Proceedings of the International Conference on Industrial Engineering and Operations Management
Dubai, UAE, March 10-12, 2020

Artificial Intelligence Technique For Detecting Bone
Irregularity Using Fastai
Meghna Hooda and Shravankumar Bachu
Department of Computer Science
SRM University
Delhi NCR, Haryana
hoodameghna@gmail.com, shravan2311@hotmail.com
Surjeet Dalal
Department of Computer Science
SRM University
Delhi NCR, Haryana
surjeet.d@srmuniversity.ac.in
Piya Ghosh
Department of Industrial and Management Engineering
Indian Institute of Technology
Kanpur, Uttar Pradeshpiyagh@iitk.ac.in
Abstract
A bone abnormality is a medical condition which is caused by physical damage or diseases. There are many factors
which can lead to various abnormalities. An irregularity in a bone is generally diagnosed by orthopedician andradiologists using x-ray images of the affected bone. Bone abnormalities affect more than a billion people in theworld. With more than 30 million emergency visits annually, there is a lack of expertise. Although computer aided
diagnosis is still very limited in the world. They are mostly confined to research projects and there’s been no realworld applications. For the past few years there has been a great leap in the development of Artificial Intelligence
which now makes it possible to implement and test deep learning models in the medical field. One can get anappropriate amount of data for these implementations. MURA is a database that was made available by Stanford
University for testing purposes in a target of achieving a best model for detecting bone abnormalities. Our
paper mainly focuses on the advancement in medical imaging technologies targeting the diagnosis at the level ofexperts for improving health care access. We used python, as a programming tool, and fastai, to process images andimplement the model for abnormality detection. The proposed model follows a feed forward network resulting in agood accuracy rate.

Keywords

x-ray, artificial intelligence, neural network, python, fastai

1. Introduction© IEOM Society International

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Dubai, UAE, March 10-12, 2020
In recent days the use of computer-aided diagnosis(CAD) has seen a rapid rise. With the advent of the CAD, it's now
possible to implement the power of the image processing for detection of abnormalities in x-ray images.
Musculoskeletal problems are one of the biggest problems in orthopedics. Musculoskeletal diseases include arthritis,
bursitis, tendinitis, and several others. In the short term, they cause severe pain, whereas, in the long term, the pain
gets more severe and sometimes it can even lead to disabilities. With the increase in the number of cases ofmusculoskeletal problems around the world, there is a clear rise in the number of visits to orthopedician. We improved
and analyzed the Neural model for scanning an x-ray image and then provide whether an abnormality exists or not at
an expert level. This paper's work is implemented in Python. [18] Although Python is a very old language but is still
somewhat new in the field of general software development. It is an object-oriented high-level language which isextensible, you may be able to organize your project logically even if it Python code increases. It supports shell
scripting which is helpful as it promotes code-reusability. Python is a very portable language on various platforms.
These features of Python can also be found in many other programming languages, which makes it different is its
simplicity which makes it easy to learn and use. That is the reason we are using Python as our programmingbackground. [6] Pytorch is a library generally used in machine learning models for rapid research work and analysis.
We use Pytorch to implement fastai. Fastai which was made to make deep learning more accessible. It was designed
to be used in neural networks to make the processing much easier. We decided to use fastai because of its promising
work in the application of deep learning in medicine.

