Fingers are vital for doing daily chores. One of the most often used and most sensitive appendages, fingers are vulnerable to injury. Anyone could become slower if they suffered from finger injury. Phalanges are the name for the bones in your fingers. All fingers have three phalanges and The thumb consists of two phalanges, and the bones in the fingers are interconnected and held together by ligaments. Tendons link bones and muscles.The muscles in the forearm that exert force on the tendons of the fingers play a role in controlling the movement of the fingers. The distance between the finger bones and the rest of the body is what distinguishes them. The proximal phalanx is the bone that lies nearest to the palm. The middle phalanx is the next bone after the proximal phalange. The distal phalanx is the last bone, the smallest and the furthest from the hand. There is no middle phalanx on the thumb. When one or more of these bone breaks, it referred as broken or fractured finger. Your knuckles, which are the joints where your finger bones converge, are another place where fractures can happen. You’re more likely to shatter a finger if you have diseases like osteoporosis and malnutrition. A fracture might occasionally happen because of atypical bone in the finger. A condition that weakens the bone and makes it more susceptible to fracture results in this form of fracture, known as a pathologic fracture. It happens frequently for someone to believe they have sprained their finger when in fact they have a fracture that needs to be treated surgically. A delayed diagnosis can result in a worse long-term outcome if an injury is treated as a sprain when it is actually a fracture that needs specialist care. This frequently refers to restricted mobility or a chronically deformed finger. Because of this, any suspicious finger injury should be examined by a doctor to see if an X-ray is necessary. Your doctor will likely order an X-ray if you exhibit signs of a fractured finger to see if there is a fracture. If the diagnosis is uncertain, it might be essential to take additional X-rays in various orientations because not all fractures will show up clearly on a single image. To determine the best course of therapy, the wounded finger should be examined. If the digit has been rotated or shortened as a result of the injury, this might be discovered by examination. You’ll discover that these are significant guiding variables for treatment. To be sure there is no tendon damage or other injury that could change the recommended course of treatment, examination can be helpful.When X-ray is done, finding fractures with the naked eye is a laborious technique that carries a larger chance of missing small fractures. Therefore, the goal is to design a finger fracture classification system that can be used regularly in any busy orthopaedic clinic to detect finger fractures and to make decision taking easy on a course of treatment, without formal criticism. In this project we were aimed to classify and detect finger fractures using deep learning architecture. We use the dataset called MURA, and images were preprocessed using resizing and data augmentation to address data imbalance issues. Multiple deep learning models such as Resnet34, ResNet50, ResNet101, ResNet152, VGG-16 and VGG-19 are trained and tested for classification and detection of finger fractures. Standard machine learning techniques are used to optimize the model’s performance. The models are assessed using metrics including F1 score, recall, accuracy, and precision. Lastly, a test dataset is used to evaluate the generalization capacity of the top-performing model. The outcomes are examined in order to make judgments regarding the viability of the suggested strategy for classification and detection of finger fractures.

The major research contributions of this study are given as below:

The rest of the article is organised in such a way that Section 2 provides an over view of the existing research in the field of finger fracture classification. Methodology of the current study is elaborated in Sections 3 while Section 4 provides statistics regarding the results of multiple experiments. Conclusion and Future Work are provided in Section 5.

In this study, the researcher employed a multi-network model and utilized the "MURA" dataset, achieving an accuracy of 90.77%. The model comprised three sub-networks designed to identify distinct categories of abnormalities in radiographs. These sub-networks were dedicated to detecting abnormalities related to bones, joints, and implants, respectively.[1]. In this study, By using the BoneNet model and a dataset of bone X-rays, the author was able to achieve an astounding accuracy of 98.6%. The model proved adept at identifying subtle distinctions among closely related bone categories, surpassing the capabilities of human observation. Notably, the model showcased resilience in the face of changes in bone positioning, angles, and image quality, highlighting its consistent accuracy across diverse conditions. [2]. In this research, the researcher utilized a publicly accessible dataset comprising 3000 femur X-ray images, achieving an accuracy of 93.8% through the implementation of the Vision Transformation (ViT) model. Scientists utilizing the ImageNet dataset’s pre-trained weights. The training process involved a dataset consisting of approximately 3,000 femoral X-ray images. The model strategically prioritizes regions around the fracture site and areas with distinctive bone texture, as per the authors’ analysis of the relevance of different image regions in the classification of femur fractures. [3]. The authors of this article personally collected the X-ray images. from 1280 patients in an Indian hospital, utilizing a CNN to achieve an impressive accuracy of 98.31%. Through ablation research, the authors discerned the crucial role of convolutional layers in learning valuable features for fracture detection. This experimentation involved analyzing the contributions of different components within the model. [4]. In this study, the author employed CNN-based models, denoted as E1 and E2, incorporating transfer learning on the "MURA" dataset, attaining, respectively, test accuracies of 0.8455 and 0.8472. The model’s accuracy and robustness were exemplified by its proficient classification of various shoulder X-ray abnormalities, including acromioclavicular joint separation, rotator cuff tears, proximal humerus fractures, and glenohumeral joint osteoarthritis. The research underscores that the model excels when transfer learning is applied, using the ImageNet dataset’s pre-trained weights. This method allows the model to understand important features essential for the categorization of shoulder X-ray images. [5].

