Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/470523
Title: Automatic detection of humerus bone fracture from x ray images using transfer learning approach
Researcher: Sasidhar, A
Guide(s): Thanabal , M S
Keywords: Engineering and Technology
Engineering
Engineering Multidisciplinary
Transfer learning
X ray images
Bone fracture
University: Anna University
Completed Date: 2022
Abstract: Identification of bone fractures with computer aided detection and newlinediagnosis is an utmost need of today. It helps the radiologists in saving the time newlineand improving the performance. There were many image processing techniques newlineused earlier for detecting the bone fractures. Deep learning model in specific newlineconvolutional neural networks are widely used currently in the medical image newlineprocessing. It also extends its horizon in bone fracture detection from the X-Ray newlineimages. The commonly used dataset for bone fracture detection is MURA newlineDataset. This work concentrates on classifying the bone fractures and it employs newlinethe MURA dataset. While almost all the works that detects bone fracture newlineemploys the entire MURA Dataset, this work uses only the specific type of bone newlinecalled as the humerus bones and tries to classify the bone fractures. The work is newlinethree-fold, the first phase of the work concentrates on comparing the pre-trained newlinemodels DenseNet169 Model and VGG Model. Two variants of the DenseNet169 newlineModels one with pre-trained weights and another without the pre-trained weights newlineis experimented and the results are compared. The second phase of the work concentrates on comparing the three models VGG, DenseNet169 and DenseNet121. Two datasets, humerus dataset as it is in the MURA Dataset and the same one without the images that has metal fitted to it. The performances of the models are tested and DenseNet169 is newlinefurther chosen for transfer learning process. The method of transfer learning newlineapproach used is carefully designed to meet the requirements. Customization of newlinethe layers was made with the consideration that the pre -trained models are newlinetrained with the ImageNet dataset and the dataset to which it has to be applied is newlineX-Ray images. The model is tested with both the dataset and the results are newlineinferred. The third phase of the work concentrates on developing a new CNN newlineModel. A customized model is developed for detecting the bone fracture of newlinehumerus bones. The model is tested with the two datasets. A slight change has newlinebeen made i
Pagination: xv,111
URI: http://hdl.handle.net/10603/470523
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File27.74 kBAdobe PDFView/Open
02_prelim pages.pdf1.29 MBAdobe PDFView/Open
03_content.pdf119.54 kBAdobe PDFView/Open
04_abstract.pdf115.34 kBAdobe PDFView/Open
05_chapter 1.pdf391.16 kBAdobe PDFView/Open
06_chapter 2.pdf396.89 kBAdobe PDFView/Open
07_chapter 3.pdf599.62 kBAdobe PDFView/Open
08_chapter 4.pdf848.22 kBAdobe PDFView/Open
09_chapter 5.pdf619.75 kBAdobe PDFView/Open
10_chapter 6.pdf641.78 kBAdobe PDFView/Open
11_annexures.pdf200.67 kBAdobe PDFView/Open
80_recommendation.pdf122.07 kBAdobe PDFView/Open
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