2. Literature Review
With the upcoming availability of new, expensive and targeted therapies it is becoming more important to increase
the accuracy of the diagnosis of bone abnormalities like rheumatoid arthritis(RA). [17] Studies have shown thatsonography became more reliable techniques with a modern imaging technique, that detect more erosions thanradiography. To make the system more automatic some new methods came up with time. [15] CAD system is proposed
to improve the manual diagnostic analyses of x-ray images. In this type of system, Grey Level Co-occurrence Matrices
are used in which image texture are indicated statistically. This method achieves around 86% accuracy but faces some
limitations. The process starts with image processing leading to edge detection to target only the required part of the
x-ray images used, also needs noise removal to increase the accuracy of the results. Some different approaches can beseen, [7] uses residual networks for image processing for the taken dataset, resulting in analysis is achieved bybottleneck architecture. The detection is done with Pascal and ms coco. On the other hand [11] follows a recurrent
neural network (RNN) training setup for speech detection. RNN contains five layers resulting in more optimized
results.
Medical imaging machines such as x-ray or Magnetic Resonance Imaging (MRI) are used for the detection of different
types of abnormalities, mostly the diagnostic procedures depend upon human expertise, these procedures can be
automated using image processing machine learning techniques. Some related work is shown in [12] [13]. Here theimplementation of the method is also done using the MATLAB environment due to the diversity of image processing.
It uses machine learning algorithms like decision tree algorithms for the classification of the data. Neural
Network(NN) plays a major role in these types of methods to simulate the problem into layers. It is made up of threelayers i.e. input layer, hidden layer and output layer which makes it a good classifier. It works with edges propagating
signals connected to each problem segment and weight is associated with each edge, NN learns on its own by adjustingthese weights and creating an environment with an acceptable error rate.
Enhancing the work, some works are done for the detection of a particular disease or abnormality. [14] Neural Network
is used to detect abnormalities of lung cancer in an x-ray. To improve a patient's condition, the first step is to find the
problem. Using automatic detection techniques is targeted to make human's life easy but the main concern is accuracy.
[14] This paper shows the work of image pre-processing as seen in some previous works we discussed, but here only
the lungs segmentation is targeted. Their method contains ability lungs nodules in primary stages using whichclassification can be built between cancerous and non-cancerous candidate nodules. These work show theirimplementation in MATLAB for image processing techniques, binary image, erosion and image filtration. Another
implementation that can be discussed is the detection of Diabetic Retinopathy using deep learning algorithm [9] There
are among 18% of Indian with the prevalence of diabetic retinopathy. They need an ophthalmologist referral for thetreatment which can take weeks to months. The method takes a moderate amount of dataset to use deep learningalgorithms train neural networks for the dataset targeting least error occurrence and more accuracy in the detection of© IEOM Society International

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the diabetic retinopathy and diabetic macular edema in retinal fundus photographs. A related paper can be seen whichtarget a good accuracy rate for the Detection of Osteoarthritis using Knee X-Ray Image using Machine vision based
approach [8] here the dataset is limited to only 200 knee x-ray images although osteoarthritis can cartilage many bigger
joints like feet, hip, and spine. The process includes pre-processing of the images, in which images are cropped,
segmentation, which divide the image into individual parts, feature extraction, where features of segmentation andenhanced images are computed using MATLAB. This method receives a good accuracy rate of up to 87%. Some very
advanced works are done in ChestX-rays for the advancement of the automation process for abnormality detection.
[4][5][2] shows the procedures explained above. [4]Big datasets like CheXNet are used for detection using deeplearning algorithms for processing and neural networks for classification of the resulting data of multi-label disease
and then are compared with radiologist performances. The accuracy rate is effectively high but some limitations areseen like only frontal radiographs were presented and the history of the patient is provided. [3]Similar neural network
approach can also be used for hip fracture detection. This approach targets radiologists-level performance to improvise
the overall diagnosis outcome. Unlike these works, some studies are done on how large dataset can be treated. [16]shows comprehensive coverage of the image world targeting the accuracy, diversity and hierarchical structure of
ImageNet. ImageNet is a dataset containing 12 subtrees with 5247 synsets and 3.2 million images in total which is ahuge data to be worked on, but larger the dataset more will be the accuracy.
A work by Rajpurkar, P., Irvin, J. et al came to notice where MURA dataset is used [1] which is also used in our paper
also. In the mentioned paper the main model used is provided with one or more inputs which are processed by 169
layered neural networks for probability predictions. They conclude their work by comparing radiologist and theirmodel performance. Similar work can be seen in our paper but with other approaches and the target of our paper differ.
As per our knowledge, this type of implementation is not yet published.

