shown promising classification performance. The performance of the used algorithms is validated using the multi-centric dataset from Kaggle (having Covid-19, Normal, Pneumonia classes) and Mendeley chest X-ray dataset (having Normal, Pneumonia-Bacterial, Pneumonia-Viral, Covid-19 classes). Upon training the algorithms with a mini-batch size 32 and a maximum epoch of 40 using training data, we achieved 0.9807 and 0.9874 accuracy for Kaggle Dataset and Mendeley Dataset, respectively. Similarly, we achieved 0.9962 and 9.9978 for Kaggle dataset and Mendeley dataset for test data, respectively. From the result, it is evident that the EfficientNet-B0 model outperformed the multi-centric datasets.

Multi-Label Generalized Zero Shot Chest Xray Classification By Combining Image-Text Information With Feature Disentanglement.

In fully supervised learning-based medical image classification, the robustness of a trained model is influenced by its exposure to the range of candidate disease classes. Generalized Zero Shot Learning (GZSL) aims to correctly predict seen and novel unseen classes. Current GZSL approaches have focused mostly on the single-label case. However, it is common for chest X-rays to be labelled with multiple disease classes. We propose a novel multi-modal multi-label GZSL approach that leverages feature disentanglement andmulti-modal information to synthesize features of unseen classes. Disease labels are processed through a pre-trained BioBert model to obtain text embeddings that are used to create a dictionary encoding similarity among different labels. We then use disentangled features and graph aggregation to learn a second dictionary of inter-label similarities. A subsequent clustering step helps to identify representative vectors for each class. The multi-modal multi-label dictionaries and the class representative vectors are used to guide the feature synthesis step, which is the most important component of our pipeline, for generating realistic multi-label disease samples of seen and unseen classes. Our method is benchmarked against multiple competing methods and we outperform all of them based on experiments conducted on the publicly available NIH and CheXpert chest X-ray datasets.

The The Classification and Segmentation of Pneumonia using Deep Learning Algorithms: A Comparative Study

The paper uses convolutional neural networks (CNNs) to analyze radiography pictures and discriminate between areas afflicted by pneumonia and normal lung tissue. A sizable dataset of annotated chest X-rays is used to train the deep learning model, which enables it to pick up on complex patterns and characteristics linked to pneumonia. Pneumonia is a significant respiratory disease that affects a large number of individuals worldwide. Timely and accurate diagnosis of pneumonia plays a crucial role in effective treatment and management of the disease. We evaluate the performance of several up-to-date convolution neural network (CNN) architectures, namely ResNet-50, VGG-16, and DenseNet-121, then compare their results with traditional machine learning classifiers. Recent advancements in deep learning methods have shown accurate results in the investigation and diagnosis of medical image data, including the detection of pneumonia. This paper examines different deep-learning methods for categorizing pneumonia from lung X-ray imagery. Our results show that deep learning techniques performed better than conventional machine learning techniques in classifying pneumonia, with an estimated accuracy of 95% across all of the examined CNN models. These results demonstrate the potential of deep learning algorithms to significantly improve the accuracy and effectiveness of pneumonia diagnosis, supporting physicians in making knowledgeable decisions about patient care.

CheXNet and feature pyramid network: a fusion deep learning architecture for multilabel chest X-Ray clinical diagnoses classification.

The existing multilabel X-Ray image learning tasks generally contain much information on pathology co-occurrence and interdependency, which is very important for clinical diagnosis. However, the challenging part of this subject is to accurately diagnose multiple diseases that occurred in a single X-Ray image since multiple levels of features are generated in the images, and create different features as in single label detection. Various works were developed to address this challenge with proposed deep learning architectures to improve classification performance and enrich diagnosis results with multi-probability disease detection. The objective is to create an accurate result and a faster inference system to support a quick diagnosis in the medical system. To contribute to this state-of-the-art, we designed a fusion architecture, CheXNet and Feature Pyramid Network (FPN), to classify and discriminate multiple thoracic diseases from chest X-Rays. This concept enables the model to extract while creating a pyramid of feature maps with different spatial resolutions that capture low-level and high-level semantic information to encounter multiple features. The model's effectiveness is evaluated using the NIH ChestXray14 dataset, with the Area Under Curve (AUC) and accuracy metrics used to compare the results against other cutting-edge approaches. The overall results demonstrate that our method outperforms other approaches and has become promising for multilabel disease classification in chest X-Rays, with potential applications in clinical practice. The result demonstrated that we achieved an average AUC of 0.846 and an accuracy of 0.914. Further, our proposed architecture diagnoses images in 0.013s, faster than the latest approaches.

