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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 09 Issue: 04 | Apr 2022

p-ISSN: 2395-0072

www.irjet.net

Bone Age Estimation for Investigational Analysis Rahul Akhade1, Jill Chawhan2, Arya Dhanorkar3, Dr. Jayashree Khanapuri4 1,2,3Student,

Dept. of Electronics and Telecommunications Engineering, KJSIEIT, Sion, Mumbai Dept. of Electronics and Telecommunications Engineering, KJSIEIT, Sion, Mumbai ---------------------------------------------------------------------***--------------------------------------------------------------------4HOD,

Abstract - Machine learning has slowly and steadily secured

In India, a large portion of the rural population is illiterate, and many people lack government ID credentials, resulting in a lack of information in the government database. It is exceedingly difficult to determine the age of a victim who does not have government ID documents at a crime scene, thus our bone age estimation system will assist in determining the age quickly. This model can be used by the forensics department during post-mortem examination.

its way in our day to day lives. Similarly, there is a great scope for it to go hand in hand with medical science. The proposed paper paves a way for the same. Machine learning can curb the inconsistencies in medical science caused due to human error. Machine learning can be applied in many disciplines of medical science to enhance the user experience. A detailed approach to create a Bone age detection model is proposed in this paper. Bone age detection is an application used in forensic analysis to estimate the victim’s age, whose age is more than 18 years. In this model, Xception architecture is used for transfer learning. Neural networks are custom trained using transfer learning. The dataset used for training the model contains around 200 pelvis bone images of different age groups and was obtained from various hospitals and radiologists across the country. The aim is to achieve as little Mean Average Error (MAE) as possible in the subjects. The final MAE obtained using Xception architecture is 12.352 years.

2. LITERATURE SURVEY Bone age of adults is determined manually using Risser sign and iliac crest ossification process which is an indirect measure of skeletal maturity and shows inaccuracy. Artificial intelligence is applied in overcoming such inconsistencies in results. Convolutional Neural Networks (CNN) is used on pelvis X-rays for subjects above 18 years of age. Earlier, Tanner-Whitehouse (TW3) or the Greulich and Pyle (G&P) method was used to estimate bone age manually. These processes are laborious and flawed due to the involvement of an excessive number of steps. Thus machine learning was introduced to ease the age detection model. S. Son[1] employed Visual Geometry Group (VGGNet) architecture to automate the TW3 and G&P approaches of the bone age assessment system, based on 13 regions of interest of the left hand. This approach gives an accuracy of 97.6% of the age group 2-18 years.

Key Words: MAE (Mean Average Error), CLAHE (Contrast Limited Adaptive Histogram Equalization), CNN (Convolutional Neural Networks), Xception Architecture.

1. INTRODUCTION Radiologists and clinicians currently require a significant amount of time and experience to forecast a person's bone age from a pelvic X-ray. The automated bone age assessment system will save time and may be utilized by anyone without any prior knowledge of bone age prediction. The main idea is to use a pelvis X-ray to automatically anticipate a person's bone age (over 18 years) by focusing on the pubic symphysis area, which can be observed in the centre of the pelvis. This can be accomplished by utilizing deep learning neural networks to predict a person's age, and the accuracy of the prediction can be increased using CNN and Transfer Learning techniques. Tanner Whitehouse techniques are used by doctors to forecast the age of their patients.

Another approach proposed by K. Panday[2] uses Xrays and ultrasonography of the pelvis for manual bone age assessment. Risser’s staging system is used in the assessment of bone age of the age group 12-21 years, with an accuracy of 43% Y. Li[3] have approached the use of AlexNet architecture by focusing on the iliac crest apophysis of the pelvis of the age group 10-25 years. This model provides an accuracy of ±1.5 years. N. Poojary[4] have suggested an approach based on Xception architecture which uses X-rays of the left hand of the age group below 18 years. Contrast Limited Adaptive Histogram Equalization (CLAHE) is used to enhance the Xrays, hence aggrandizing the accuracy. This system achieves mean average error (MAE) of 8.175 months.

Predicting age is a difficult operation that takes a long time. As a result, libraries such as 'keras,' are employed which aid in the training of the dataset, as well as the CLAHE approach, which aids in the enhancement of the most significant areas of the x-rays and provides definitive findings.

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