3. MURA
Musculoskeletal Radiographs is a large data set of bone X-rays.[20] It is one of thelargest public radiographic image datasets available. This data consists of 14,863
studies from 12,173 patients. It has a total of 40,561 multi-view radiographs. It
consists of radiographs of elbow, hand, finger, forearm, humerus, shoulder and wrist.
MURA contains 9,045 normal and 5,818 abnormal musculoskeletal radiographic studiesof the upper extremity including the shoulder, humerus, elbow, forearm, wrist, hand,and finger. MURA is one of the largest public radiographic image datasets.All thestudies have been manually labeled by the certified radiologists from Stanford
Hospital in Stanford University from 2001 to 2012 [20]. MURA expect binary label y ∈{0, 1} as output pointing out whether the specified study is normal or not.[1]4. Neural Networks
Neural Networks are computer systems that are inspired by the biological Neural System. Neural Network refers to aframework rather than an algorithm, also explained in Section 2. Neural Networks consists of units, sometimes referred
to as nodes, called neurons. Each Neuron tries to mimic the functionality of a biological neuron. Artificial neuralnetwork(ANN) is a model that is designed to account for the parallel nature of a human being’s brain. In an ANN eachneuron is connected to multiple other neurons and thus forming a network. Every node in the network has an input
and an output. A neuron receives an input signal and sends the output signal to all the connected neurons in the
network. In biological neuron signals are synapses formed due to chemical reactions, whereas in ANN the signals are
just real numbers. McCulloch and Pitts are the first to create a computational model for neural networks. It's beenfurther developed and improved by Rosenblatt forming a model called the perceptron model. There has been a great
development in the field of Artificial Neural Networks in the past decade.[23] Some works can be seen using ANN in
the medical field, as in the referred paper ANN is used on Nephritis and Heart disease diagnosis data using MATLAB.
As per its analysis this work was able to classify 99% of the taken cases in the network. ANN is a very hot field in
medicine and believed to be more useful. Recurrent neural networks[11] and deep feedforward neural networks arerecent developments in the field of Neural networks, has shown great potential in solving many critical problems suchas handwriting recognition, speech recognition and natural language processing.

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Figure 1. Neural Network

4.1 Deep Neural Network
Deep Neural Networks or generally referred to as feedforward neural network or multilayer perceptron(MLPs) are apart of Neural Networks. It is based on the similar concepts used for Neural Networks and uses a cascade of multiple
layers. It uses non-linear processing units for feature extraction and transformation.[22] Each layer in the network
represents different levels of abstraction and thus forming a hierarchy of concepts. The name deep learning refers to
the use of multiple layers and data being transformed in a hierarchical fashion. In this model, each layer has its ownfunctionalities that transform its input into a slightly different and more composite form. As it is more powerful andmore abstract than most of the other models, it helps us extract features better than other models. Implementation ofdeep neural networks in image processing has a wide range of advantages as each layer can process a different part ofthe image and extract its features independently. Seen in some profound work[22] using deep neural networks to keep
the layer-to-layer transformations appears helpful for both supervised and unsupervised data.

4.3 Image Processing
Image processing is digitizing and images such that we can perform operations on it. Image processing is generally
performed to enhance or extract some kind of information from it. It is generally done by converting an image to a
multidimensional matrix where every element in the matrix represents the tuple( typically RGB values of the pixel).
It includes several steps they are mostly:● Scanning or importing an image through a scanner or by digital photography.
● Analysing the image which includes conversion to a form in which operations can be applied.
● Performing Operations and applying different algorithms to enhance and extract the required features fromthe images.
● Output the result as required( maybe altered image or extracted features.
Image processing has been rapidly increasing in the field of Machine learning. It has various applications in different
aspects of business, medical, defense, weather sensing, and art.

4.4 Cohen’s Kappa
Cohen's kappa coefficient (κ) is not a classical accuracy measure rather it's a statistic that measures inter-rater
agreement for qualitative items. It is more accurate and robust than simple accuracy measures as it takes the possibilityof an agreement by chance into account. This method includes subtracting the agreement by chance from the observed
agreement. It makes the measurement of efficiency of a model more accurate as it removes the occurrence by chancefrom the observed agreement. It generally has less significance in statistics but is widely used in various computer
programs due to its accuracy in calculation of standard error.
The definition of κ is:© IEOM Society International

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Here ,
po = Relative observed agreement among raters
pe = Hypothetical probability of chance agreement
If there is complete agreement between the observers than the. If there is complete disagreement then it would
be 0..
Weighted Kappa.
The weighted kappa allows disagreements to be weighted differently. It has three matrices in it. Weight matrix locatedon the diagonal represent agreement. The equation of the weighted kappa is given byHere,
k = number of codes
wij, xij, mi are the three matrices that represent weight, observed, and expected values.