Lung cancer detection via deep learning-based pyramid network with honey badger algorithm

Lung carcinoma is a fatal illness characterized by the unchecked growth of tumors in the respiratory tract. A diagnosis of cancers and their dimensions and forms are often difficult to ascertain from imaging tests because of their irregular form. And high discrepancy in health images is a severe problem in health image investigation. To overcome this issue a novel deep learning-based lung cancer detection (DL-LCD) model is proposed. The LIDC-IDRI and NIH Chest X-ray dataset was used which comprises CT scan and CXR images. Initially, these images are pre-processed using CLAHE (Contrast Limited Adaptive Histogram Equalization) by eliminating noise distortions and enhancing the image clarity. The lung images are segmented using Honey Badger Algorithm. The segmented images are classified into cancerous and non-cancerous. Based on the LIDC-IDRI database, achieves 98.5 % accuracy in normal lungs and 97.44 % accuracy in abnormal lungs. The results of the experiments indicate that the proposed approach outperforms existing approaches.

WindowNet: Learnable Windows for Chest X-ray Classification.

Public chest X-ray (CXR) data sets are commonly compressed to a lower bit depth to reduce their size, potentially hiding subtle diagnostic features. In contrast, radiologists apply a windowing operation to the uncompressed image to enhance such subtle features. While it has been shown that windowing improves classification performance on computed tomography (CT) images, the impact of such an operation on CXR classification performance remains unclear. In this study, we show that windowing strongly improves the CXR classification performance of machine learning models and propose WindowNet, a model that learns multiple optimal window settings. Our model achieved an average AUC score of 0.812 compared with the 0.759 score of a commonly used architecture without windowing capabilities on the MIMIC data set.

SWSSL: Sliding Window-Based Self-Supervised Learning for Anomaly Detection in High-Resolution Images.

Anomaly detection (AD) aims to determine if an instance has properties different from those seen in normal cases. The success of this technique depends on how well a neural network learns from normal instances. We observe that the learning difficulty scales exponentially with the input resolution, making it infeasible to apply AD to high-resolution images. Resizing them to a lower resolution is a compromising solution and does not align with clinical practice where the diagnosis could depend on image details. In this work, we propose to train the network and perform inference at the patch level, through the sliding window algorithm. This simple operation allows the network to receive high-resolution images but introduces additional training difficulties, including inconsistent image structure and higher variance. We address these concerns by setting the network's objective to learn augmentation-invariant features. We further study the augmentation function in the context of medical imaging. In particular, we observe that the resizing operation, a key augmentation in general computer vision literature, is detrimental to detection accuracy, and the inverting operation can be beneficial. We also propose a new module that encourages the network to learn from adjacent patches to boost detection performance. Extensive experiments are conducted on breast tomosynthesis and chest X-ray datasets and our method improves 8.03% and 5.66% AUC on image-level classification respectively over the current leading techniques. The experimental results demonstrate the effectiveness of our approach.

Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images.

