Deep learning-based classification of breast tumors in ultrasound images / Ayub Ahmed Omar
The use of ultrasound imaging techniques to diagnose breast cancer at an early stage is a popular and effective method. The issue with traditional breast ultrasound diagnosis is that, unlike magnetic resonance imaging (MRI) and mammography, it is prone to making a mistake due to its subjectivi...
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| Format: | Thesis |
| Published: |
2022
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| Subjects: | |
| Online Access: | http://studentsrepo.um.edu.my/14364/1/Ayub_Ahmed_Omar.jpg http://studentsrepo.um.edu.my/14364/3/ayub.pdf http://studentsrepo.um.edu.my/14364/ |
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| Summary: | The use of ultrasound imaging techniques to diagnose breast cancer at an early stage
is a popular and effective method. The issue with traditional breast ultrasound
diagnosis is that, unlike magnetic resonance imaging (MRI) and mammography, it is
prone to making a mistake due to its subjectivity, which could result in a missed
diagnosis and an unnecessary biopsy. In this research project, recent breast tumor
classification model algorithms are investigated and analyzed, and then the limitations
and gaps in previous techniques are highlighted. The Breast Ultrasound Images
Dataset (BUID) has been prepared and preprocessed in order to train both the U-Net
and Convolutional neural network (CNN) classifier models. The U-Net model is used
to locate tumor growth in original medical images because of its capacity to do
classification on each pixel in the input image and produce input and output images
that are the same size. Then, a CNN classifier model is built to classify the U-Net
model's generated mask images as benign, malignant, or normal. The accuracy
performance matrices and Dice loss function are used to evaluate the performance of
both U-Net and CNN classifier models. The U-Net model have achieved an accuracy
of 93% and a dice loss value of 0.4391. Whereas the CNN classifier model has
achieved an accuracy of 85%. |
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