5. ModelIn our model, we have taken the desent169 model trained by the Stanford University team on ImageNet. Then wealtered specific parameters and processes in the model. We followed a process in which we trained the model twice,once with an image of 112*112 size and next with the image of size 320*320. For every model, we trained we used
the freeze and unfreeze version that is available in the fastai. In the data augmentation, we have transformed the datawith the default get_transform() function in fastai. The default values are max_zoom =1.1 and max_lightning=0.2.
We trained our model with the inbuilt function fit_one_cycle(). We used the function lr_find() to find the best learning
rate that is decayed by factor 10 each time the validation loss plateaus after the epoch.

5.1 Architecture

Figure 2. Architecture of the model

5.2 Model PerformanceWith our model, with 320*320 image size, we got a better kappa score compared to other images. We can even seethat we had a better performance in elbow, finger, humerus compared to other x-ray images. Throughout the dataset,
we had an above par performance in most of the cases. We even did reasonably better performance than given score
but overall it's just average compared to the given model scores. As we have taken multiple values and averaged thescore of those models to get to the final conclusion. In each and every model the hyperparameters are different. We
used GPU provided by google collab with specifications© IEOM Society International

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n1-highmem-2 instance
2vCPU @ 2.2GHz
13GB RAM
Table 1. Comparison of performance between the given paper model and our trained model.

Elbow

Model from the paper
Average of Paper
Radiologists model
0.760
0.710

Our models
Image
Image
size 112 size 320
0.73
0.737

Finger

0.372

0.389

0.53

0.561

Hand

0.792

0.851

0.556

0.583

Humerus

0.844

0.600

0.673

0.788

Forearm

0.799

0.727

0.628

0.741

Shoulder

0.840

0.729

0.546

0.641

Wrist

0.884

0.931

0.632

0.711

Overall

0.757

0.705

0.614

0.674

Study

Figure 3. Kappa Score by label MURA dataset

6. Conclusion
We have able to achieve a kappa score of 0.674 with an accuracy of 0.84. It doesn't seem a lot but a lot of improvements
can be brought to the model with further increase in the number of layers and change in hyperparameters. We can seethat it's possible to build the models of deep learning in the field of health, and in particular in radiology. There has
been a great outcome from the model and it shows promise in helping radiologists diagnose diseases present in x-ray
images better. It would be possible to build a much better model if we have enough expertise in the specific field.
Having expertise in the field helps us understand the features much better and thus selection of hyper parameters mucheasier. It also helps in optimizing the images and better augmentation of data in initial phases.
We can also hear from some of the machine learning experts that it’s completely possible to replace the radiologist if
we can build a model that can predict with more accuracy, but the truth is far from that. It’s really impossible to replace
radiologists but we can surely say that we can see the use of Artificial Intelligence in every corner of the field. Recent
developments such as portable ultrasound machines will help further to push the AI into the field Radiology. As
precision is increasing along with the decrease in the price of GPUs we can expect the need for automation more thanever. When computation becomes more affordable and available we can expect many more algorithms that could teach© IEOM Society International

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itself while radiologists can judge the accuracy of it. Thus the model will get better just by working more. The main
challenge that will be faced is with data. Data available on the internet, with proper labels, is very limited. Somemodels may have the requirement of thousands to millions of images to get the target accuracy. The other issues
include the cost of error. As most of the models are dealing with very sensitive information the wrong prediction may
result in severe outcomes. There will be resistance even from doctors as well as hospitals, we need to convince themthat AI algorithms can work. All in all, with an increase in research in this field we can definitely say that AI will
revolutionize the field of radiology in the long run. I also see the pace of research can be much accelerated if we canget a hand from radiologists too. So, it is one of those things which we need to keep forefront.