Unsupervised anomaly detection (UAD) methods are trained with normal (or healthy) images only, but during testing, they are able to classify normal and abnormal (or disease) images. UAD is an important medical image analysis (MIA) method to be applied in disease screening problems because the training sets available for those problems usually contain only normal images. However, the exclusive reliance on normal images may result in the learning of ineffective low-dimensional image representations that are not sensitive enough to detect and segment unseen abnormal lesions of varying size, appearance, and shape. Pre-training UAD methods with self-supervised learning, based on computer vision techniques, can mitigate this challenge, but they are sub-optimal because they do not explore domain knowledge for designing the pretext tasks, and their contrastive learning losses do not try to cluster the normal training images, which may result in a sparse distribution of normal images that is ineffective for anomaly detection. In this paper, we propose a new self-supervised pre-training method for MIA UAD applications, named Pseudo Multi-class Strong Augmentation via Contrastive Learning (PMSACL). PMSACL consists of a novel optimisation method that contrasts a normal image class from multiple pseudo classes of synthesised abnormal images, with each class enforced to form a dense cluster in the feature space. In the experiments, we show that our PMSACL pre-training improves the accuracy of SOTA UAD methods on many MIA benchmarks using colonoscopy, fundus screening and Covid-19 Chest X-ray datasets.

Empowering COVID-19 detection: Optimizing performance through fine-tuned EfficientNet deep learning architecture

The worldwide COVID-19 pandemic has profoundly influenced the health and everyday experiences of individuals across the planet. It is a highly contagious respiratory disease requiring early and accurate detection to curb its rapid transmission. Initial testing methods primarily revolved around identifying the genetic composition of the coronavirus, exhibiting a relatively low detection rate and requiring a time-intensive procedure. To address this challenge, experts have suggested using radiological imagery, particularly chest X-rays, as a valuable approach within the diagnostic protocol. This study investigates the potential of leveraging radiographic imaging (X-rays) with deep learning algorithms to swiftly and precisely identify COVID-19 patients. The proposed approach elevates the detection accuracy by fine-tuning with appropriate layers on various established transfer learning models. The experimentation was conducted on a COVID-19 X-ray dataset containing 2000 images. The accuracy rates achieved were impressive of 99.55%, 97.32%, 99.11%, 99.55%, 99.11% and 100% for Xception, InceptionResNetV2, ResNet50 , ResNet50V2, EfficientNetB0 and EfficientNetB4 respectively. The fine-tuned EfficientNetB4 achieved an excellent accuracy score, showcasing its potential as a robust COVID-19 detection model. Furthermore, EfficientNetB4 excelled in identifying Lung disease using Chest X-ray dataset containing 4,350 Images, achieving remarkable performance with an accuracy of 99.17%, precision of 99.13%, recall of 99.16%, and f1-score of 99.14%. These results highlight the promise of fine-tuned transfer learning for efficient lung detection through medical imaging, especially with X-ray images. This research offers radiologists an effective means of aiding rapid and precise COVID-19 diagnosis and contributes valuable assistance for healthcare professionals in accurately identifying affected patients.

Efficient Thorax Disease Classification and Localization Using DCNN and Chest X-ray Images.

Thorax disease is a life-threatening disease caused by bacterial infections that occur in the lungs. It could be deadly if not treated at the right time, so early diagnosis of thoracic diseases is vital. The suggested study can assist radiologists in more swiftly diagnosing thorax disorders and in the rapid airport screening of patients with a thorax disease, such as pneumonia. This paper focuses on automatically detecting and localizing thorax disease using chest X-ray images. It provides accurate detection and localization using DenseNet-121 which is foundation of our proposed framework, called Z-Net. The proposed framework utilizes the weighted cross-entropy loss function (W-CEL) that manages class imbalance issue in the ChestX-ray14 dataset, which helped in achieving the highest performance as compared to the previous models. The 112,120 images contained in the ChestX-ray14 dataset (60,412 images are normal, and the rest contain thorax diseases) were preprocessed and then trained for classification and localization. This work uses computer-aided diagnosis (CAD) system that supports development of highly accurate and precise computer-aided systems. We aim to develop a CAD system using a deep learning approach. Our quantitative results show high AUC scores in comparison with the latest research works. The proposed approach achieved the highest mean AUC score of 85.8%. This is the highest accuracy documented in the literature for any related model.

