Bone age comparison using convolutional neural network
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019.
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| Langue: | English |
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BRAC University
2019
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| Accès en ligne: | http://hdl.handle.net/10361/12256 |
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10361-122562022-01-26T10:20:01Z Bone age comparison using convolutional neural network Nawaz, Fariha Akib, Md. Samiul Imtiaz, Asif Ahmad, Sakib Uddin Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University Convolutional Neural Network Bone-age ResNet50 MobileNet VGG16 Neural networks (Computer science). This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 55-57). In the last few years,Machine Learning has taken the world by storm. From predictive web browsing to the email and text classi cation,from the autonomous car to facial recognition, machine learning is the main core of every intelligent application that we can see now a days. Predicting bone age is another eld that has been bene ted exceedingly from the exposure of this technology. For this reason, we have proposed convolutional neural network for predicting the age of a child and doing a comparative analysis on with other available techniques. We have choose four models for it and they are: InceptionV3, VGG16, ResNet50 and MobileNet. By pre-processing the image and selecting the various parameters the framework has been trained and tested in "RSNA Pediatric Bone Age Machine Learning Challenge" dataset. Highest accuracy of 91.13% has been achieved for MobileNet with mean absolute error of 8.87, the explained variance score for this method is 0.92 and value loss during the training is 0.0809 whereas the lowest accuracy has been achieved for VGG16 with mean absolute error 32.58,the explained variance score for this method is 0.032 and value loss during the training is 1.0281. Fariha Nawaz Md. Samiul Akib Asif Imtiaz Sakib Uddin Ahmad B. Computer Science and Engineering 2019-06-25T10:13:03Z 2019-06-25T10:13:03Z 2019 2019-04 Thesis ID 15301121 ID 15101074 ID 14301106 ID 14301086 http://hdl.handle.net/10361/12256 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 57 pages application/pdf BRAC University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Convolutional Neural Network Bone-age ResNet50 MobileNet VGG16 Neural networks (Computer science). |
| spellingShingle |
Convolutional Neural Network Bone-age ResNet50 MobileNet VGG16 Neural networks (Computer science). Nawaz, Fariha Akib, Md. Samiul Imtiaz, Asif Ahmad, Sakib Uddin Bone age comparison using convolutional neural network |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Nawaz, Fariha Akib, Md. Samiul Imtiaz, Asif Ahmad, Sakib Uddin |
| format |
Thesis |
| author |
Nawaz, Fariha Akib, Md. Samiul Imtiaz, Asif Ahmad, Sakib Uddin |
| author_sort |
Nawaz, Fariha |
| title |
Bone age comparison using convolutional neural network |
| title_short |
Bone age comparison using convolutional neural network |
| title_full |
Bone age comparison using convolutional neural network |
| title_fullStr |
Bone age comparison using convolutional neural network |
| title_full_unstemmed |
Bone age comparison using convolutional neural network |
| title_sort |
bone age comparison using convolutional neural network |
| publisher |
BRAC University |
| publishDate |
2019 |
| url |
http://hdl.handle.net/10361/12256 |
| work_keys_str_mv |
AT nawazfariha boneagecomparisonusingconvolutionalneuralnetwork AT akibmdsamiul boneagecomparisonusingconvolutionalneuralnetwork AT imtiazasif boneagecomparisonusingconvolutionalneuralnetwork AT ahmadsakibuddin boneagecomparisonusingconvolutionalneuralnetwork |
| _version_ |
1814309110243393536 |