Chakrabarty, AmitabhaNawaz, FarihaAkib, Md. SamiulImtiaz, AsifAhmad, Sakib Uddin2019-06-252019-06-2520192019-04ID 15301121ID 15101074ID 14301106ID 14301086http://hdl.handle.net/10361/12256Cataloged from PDF version of thesis.Includes bibliographical references (pages 55-57).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019.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 1.0281.57 pagesenBrac 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.Convolutional Neural NetworkBone-ageResNet50MobileNetVGG16Neural networks (Computer science).Thesis