How intra-source imbalanced datasets impact the performance of deep learning for COVID-19 diagnosis using chest X-ray images

Over the past decade, the use of deep learning has been widely increasing in the medical image diagnosis field. Deep learning-based methods’ (DLMs) performance strongly relies on training data. Therefore, researchers often focus on collecting as much data as possible from different medical facilities or developing approaches to avoid the impact of inter-category imbalance (ICI), which means a difference in data quantity among categories. However, due to the ICI within each medical facility, medical data are often isolated and acquired in different settings among medical facilities, known as the issue of intra-source imbalance (ISI) characteristic. This imbalance also impacts the performance of DLMs but receives negligible attention. In this study, we study the impact of the ISI on DLMs by comparison of the version of a deep learning model that was trained separately by an intra-source imbalanced chest X-ray (CXR) dataset and an intra-source balanced CXR dataset for COVID-19 diagnosis. The finding is that using the intra-source imbalanced dataset causes a serious training bias, although the dataset has a good inter-category balance. In contrast, the deep learning model performed a reliable diagnosis when trained on the intra-source balanced dataset. Therefore, our study reports clear evidence that the intra-source balance is vital for training data to minimize the risk of poor performance of DLMs.

Computer-aided Detection of Tuberculosis from Microbiological and Radiographic Images

ABSTRACT Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis tools. Tuberculosis can be detected from microscopic slides and chest X-ray but as a result of the high cases of tuberculosis, this method can be tedious for both Microbiologists and Radiologists and can lead to miss-diagnosis. These challenges can be solved by employing Computer-Aided Detection (CAD)via AI-driven models which learn features based on convolution and result in an output with high accuracy. In this paper, we described automated discrimination of X-ray and microscope slide images into tuberculosis and non-tuberculosis cases using pretrained AlexNet Models. The study employed Chest X-ray dataset made available on Kaggle repository and microscopic slide images from both Near East University Hospital and Kaggle repository. For classification of tuberculosis using microscopic slide images, the model achieved 90.56% accuracy, 97.78% sensitivity and 83.33% specificity for 70: 30 splits. For classification of tuberculosis using X-ray images, the model achieved 93.89% accuracy, 96.67% sensitivity and 91.11% specificity for 70:30 splits. Our result is in line with the notion that CNN models can be used for classifying medical images with higher accuracy and precision.

COVID-19 Diagnosis Based on Multi Deep Neural Networks of Chest Lung X-Ray Images

In this paper, increased attempts are carried out to develop the deep neural networks to diagnose COVID-19 based on Chest X-Ray (CXR). This work introduces a new diagnostic system, utilizing different deep neural networks, InceptionV3, DenseNet121, ResNet50, VGG16 and MobileNetV2 models, to classify CXR images into healthy normal, pneumonia bacterial and COVID-19. Additionally, transfer learning and data augmentation techniques are utilized to solve the problem of scarcity of CXR images; hence the over-fitting of deep models will be avoided. The proposed end-to-end deep system is applied on the dataset sourced from COVID-19 lung CXR images from Kaggle and the IEEE8020 COVID-19 CXR dataset which is introduced by John Hopkins Hospital. The classification results reveal that diagnostic of CXR lung images by InceptionV3 provides the best results, with 98.87% accuracy, 98.88% area under the curve (AUC), 98.98% sensitivity, 98.79% precision, 97.99% F1- score and 1.4574 s computational time. Finally, the ensemble classifier with a voting strategy is proposed to boost the diagnosis performance using the multiple convolutional neural networks (CNNs) instead of one. The classification results are aggregated of each image using the voting technique. It is observed that the performance of the proposed ensemble classifiers with voting strategy reaches the state-of-art performance.

A Hybrid Approach using CNN and DQN Technique for Diagnosis Pneumonia in Chest X-Ray Images