In this study, the author delves into various strategies for bone fracture classification, with a focus on X-ray images as the primary modality. Emphasizing the significance of data preprocessing and augmentation techniques, the article highlights their role in enhancing the performance of classification models.Deep learning techniques, especially Convolutional Neural Networks (CNNs), have become the standard method in recent years. The author underscores the necessity for standardized evaluation metrics and benchmark datasets to facilitate a fair comparison of diverse methods. While acknowledging the potential applications of bone fracture classification in clinical settings, the article acknowledges existing challenges and limitations. These include the demand for substantial amounts of annotated data and the complexity of detecting subtle or intricate fractures. To address these issues, the paper proposes exploring the utilization of multimodal imaging data and incorporating explainable AI techniques, aiming to enhance both the accuracy and interpretability of classification models.[6]. This paper highlights how deep learning can improve tasks in musculoskeletal radiology, including bone fracture detection, disease diagnosis, and joint segmentation. Data preprocessing and augmentation are crucial for model performance. Deep learning enhances diagnostic accuracy, reduces variability, and automates patient screening. Lack of imaging protocol standardization is a challenge. Future research could explore multiple imaging modalities, integrating clinical and imaging data, and personalized medicine in musculoskeletal radiology using deep learning. [7]. In order to investigate the application of deep learning models for the detection and classification of fractures using diverse imaging modalities, this paper performs a comprehensive review and meta-analysis of 14 studies. The combined sensitivity and specificity of these models are reported as 0.91/0.95 for fracture detection and 0.92/0.94 for fracture classification, respectively. The accuracy of these deep learning models varies depending on the type of fracture and the imaging technique employed. The potential clinical applications include enhancing diagnostic precision and minimizing variations between different observers. However, to confirm that deep learning models for orthopedic fractures accurately diagnose patients, more standardized evaluation metrics and larger studies are essential. [8]. In this article, the author proposes the utilization of a deep learning model built upon a CNN architecture. The model is specifically trained for the classification of hip fractures into stable or unstable categories and the prediction of functional outcomes based on preoperative radiographs. The dataset employed in the study comprises 374 hip fracture cases from a single institution. The model demonstrates an overall accuracy of 87.2% for fracture identification and 71.4% for functional classification.[9]. The author of this work presents an attention-based cascade R-CNN model intended for X-ray image detection of sternum fractures. The model operates in two stages, focusing on Region of Interest (ROI) detection and subsequent classification. Using a dataset comprising 380 X-ray images, the authors attained noteworthy results, including an F1-score of 0.905 and a precision of 0.947 on the test set. These results outperform those of other cutting-edge models. The findings of this research suggest promising potential for enhanced accuracy and efficiency in sternum fracture detection within clinical practice.[10].

