Microcalcification detection in mammography for early breast cancer diagnosis using deep learning technique / Leong Yew Sum

Leong, Yew Sum (2022) Microcalcification detection in mammography for early breast cancer diagnosis using deep learning technique / Leong Yew Sum. Masters thesis, Universiti Malaya.

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Abstract

Breast Cancer is one of the common cancers in women and may cause lives to be lost if they were misdiagnosed and left untreated. Existence of breast microcalcifications are common in breast cancer patients and they are an effective indicator of early breast cancer. This project will incorporate the use of machine learning in segmenting breast mammogram images with calcifications of either benign or malignant cases for early breast cancer diagnosis. ROI images of breast microcalcification will be utilized to train several pretrained models from fastai library in Google Colaboratory platform using supervised learning with a ratio of 0.80 for training dataset and 0.20 for validation dataset. Image processing of ROI images were conducted to remove possible artifacts and noises in order to enhance the quality of the images before training. The pretrained models that were included in this study are Resnet34, Resnet50, VGG16 and Alexnet. Different hyperparameters such as epoch, batch size etc were tuned in order to obtain the best possible result in this study. Confusion matrices were utilized in order to measure the output parameters of the models for comparison in terms of performance. The result from this study shows that Resnet50 achieves the highest accuracy with a value of 97.58%, followed by Resnet34 of 97.35%, VGG16 of 96.97% and finally Alexnet of 83.06%.

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