Pneumonia poses a significant risk to life and well-being respiratory infection that requires accurate and timely diagnosis for effective treatment. In this research investigation, it proposes a hybrid approach for detecting pneumonia diagnosis in chest X-ray images by combining machine learning techniques with convolutional neural networks (CNN), and deep Q-network (DQN) reinforcement learning. The suggested approach holds promising prospects for enhancing the efficacy of pneumonia diagnosis. Especially in resource-limited settings where access to radiologists or specialized equipment is limited. The proposed hybrid approach involves multiple stages. Initially, an extensive collection dataset of chest X-ray images, comprising both normal and pneumonia cases, is collected. The CNN model can be integrated into clinical decision support systems to provide accurate diagnosis of infection for pneumonia. Furthermore, the use of the rainbow method can be extended to other clinical imaging tasks to enhance deep learning models performance Additionally, it demonstrates that the use of the rainbow method improves the performance of the CNN, leading to a higher accuracy. We have introduced a novel hybrid deep learning framework called LIP-CDF Algorithm, which combines algorithms of Convolutional Neural Networks (CNN) and Deep Q-Network (DQN) techniques. LIP-CDF (Lung Infection Prediction using CNN-DQN Fusion) Algorithm is a computational approach designed for the accurate and efficient lung infection prediction using images from chest X-rays. The implementation of this framework utilized popular tools such as Jupyter Notebook, TensorFlow, and Keras. To assess the effectiveness of our model, the NIH chest X-beam picture dataset gained from the Kaggle archive. To evaluate the effectiveness of our proposed approach, we conduct experiments on the publicly available Chest X-ray14 dataset. The results show that our approach achieves a high accuracy of 94.8% in detecting pneumonia cases.
 The purpose of our framework is to streamline the detection of lung diseases, making it easier for both medical experts and doctors. By harnessing the power of CNN and DQN, our approach offers a simplified yet accurate method for identifying lung diseases from chest X-ray images. This advancement in deep learning technology has the potential to greatly assist healthcare professionals in diagnosing and treating patients effectively

A multi-objective segmentation method for chest X-rays based on collaborative learning from multiple partially annotated datasets

Accurate segmentation of multiple targets, such as ribs, clavicles, heart, and lung fields, from chest X-ray images is crucial for diagnosing various lung diseases. Currently, mainstream deep learning methods heavily rely on fully annotated large-scale datasets. However, annotating all objects in chest X-ray images is a labor-intensive and time-consuming task. The publicly available partially annotated chest X-ray datasets have varying annotation objects and standards, and they are seldom utilized comprehensively in existing studies. To address these challenges, we propose A Multi-objective Segmentation Method for chest X-rays based on Collaborative Learning from Multiple Partially Annotated Datasets (called: MSM-CLMPAD). Our approach first utilizes an encoder constructed with densely connected blocks to extract multi-scale features from multiple partially annotated datasets. Then, leveraging the overlapping relationships among segmentation targets, we design a dual decoder guided by the attention mechanism. The novel attention-guided decoder effectively disentangles the features corresponding to various targets. Importantly, we propose an alternating training strategy for different datasets, facilitating collaborative learning of the same network model across datasets with diverse annotation targets. The experimental results performing on four public datasets demonstrate that our method achieves superior Dice and Jaccard coefficients compared to other popular methods, particularly for overlapping targets and unclear regions. We also explore the mutual influence of different targets in chest X-rays, offering a solution for interaction among partial datasets and further alleviating the difficulties of data annotation for multi-organ segmentation tasks.

Multi-Label Classification of Chest X-ray Abnormalities Using Transfer Learning Techniques.

In recent years, deep neural networks have enabled countless innovations in the field of image classification. Encouraged by success in this field, researchers worldwide have demonstrated how to use Convolutional Neural Network techniques in medical imaging problems. In this article, the results were obtained through the use of the EfficientNet in the task of classifying 14 different diseases based on chest X-ray images coming from the NIH (National Institutes of Health) ChestX-ray14 dataset. The approach addresses dataset imbalances by introducing a custom split to ensure fair representation. Binary cross entropy loss is utilized to handle the multi-label difficulty. The model architecture comprises an EfficientNet backbone for feature extraction, succeeded by sequential layers including GlobalAveragePooling, Dense, and BatchNormalization. The main contribution of this paper is a proposed solution that outperforms previous state-of-the-art deep learning models average area under the receiver operating characteristic curve-AUC-ROC (score: 84.28%). The usage of the transfer-learning technique and traditional deep learning engineering techniques was shown to enable us to obtain such results on consumer-class GPUs (graphics processing units).

Using Computer Vision Techniques to Automatically Detect Abnormalities in Chest X-rays.