References
Rajpurkar, Pranav, et al. "Mura: Large dataset for abnormality detection in musculoskeletal radiographs." arXiv
preprint arXiv:1712.06957 (2017).
[2] Islam, Mohammad Tariqul, et al. "Abnormality detection and localization in chest x-rays using deep convolutional
neural networks." arXiv preprint arXiv:1705.09850 (2017).
[3] Gale, William, et al. "Detecting hip fractures with radiologist-level performance using deep neural networks."
arXiv preprint arXiv:1711.06504 (2017).
[4] Rajpurkar, Pranav, et al. "Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning."
arXiv preprint arXiv:1711.05225 (2017).
[5] Wang, Xiaosong, et al. "Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervisedclassification and localization of common thorax diseases." Proceedings of the IEEE conference on computer
vision and pattern recognition. 2017.
[6] Paszke, Adam, et al. "Automatic differentiation in pytorch." (2017).
[7] He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on
computer vision and pattern recognition. 2016.
[8] Gornale, Shivanand S., Pooja U. Patravali, and Ramesh R. Manza. "Detection of osteoarthritis using knee X-ray
image analyses: a machine vision based approach." International Journal of Computer Applications 145.1 (2016).
[9] Gulshan, Varun, et al. "Development and validation of a deep learning algorithm for detection of diabetic
retinopathy in retinal fundus photographs." Jama 316.22 (2016): 2402-2410.
[10] Anu, T. C., and R. Raman. "Detection of bone fracture using image processing methods." International Journalof computer applications 975 (2015): 8887.
[11] Hannun, Awni, et al. "Deep speech: Scaling up end-to-end speech recognition." arXiv preprint arXiv:1412.5567(2014).
[12] Al-Ayyoub, Mahmoud, and Duha Al-Zghool. "Determining the type of long bone fractures in x-ray images."
WSEAS Transactions on Information Science and Applications 10.8 (2013): 261-270.
[13] Al-Ayyoub, Mahmoud, Ismail Hmeidi, and Haya Rababah. "Detecting Hand Bone Fractures in X-Ray Images."
JMPT 4.3 (2013): 155-168.
[14] Kumar, Vinod, and Dr Kanwal Garg. "Neural Network Based Approach for Detection of Abnormal Regions ofLung Cancer in X-Ray Image." International Journal of Engineering Research & Technology, ISSN (2012): 22780181.
[15] Chai, Hum Yan, et al. "Gray-level co-occurrence matrix bone fracture detection." WSEAS Transactions on
Systems 10.1 (2011): 7-16..
[16] Deng, Jia, et al. "Imagenet: A large-scale hierarchical image database." 2009 IEEE conference on computer visionand pattern recognition. Ieee, 2009.
[17] Wakefield, Richard J., et al. "The value of sonography in the detection of bone erosions in patients withrheumatoid arthritis: a comparison with conventional radiography." Arthritis & Rheumatism 43.12 (2000): 27622770.
[18] SzkeWesley J. Chun (1st edition december 2000), Core Python Programming, Prentice Hall PTR, Upper Saddle
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[1]© IEOM Society International

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Dubai, UAE, March 10-12, 2020[20] MURA

Dataset: Towards Radiologist- Level Abnormality Detecion in Muscloskeletal Radiographs,
https://stanfordmlgroup.github.io/competitions/mura/[21] Fastai,the new radiology tool, https://medium.com/@pierre_guillou/fastai-the-new-radiology-tool76f02c1e25bf[22] Glorot, Xavier, and Yoshua Bengio. "Understanding the difficulty of training deep feedforward neural networks."
Proceedings of the thirteenth international conference on artificial intelligence and statistics. 2010.
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Biographies
Meghna Hooda and Shravankumar Bachu are currently a final year undergraduate student of SRM University,
Delhi NCR, India.
Surjeet Dalal is an Associate Professor at SRM University, Delhi NCR. Dr. Surjeet Dalal received his Ph.D. Degreein 2014 from Suresh Gyan Vihar University Jaipur (Rajasthan) and M.Tech Degree in 2010 from PDM College ofEngineering, Bahadurgarh, Haryana. He has completed B.Tech (Computer Science &amp; Engineering) from Jind
Institute of Engineering & amp; Technology Jind (Haryana) in 2005. He has more than nine years of teachingexperience in various colleges under Kurukshetra University Kurukshetra. His current research area is Artificial
Intelligence, Multi-agent system, Case-based reasoning and Cloud Computing. He has presented more than twenty
papers in the national/international conferences. He has published more than thirty papers in national and international
journals.He is the professional member of various professional and research committees. He is a professional memberof CSI India, IEEE New York, IETE Chandigarh and ISTE-AICTE New Delhi.
Piya Ghosh is currently a full-time research scholar at Indian Institute of Technology Kanpur. Ms. Ghosh holds a
Bachelor of Technology degree in Computer Science and Engineering from West Bengal University of Technological,
Kolkata and a Master of Technology in Operations Research from National Institute of Technology, Durgapur. Her
interest areas include Operations Research and Operations Management.

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