Traditionally, analysing images for patient diagnosis and personalised treatment planning hasbeen reliant only on human expertise. Now, with growing data volumes and the need for fasterprocessing, artificial intelligence (AI) has increased in importance, revolutionising medical imageanalysis.In this project, a computer-aided diagnosis (CAD) system using deep learning models (DLMs)was developed for ultrasound (US) breast cancer images. Two datasets were combined andused for model training and evaluation. The first dataset was older, larger, and moreestablished, while the second was smaller and recently published at the project start. Bothdatasets contained benign, malignant and normal cases, with an US image and a mask file foreach case. The mask file contained the segmented lesion in the US image. Two differentapproaches were used in this project. The first approach used only US images to train andevaluate models. The second approach created an overlaid image from the US image andmask file for each case, and inputted the overlay image to the model.Different techniques, including class weighting, data augmentation, and pre-processing, wereexplored to address class imbalance and enhance model performance. Class weighting anddata augmentation were both shown to even out class performance. Results indicate thataccuracy and minority class recall can be improved with pre-processing and data augmentation.Hyperparameter tuning optimised the model performance further. Approach 1 achieved anaccuracy of 83%, AUC of 87%, benign and normal recalls of 77%, and malignant recall of 95%.Approach 2 achieved an accuracy of 94%, AUC of 96%, benign recall of 97%, malignant recallof 87% and normal recall of 100%.For CAD systems to reach their full potential, they must be reliable and easy to use andinterpret for medical professionals. The field of CAD systems for US breast cancer imagesremains challenged by the lack of public comprehensive datasets. Models must be able togeneralise to diverse patient populations, and future work should focus on larger and morediverse datasets.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-532044 |
| Date | January 2024 |
| Creators | Qvarnlöf, Moa |
| Publisher | Uppsala universitet, Molekylär evolution |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UPTEC X ; 24030 |
Page generated in 0.0017 seconds