Our research focused on creating an advanced machine-learning algorithm that accurately detects anomalies in chest X-ray images to provide healthcare professionals with a reliable tool for diagnosing various lung conditions. To achieve this, we analysed a vast collection of X-ray images and utilised sophisticated visual analysis techniques; such as deep learning (DL) algorithms, object recognition, and categorisation models. To create our model, we used a large training dataset of chest X-rays, which provided valuable information for visualising and categorising abnormalities. We also utilised various data augmentation methods; such as scaling, rotation, and imitation; to increase the diversity of images used for training. We adopted the widely used You Only Look Once (YOLO) v8 algorithm, an object recognition paradigm that has demonstrated positive outcomes in computer vision applications, and modified it to classify X-ray images into distinct categories; such as respiratory infections, tuberculosis (TB), and lung nodules. It was particularly effective in identifying unique and crucial outcomes that may, otherwise, be difficult to detect using traditional diagnostic methods. Our findings demonstrate that healthcare practitioners can reliably use machine learning (ML) algorithms to diagnose various lung disorders with greater accuracy and efficiency.

Covid-19 Mortality Risk Prediction Using Small Dataset of Chest X-Ray Images

COVID-19 outbreak ravaged the whole world starting from the early part of 2020. The rapid spread of the pandemic accounts for the major reason the world was thrown into panic mode and pervasive confusion. However, COVID-19’s greatest strength is its virility but its severity on an individual is mostly ambiguous, which is dependent on the particular individual. This, combined with the increasingly limited capacity of the global healthcare infrastructure warrants some mechanism that can predict the prognosis of an individual to better determine if the patient would require hospital resources or be better treated as an outpatient. The lack of such a mechanism leads to suboptimal utilization of valuable hospital resources leading to unnecessary loss of life. However, often at the onset of a pandemic such as it was experienced during the outbreak of COVID-19, ample and appropriately labelled dataset to build accurate deep learning models to assist in this respect was limited. In this vein, frantic efforts were made to acquire dataset to train deep learning models for the stated objectives, unfortunately only a small dataset from a single source was available at the time of the study. Consequently, deep learning models based on the ResNet-18 architecture were trained on a small dataset of chest X-rays of patients infected with COVID-19 to predict mortality risk. The models exhibit considerable accuracy with high sensitivity. The appropriateness of the techniques proposed in this study for predictive modelling maybe particularly suited when only small datasets are available especially at the onset of similar pandemics. From existing literature, models with low complexity such as ResNet perform better with small dataset. Hence, this study utilised ResNet-18 as the baseline to evaluate the performance of other popular models on small datasets. The performance of the baseline models based on ResNet-18 with an accuracy of 0.89 compared favourably with those of the several other models including AlexNet, MobileNetV3, EfficientNetV2, SwinTransformer, and ConvNeXt using the same datasets and similar parameters.

Chest X-ray Foreign Objects Detection Using Artificial Intelligence.

Diagnostic imaging has become an integral part of the healthcare system. In recent years, scientists around the world have been working on artificial intelligence-based tools that help in achieving better and faster diagnoses. Their accuracy is crucial for successful treatment, especially for imaging diagnostics. This study used a deep convolutional neural network to detect four categories of objects on digital chest X-ray images. The data were obtained from the publicly available National Institutes of Health (NIH) Chest X-ray (CXR) Dataset. In total, 112,120 CXRs from 30,805 patients were manually checked for foreign objects: vascular port, shoulder endoprosthesis, necklace, and implantable cardioverter-defibrillator (ICD). Then, they were annotated with the use of a computer program, and the necessary image preprocessing was performed, such as resizing, normalization, and cropping. The object detection model was trained using the You Only Look Once v8 architecture and the Ultralytics framework. The results showed not only that the obtained average precision of foreign object detection on the CXR was 0.815 but also that the model can be useful in detecting foreign objects on the CXR images. Models of this type may be used as a tool for specialists, in particular, with the growing popularity of radiology comes an increasing workload. We are optimistic that it could accelerate and facilitate the work to provide a faster diagnosis.