This study recommends using a deep learning model for the identification and categorization of bone abnormalities that is based on the VGG-16 architecture in radiographic images, employing the MURA dataset. High accuracy rates for binary and multi-class classification tasks are reported by the study, distinguishing between normal and abnormal images. These findings underscore the model’s potential utility in aiding the detection and diagnosis of bone abnormalities. However, the paper acknowledges certain limitations, such as the necessity for larger and more diverse datasets, and the exploration of alternative deep learning techniques.[11]. In this study, With a mean average precision of 85.6%, the author argues in favor of using the YOLOv5 model to automatically identify bone fractures in X-ray images. This proposed approach holds promise for enhancing clinical outcomes through the accurate and efficient identification of bone fractures in X-ray data. The authors underscore the significance of automated fracture detection systems, particularly in regions with high incidence rates of bone fractures, where manual interpretation of X-ray images may be prone to errors. [12]. In this article, In order to identify arm fractures in X-rays, the author proposes an improved deep CNN that is derived from the R-CNN model. The proposed method surpasses other state-of-the-art deep learning approaches, attaining a notable average precision of 62.04%. The authors attribute this achievement to various enhancements, including the implementation of a new backbone network, image preprocessing techniques for contrast enhancement, and adjustments to the receptive field. The research showcases the potential practical applicability of the proposed method in clinical settings, aiming to enhance the efficiency and accuracy of detection of arm fracture in X-rays.[13]. In this research, the author proposes the exploration of deep learning models for various musculoskeletal disease-related tasks, including lesion detection, progression prediction, and bone age assessment. The results indicate the potential for these models to achieve high accuracy, offering possibilities for improved clinical outcomes through early detection and diagnosis, prediction of disease progression, and more precise assessments of bone age. However, the author emphasizes the necessity for additional validation and testing to assess the models’ applicability and reliability in real-world clinical scenarios. [14]. In this study, the The author suggests and illustrates the use of CNN-based models to identify cartilage lesions in knee MR images. The model underwent training and evaluation using a dataset from the Osteoarthritis Initiative (OAI) database, surpassing the diagnostic performance of radiologists with an AUC-ROC of 0.92. This suggests that deep learning methods can serve as valuable tools to assist clinicians in accurately detecting musculoskeletal abnormalities, potentially enhancing patient outcomes. But it’s important to remember that validation of the model’s performance is necessary. on larger and more diverse datasets before considering its application in clinical practice. [15]. Deep learning (DL) has emerged as a game-changing technology for medical applications in recent years, bringing about significant changes in diagnostic and treatment approaches. With its advanced neural network architectures, deep learning has particularly excelled in tasks like medical imaging for improved image segmentation and disease classification. This study specifically focuses on the Alzheimer’s disease segmentation and classification through Magnetic Resonance Imaging (MRI). The innovative approach integrates both transfer learning and customization of Convolutional Neural Networks (CNNs). The methodology involves the utilization of pre-segmented brain images, with a specific focus on the Gray Matter. The researchers use a pre-trained DL model as the initial framework and then apply transfer learning, rather than creating the model from scratch. Through assessing the model’s accuracy at various epochs (10, 25, and 50), the study achieves an outstanding overall accuracy of 97.84%. This demonstrates how their method of applying state-of-the-art deep learning techniques to advance the analysis of Alzheimer’s disease is effective [16]. In this study, a computer-aided diagnosis (CAD) system is developed using images from chest X-rays (CXRs) tailored for a specific disease. The method entails training two deep learning networks: the Long Short-Term Memory Network (LSTM) and the Convolutional Neural Network (CNN), in three phases. Initially, the CNN undergoes training with raw CXR images, subsequently pre-processed images, and lastly, using augmentation techniques, improved CXR images. The final CNN-LSTM model achieves an impressive accuracy of 99.02%, surpassing benchmark models. Notably, this approach enhances true positive rates, addressing the issue of false negatives encountered when using raw CXR images [17]. Predicting road traffic is vital for intelligent transportation systems, yet it presents challenges given diverse roads, speed fluctuations, and interdependencies among segments. In order to handle dynamic spatial dependencies, this work incorporates attention mechanisms into the Graph WaveNet model. When evaluated against the Graph WaveNet for a 60-minute prediction, On the PEMS-BAY and METR-LA datasets, the improved model showed a reduction in root-mean-square error of 3.4% and 4.76%, respectively.It is noteworthy, though, that the additional computation of attention scores resulted in an increase in the model’s training time[18]. Another study underscores the critical importance of early detection in Acute Pancreatitis (AP). This research leverages advanced machine learning techniques to replace traditional scoring methods, overcoming challenges such as small datasets and class imbalance. The incorporation of augmented datasets from sources like MIMIC-III, MIMIC-IV, and eICU enhances the training of the classifier. Effective handling of missing values is achieved through iterative imputation. The study compares the performance of downsampling on small test sets, cautioning about its potential for being misleading on larger sets. Upsampling techniques, including CTGAN, TGAN, CopulaGAN, CTAB, TVAE, and SMOTE, were explored. Among these, Random Forest demonstrated exceptional performance with an F-Beta of 0.702 and a recall of 0.833 in a 50-50 class split by CTGAN. Random Forest also exhibited strong performance on the TVAE dataset with an F-Beta of 0.698. In the case of SMOTE-based upsampling, the Deep Neural Network emerged as the top performer, achieving the best result with an F-Beta of 0.671. [19].

The steps of the methodology we applied in this study are as shown in Figure 1 take X-ray images of finger from MURA dataset, data preprocessing and data augmentation for data imbalance of classes, apply transfer learning based deep learning models, show the results in class 0 and class 1, where class 0 is negative and class 1 is positive.

The categorization of X-rays of the finger bones in this study was made easier by online servers serving as the hardware infrastructure. The ResNet and VGG deep learning models, alongside packages such as torchvision, seaborn, scikit-learn, matplotlib, and torch, were instrumental in executing the classification procedures. The cross-entropy loss function, the Adam optimizer, and a fixed learning rate of 0.0001 were employed in the procedure, spanning across 40 epochs. Notably, the initial learning rate was dynamic, undergoing a reduction by a factor of 10 every 10 epochs to enhance the learning success of the network.

The ResNet architectures, in particular ResNet34, ResNet50, ResNet101, and ResNet152, outperform VGG models in terms of test accuracy, precision, recall, and F1-Score when evaluating deep learning models for finger fracture classification. ResNet models’ reliable and powerful performance indicates their applicability for using X-ray images to automatically diagnose finger fractures. These findings can direct the choice of optimal models for practical implementation in computer aided diagnosis, highlighting the importance of model selection that prioritize both accuracy and reliability in healthcare applications. The research concludes the following findings: