Enhanced Bone Fracture Detection and Quantiﬁcation in X-Ray Images

Using Deep Learning

Aman Kshetri

, Raj Sah Rauniyar

and S S Chakravarthi

Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, India

Keywords:

Bone Fracture Detection, X-Ray Imaging, YOLO, Radiologists, Deep Learning.

Abstract:

Bone fracture detection in X-ray imaging is an essential diagnostic process, yet it often requires specialized ex-

pertise that may be limited in under-resourced healthcare settings. In major hospitals, experienced radiologists

typically interpret X-rays with high accuracy. However, in smaller facilities within underdeveloped regions,

less experienced medical personnel may struggle to provide accurate readings, leading to a signiﬁcant rate of

misinterpretation, currently reported at 26%. While numerous studies have focused on localizing fractures,

few address the need for quantifying the length of the fractured bone segment, a critical factor in treatment

planning. This project aims to develop an advanced deep learning model using the YOLO architecture to

enhance bone fracture detection and quan-tiﬁcation in X-ray images. By automating fracture detection and

accurately measuring fracture length, the YOLO-based model will improve diagnostic accuracy, reduce radi-

ologist workload, and ensure consistent assessments across diverse healthcare environments. The objectives

include designing robust algorithms for fracture localization and length measurement, achieving high preci-

sion in fracture detection, and validating the model against a comprehensive X-ray dataset. Ultimately, this

tool is expected to provide valuable diagnostic aid, particularly in settings with limited radiological resources,

improving patient outcomes through reliable, automated fracture analysis.

1 INTRODUCTION

In hospital emergency rooms, radiologists frequently

examine patients having fractures in various body

parts like the wrist, arm, or leg. Fractures, which

are disruptions in bone continuity, are typically classi-

ﬁed into two categories: open and closed. Open frac-

tures involve the bone piercing the skin, while closed

fractures occur when the bone is broken without

breaching the skin’s surface. Accurate identiﬁcation

and classiﬁcation of fractures are essential for proper

treatment planning, often necessitating surgery. Prior

to surgical intervention, surgeons must thoroughly as-

sess a patient’s medical history and conduct a com-

prehensive checkup & understand the fracture’s com-

plexity. In the latest medical image advancements,

three primary modalities are widely used to diag-

nose fractures: X-ray, Magnetic Resonance Imag-

ing (MRI), and Computed Tomography (CT). Among

these, X-ray imaging remains the most commonly uti-

https://orcid.org/0009-0000-2037-522X

https://orcid.org/0009-0002-5887-0132

https://orcid.org/0000-0002-8373-7264

lized method due to its cost-effectiveness, availability,

and speed, making it a primary diagnostic tool, espe-

cially in emergency settings. X-ray imaging plays a

crucial role in diagnosing fractures, such as distal ra-

dius and ulna fractures, which are prevalent in pedi-

atric patients and account for a signiﬁcant portion of

wrist trauma cases.

With the rapid advancements in deep learning,

neural network-based models have emerged as pow-

erful tools for medical image processing. Deep learn-

ing’s capacity to analyze complex data patterns makes

it particularly suitable for ap- plications like fracture

detection, a growing research focus within the ﬁeld of

computer vision. Object detection models, a subdo-

main of deep learning, have shown promising results

in fracture detection, enabling real-time identiﬁcation

and localization of fractures within medical images.

Deep learning object detection techniques are gener-

ally categorized into two-stage and one-stage algo-

rithms. Two-stage algorithms, such as Region-based

Convolutional Neural Networks (R- CNN) and their

advanced iterations, generate both location and class

probabilities through a sequential two-stage process.

This results in highly accurate outcomes but often re-

Kshetri, A., Sah Rauniyar, R. and S Chakravarthi, S.

Enhanced Bone Fracture Detection and Quantiﬁcation in X-Ray Images Using Deep Learning.

DOI: 10.5220/0013586900004664

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 2, pages 80-86

ISBN: 978-989-758-763-4

quires extended processing times, making them less

ideal for real-time applications.

In this project, we employ the YOLOv8 model,

the latest iteration of the YOLO series, to advance

bone fracture detection in X-ray images. Our ap-

proach is designed to address two primary goals:

identifying the location of the fracture and quantify-

ing the fracture length, an aspect rarely explored in

existing research. By training the YOLOv8 model on

a diverse dataset, we aim to develop a robust, an ef-

ﬁcient solution for fracture detection that can be de-

ployed across various healthcare settings, from well-

resourced hospitals to under-resourced clinics. We

further enhance model performance through data aug-

mentation techniques, optimizing the YOLOv8 algo-

rithm for pediatric wrist fractures.

Through experimental comparison, we assess the

YOLOv8 model’s perfor- mance against YOLOv7

and its improved variants, using mean average preci-

sion (mAP 50) as the evaluation metric. Our ﬁndings

demonstrate that YOLOv8, when trained with tailored

data augmentation strategies, achieves the highest

mAP 50 score, underscoring its efﬁcacy in accurately

detecting and quantifying fractures. This project, by

automating fracture detection and measurement, has

the poten- tial to alleviate radiologists’ workloads, en-

sure consistent diagnostic outcomes, and improve pa-

tient care across diverse healthcare settings, particu-

larly in areas where access to radiology expertise is

limited.

2 RELATED WORKS

This ﬁeld has experienced signiﬁcant growth, partic-

ularly in leveraging deep learning techniques to en-

hance medical image analysis. Recent advancements

focus on improving the accuracy and reliability of

bone fracture detection and quantiﬁcation in X-ray

images. Much of the earlier research serves as a foun-

dation for developing modern approaches, such as the

method presented in this study, which helps in deliv-

ering enhanced diagnostic performance.

(A. Saad, 2023) developed a convolutional neural

network (CNN) using Keras to detect fractures in X-

ray images. The model was trained and augmented

on a dataset of 9,103 X-ray images, to improve the

diversity and robustness of the training. The CNN

model achieved a high accuracy of 91%, with pre-

cision and recall rates of 89.5% and 87%, respec-

tively, largely due to the data augmentation. While

this accuracy places it above several other methods,

the study notes a risk of false positives, suggesting

further reﬁnements to make it suitable for clinical set-

tings. (Kalb and Harris, 2021) The dataset consisted

of X-rays classiﬁed as fractured and non-fractured,

enhanced through augmentation techniques. The re-

sults are promising, with the model showing signif-

icant accuracy; however, the research highlights the

need for comparisons with other models to ensure

consistency and reduce the rate of false positives.

(Zou and Arshad, 2022) explored the performance

of YOLO variants and two-stage models for frac-

ture detection, emphasizing Enhanced Intersection

over Union (EIoU) to improve bounding box preci-

sion. The study found that the YOLOv7-ATT model

achieved a mean average precision (mAP) of 80.2%

and 86.2% on the FracAtlas dataset, outperform-

ing other models in terms of precision and recall.

(M. Salimi, 2022) While YOLOv7-ATT stood out,

the research also revealed that other two-stage models

and SSD performed suboptimally, and additional en-

hancements are still needed for further accuracy im-

provements. (T. Gruber, 2022) The dataset included

annotated images representing four types of fractures

and was evaluated using precision, recall, mAP, and

IoU. Overall, the YOLOv7-ATT model demonstrated

that single-stage models generally surpass two-stage

models in terms of both speed and detection accuracy.

(J. Li, 2021) employed DenseNet-201, a deep

learning model that was trained on 1,370 X-ray

images, with preprocessing and data augmentation

methods applied to enhance its accuracy. The model’s

performance was measured by metrics like accuracy,

sensitivity, AUC and speciﬁcity, where it achieved

94.1% accuracy and an AUC of 98.7%. The model

also demonstrated high sensitivity and speciﬁcity

rates, with sensitivity at 93.2% and speciﬁcity at

94.8%. (M. Oppenheimer, 2021) However, the study

notes that further clinical validation is necessary to

ensure its reliability for widespread clinical use. The

dataset focused on pediatric elbow fractures, provid-

ing a specialized area for evaluation. DenseNet-201’s

promising results indicate its high diagnostic poten-

tial, especially for pediatric fractures, though broader

testing is recommended.

(Riska, 2022) investigated the application of a De-

cision Tree classiﬁer on 4,083 X-ray images, utiliz-

ing Canny edge detection and Hu Moments for ef-

fective feature extraction. The model was validated

through 5-fold cross-validation, achieving a moderate

accuracy range of 69.89% to 74.05%, with balanced

performance metrics across evaluations. (R. Hruby,

2023) Although the classiﬁer provided a reliable base

for fracture detection, the study highlights variability

in performance, suggesting that advanced algorithms

and optimization are required to enhance accuracy.

The dataset used consisted of labeled X-ray images

Enhanced Bone Fracture Detection and Quantiﬁcation in X-Ray Images Using Deep Learning

processed for feature extraction, creating a foundation

for further studies to develop more sophisticated algo-

rithms.

(A. Gal

an-Cuenca, 2022) employed Siamese net-

works and techniques like weighted loss and balanced

sampling to improve few-shot learning. The Siamese

network was speciﬁcally tailored to address class im-

balance, showing a potential gain of up to 5.6% in

F1-score. (Gupta and Singh, 2022) While the model

effectively managed imbalanced data, performance

still varied depending on the dataset and architecture

selection, underscoring the importance of choosing

the right techniques for each scenario. (Ju and Cai,

2023) The study used three chest X-ray datasets la-

beled for COVID-19, providing a range of challenges

for the model. Findings suggest that Siamese net-

works are superior to CNNs in handling imbalanced

data, though further exploration of other architectures

could yield improved outcomes.

In summary, prior research has laid the ground-

work for advancements in bone fracture detection us-

ing deep learning. (T. Mukherjee, 2023) Limitations

of traditional methods, coupled with the potential of

modern neural networks, highlight the need for im-

proved approaches. Our proposed framework builds

on this foundation by integrating advanced techniques

to enhance accuracy, reliability, and interpretability

in detecting and quantifying fractures from X-ray im-

ages.

3 METHODOLOGY

3.1 Proposed Method

In this section, we describe the steps involved in data

pre-processing, training, validating, and testing the

model on the dataset, as well as the YOLOv8 model

architecture. The GRAZPEDWRI-DX dataset, com-

prising 20,327 X-ray images, is divided into train-

ing, validation, and test sets. To enhance the train-

ing set, data augmentation is employed, increasing

the previous 14,000 X-ray images to 28,408 images.

The model design and architecture are based on the

YOLOv8 algorithm, as illustrated in Figure 1.

3.2 Data Preprocessing

Data preprocessing is a critical step in ensuring the

effectiveness of the YOLOv8 model for bone fracture

detection. This phase involves multiple stages to re-

ﬁne raw X-ray images into a form suitable for efﬁcient

learning and robust detection.

3.2.1 Image Cleaning

The initial step involves enhancing the quality of in-

put X-ray images by removing noise and artifacts that

may obscure fracture regions. Noise reduction and ar-

tifact removal improve the clarity and consistency of

the dataset. The process can be expressed as:

clean

= I − Artifacts(I) (1)

Where I is the raw input image, and Artifacts(I)

represent unwanted elements removed using:

• Median Filtering: A non-linear ﬁltering tech-

nique that reduces noise while preserving edges.

• Morphological Operations: Techniques like

erosion and dilation eliminate small irrelevant

structures.

• Contrast Enhancement: Adjusts the intensity

levels to improve visibility of fractures.

3.2.2 Resizing and Scaling

Images are resized to a standard resolution W ×

H to ensure uniformity and compatibility with the

YOLOv8 model while retaining essential structural

information:

resized

= Resize(I

clean

, W, H) (2)

Resizing reduces computational overhead and en-

sures consistent feature extraction across samples.

3.2.3 Data Augmentation

To improve the model’s generalization ability, data

augmentation introduces variability into the training

dataset by applying random transformations. Given

a resized image I

resized

, augmentation produces new

variants:

augmented

= Augment(I

resized

) (3)

Common augmentation techniques include:

• Rotation and Flipping: Simulates different ori-

entations of X-rays.

• Zooming: Focuses on speciﬁc regions to high-

light ﬁne details.

• Brightness and Contrast Adjustments: Ac-

counts for varying imaging conditions.

• Random Cropping and Padding: Enhances ro-

bustness to partial views of fractures.

INCOFT 2025 - International Conference on Futuristic Technology

3.2.4 Image Normalization

Normalization standardizes the pixel values in images

to fall within a consistent range, usually between 0

and 1. This helps minimize sensitivity to changes in

lighting conditions.

normalized

augmented

− µ

(4)

Where µ and σ are the mean and standard devi-

ation of the pixel intensities, respectively. This step

accelerates convergence during training.

3.2.5 Annotation and Labeling

Annotations deﬁne the ground truth for supervised

learning. X-ray images are labeled with bounding

boxes around fracture regions:

B = Annotate(I

normalized

) (5)

Annotations include:

• Bounding Boxes: Highlight fracture locations.

• Class Labels: Indicate fracture or non-fracture.

• Conﬁdence Scores: Quantify the certainty of

each label.

The YOLOv8 architecture consists of three main

components: Backbone, Neck, and Head, each con-

tributing uniquely to the model’s ability to detect frac-

tures with high accuracy.

3.3 Model Architecture

3.3.1 Backbone

The Backbone extracts hierarchical features from X-

ray images, progressively capturing complex patterns

through convolutional layers:

= Conv(I

X-ray

) (6)

Where P

represents multi-scale feature maps

(e.g., P

, P

, . . . , P

• Lower layers (P

and P

): Capture ﬁne-grained

details such as fracture edges.

• Higher layers (P

to P

): Identify global structures

and contextual patterns.

3.3.2 Neck

The Neck fuses multi-scale features to enhance the

detection of fractures of varying sizes. It employs up-

sampling and C2f blocks to combine coarse and ﬁne

details:

F = C2f(P

) + Upsample(P

) (7)

Key operations include:

• Feature Pyramid Network (FPN): Integrates

features across scales.

• Cross-Stage Partial (CSP) Networks: Improve

efﬁciency by reusing features.

3.3.3 Head

The Head generates predictions for bounding boxes,

class labels, and conﬁdence scores. It uses regression

and classiﬁcation techniques:

B = Detect(F) (8)

Predictions are made at three scales (P3, P4, P5)

to handle objects of varying dimensions.

3.3.4 Loss Function

The loss function optimizes the model for accurate

detection and classiﬁcation. It combines:

L = L

bbox

+ L

cls

+ L

obj

(9)

Where:

• L

bbox

: Bounding box regression loss (CIoU +

DFL).

• L

cls

: Classiﬁcation loss (Binary Cross-Entropy).

• L

obj

: Objectness loss (conﬁdence score adjust-

ment).

3.4 Training Process

The model is trained iteratively to minimize the loss

function and improve fracture detection. Training in-

volves:

• Forward Pass: Processes images to compute pre-

dictions.

• Loss Computation: Calculates the discrepancy

between predictions and ground truth.

• Backward Pass: Updates model weights using

backpropagation.

Enhanced Bone Fracture Detection and Quantiﬁcation in X-Ray Images Using Deep Learning

Figure 1: YOLOv8 Architecture

3.4.1 Iterative Optimization

Each training iteration reﬁnes the Backbone, Neck,

and Head:

• Backbone: Enhances hierarchical feature extrac-

tion.

• Neck: Improves feature fusion.

• Head: Reﬁnes bounding box predictions and class

scores.

3.4.2 Evaluation Metrics

The model’s performance is validated using metrics

such as:

• Precision and Recall: Measure accuracy in iden-

tifying fractures.

• mAP@50: Evaluates the quality of bounding box

predictions.

• IoU: Assesses the overlap between predicted and

ground truth boxes.

3.4.3 Validation Strategy

A separate validation set ensures generalizability by

tracking loss reduction and metric improvement over

epochs.

4 RESULTS

The proposed YOLOv8-based model for bone frac-

ture detection demonstrates superior performance

compared to earlier models in the domain of medical

image analysis. This section presents the results of

the model’s evaluation on standard metrics, including

mAP@50, Precision, Recall, and IoU, while also pro-

viding visual comparisons of its performance in de-

tecting fractures across diverse X-ray images.

4.1 Performance Metrics

The confusion matrix outlines the true positives, false

positives, true negatives, and false negatives. The

model achieves a high True Positive Rate (TPR),

reﬂecting its effectiveness in accurately identifying

fracture regions. Table 1 summarizes the evaluation

metrics:

The model’s mAP@50 score of 93.2% indicates

its superior ability to localize and classify fractures

accurately, outperforming earlier approaches. Addi-

tionally, a Precision of 92.3% ensures minimal false

positives, while a Recall of 89.7% highlights the

model’s capacity to detect nearly all fractures.

Metric Score

Precision 92.3%

Recall 89.7%

mAP@50 93.2%

IoU 0.87

Table 1: Performance Metrics of the YOLOv8 Model

4.2 Training and Validation

Performance

The convergence of training and validation metrics is

illustrated in Figure 2, showing the model’s consistent

improvement over epochs. Both training and valida-

tion loss decrease steadily, with Precision and Recall

improving throughout the process.

The graph reﬂects the stability and robustness of

the YOLOv8 architecture in learning from the dataset,

ensuring accurate predictions while avoiding overﬁt-

ting.

4.3 Visual Results

This provides examples of the bounding box predic-

tions generated by the YOLOv8 model on X-ray im-

INCOFT 2025 - International Conference on Futuristic Technology

Figure 2: Training vs. Validation Performance Metrics

ages. The model accurately localizes fracture regions

with high conﬁdence scores, showcasing its ability to

handle variations in image quality and fracture types.

These results demonstrate the model’s potential

for deployment in real-world clinical settings, where

it can assist radiologists by automating fracture detec-

tion and reducing diagnostic workloads.

Figure 3: Predicted Wrist Fracture Images

It presents the predicted results generated by our

model. The ﬁndings indicate that the model performs

well in detecting single fractures. However, its accu-

racy is signiﬁcantly impacted in cases involving metal

punctures or densely overlapping multiple fractures.

4.4 Discussion

The results conﬁrm that the proposed model excels in

terms of both accuracy and efﬁciency. Compared to

traditional approaches, the YOLOv8 model provides:

• Higher Precision and Recall: Ensures reliable

detection of fractures with minimal false posi-

tives.

• Improved IoU: Indicates precise localization of

fracture regions.

• Faster Inference Time: Suitable for real-time ap-

plications in clinical settings.

The results underscore the model’s ability to per-

form robustly across diverse datasets, making it a re-

liable tool for fracture detection in under-resourced

healthcare facilities.

5 CONCLUSION

This research introduced an advanced framework in

Bone Fracture Detection establishing a new standard

in medical diagnostics. By leveraging state-of-the-art

convolutional neural networks (CNNs) and advanced

architectures like YOLOv8, the framework addressed

critical challenges in detecting and classifying bone

fractures. Its ability to combine high-speed process-

ing with exceptional accuracy ensures a signiﬁcant

improvement over traditional image processing meth-

ods, making it a valuable tool for healthcare practi-

tioners.

The integration of YOLOv8 signiﬁcantly en-

hanced fracture localization and classiﬁcation by en-

abling real-time, accurate detection, which is es-

pecially beneﬁcial in emergency medical scenarios

where quick decision-making is critical. (Meena and

Roy, 2022) Additionally, to address class imbalance,

the framework utilized data augmentation and over-

sampling techniques, ensuring balanced predictions

across various fracture types. This approach miti-

gated biases commonly seen in traditional methods,

improving diagnostic accuracy for simple, complex,

and comminuted fractures. (Rosenberg and Cina,

2023) By combining real-time detection with bal-

anced classiﬁcation, the framework delivers reliable

and consistent results, making it a robust tool for prac-

tical deployment in clinical settings.

In conclusion, the framework shows major

progress in the area of medical diagnosis. By com-

bining state-of-the-art deep learning techniques with

practical clinical applications, it delivers a robust, ef-

ﬁcient, and accurate solution for fracture detection.

The framework’s scalability, cost-effectiveness, and

exceptional performance metrics underscore its po-

tential to revolutionize medical imaging and foster

better patient outcomes. This work highlights AI’s

transformative role in healthcare and sets a bench-

mark for future developments in the domain.

Enhanced Bone Fracture Detection and Quantiﬁcation in X-Ray Images Using Deep Learning

6 FUTURE SCOPE

The future work for the proposed deep learning

framework in enhanced bone fracture detection and

quantiﬁcation focuses on broadening its functionality

and expanding its applicability across diverse medical

domains. As deep learning models evolve, so too will

the ability to detect fractures with increased precision,

offering more nuanced insights that directly inﬂuence

treatment planning and patient care.

A signiﬁcant direction for future development is

the integration of fracture quantiﬁcation into the sys-

tem. While current models can detect fractures and

classify their types, the next step is to incorporate the

ability to evaluate the severity, size, and orientation of

the fractures. This level of detail is crucial for more

effective treatment planning, as it allows medical pro-

fessionals to assess the fracture’s potential impact on

bone healing, decide on the most appropriate inter-

ventions, and monitor recovery progress with greater

accuracy. (S. C. Shelmerdiner, 2022) By combin-

ing fracture detection with quantitative analysis, the

system can help guide decisions regarding whether

a fracture requires surgical intervention, casting, or

other treatments.

Enhancing the model with larger, diverse datasets

such as GRAZPEDWRI-DX can improve its accuracy

and robustness by exposing it to a broader spectrum

of fracture types, imaging conditions, and anatomi-

cal variations. This would help the system recog-

nize subtle fracture patterns that traditional methods

might overlook, improving its generalizability across

different patient demographics and medical settings.

(D. Velychko, 2021) Additionally, integrating mul-

timodal imaging data, such as CT and MRI scans,

alongside X-rays, could provide a more comprehen-

sive diagnostic tool. CT scans offer detailed 3D views

of bone structures, while MRI scans excel at visu-

alizing soft tissues, allowing for better detection of

complex or multi-fracture cases. A multimodal deep-

learning framework would not only enhance fracture

identiﬁcation but also aid in assessing associated soft

tissue damage, crucial for comprehensive injury anal-

ysis.

These advancements hold the potential to revo-

lutionize diagnostic tools in medical imaging, im-

proving the speed and accuracy of fracture detec-

tion while enhancing the clinician’s ability to treat

fractures more effectively. By continuously improv-

ing the framework’s capabilities—whether through

deeper integration with multimodal data, better han-

dling of specialized datasets, or faster real-time feed-

back—the system will ultimately contribute to bet-

ter patient outcomes and more efﬁcient clinical work-

ﬂows. As research progresses, this framework could

serve as a foundational technology, setting a new stan-

dard for the role of AI in healthcare and inspiring fur-

ther innovations in the ﬁeld.

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