3. ABSTRACT
X-Ray is one the oldest and frequently used devices, that makes images of any
bone in the body, including the hand, wrist, arm, elbow, shoulder, foot, ankle,
leg (shin), knee, thigh, hip, pelvis or spine.
A typical bone ailment is the fracture, which occurs when bone cannot
withstand outside force like direct blows, twisting injuries and falls.
Automatic detection of fractures in bone x-ray images is considered important,
as humans are prone to miss-diagnosis.
4. INTRODUCTION
In today's revolution oriented medical environment, computer aided disease
detection play a vital role in a wide range of applications and services in day-to-
day activities.
The most important quality goal of such a system is speed and high accuracy in
disease detection.
This is more important in radiology department, where hundreds and hundreds of
images are to be examined for various ailments including bone fracture, infection
and location of foreign objects.
5. PROPOSED SYSTEM
The accuracy of such an ensemble model depends on several factors like
(i) Number and type of classifiers
(ii) Feature used
(iii) Partitioning method (Training and Testing sets)
(iv) The aggregation method and
(v) Type of training.
A successful ensembling depends on the design of the individual classifiers, selection
of classifier and design of the fusion mechanism. Studies reported have shown that
ensembling classification is effective only when the individual classifiers selected
exhibit two important criterions, namely, high accuracy (or low error rate) and
diversity between classifiers (make different error rate).
6. This section includes data collection, augmentation of data using
transformations of the image and finally classification of healthy and
cancerous bone using deep CNN.
The experiment has been performed on the bone X-ray image data sets,
collected from different sources publicly available for research such as the
Cancer Imaging Archive(TCIA) and Indian Institute of Engineering Science
and Technology, Shibpur (IIEST).The fracture and the healthy bone is
shown in figure1.
7.
8. REQUIREMENTS
HARDWARE RESOURCES REQUIRED
System Processors : Core2Duo.
Hard Disk : 150 GB.
Speed : 2.4 GHZ
SOFTWARE RESOURCES REQUIRED
Operating system : 32bitWindows 7 and on words .
Coding Language : Python
IDE : Spider
Database : MYSQL
9. CONCLUSION
In this paper bone fracture detection and classification system using deep
learning technique has been developed.
The X-ray image of the human fracture bone and the healthy bone were used to
perform the experiment. The original 100 images were collected from the
different source.
The data setwas augmented to overcome the over fitting problem in the deep
learning on the small data set. Finally, the size of the data set was set to 4000.
The classification accuracy of the model is 92.44% for the healthy and the
fractured bone.
10. REFRANCES
“[1] R.C. Gonzalez, R.E woods and S.L Eddins “Image Restoration and Reconstruction”, In Digital
Image Processing Using MATLAB, second edition, Gatemark, LLC, 2009.”
“[2] A.K. Pujari “Other Techniques” In Datamining Techniques, second edition, University Press,
2010.”
“[3] M.R. Ogiela and T. Hachaj “Image Processing, Pattern Recognition, and Semantic
Understanding Techniques” In Natural User Interface in Medical image analysis, Springer, 2015.”
“[4] P.P Acharjya, R. Das & D. Ghoshal “Study and Comparison of Different Edge Detectors for
Image Segmentation” IJCSI International Journal of Computer Science Issues, Vol. 12, No. 13,
Version 1.0 2012.”
“[5] W. Gao, L. Yang, X. Zhang, H. Liu “An improved Sobel edge detection”, Computer Science and
Information Technology (ICCSIT), 2010 3rd IEEE International Conference, China, Vol. 5, pp. 67 –
71, 2010.”
