Mammographic Lesion Detection System Using Convolutional Neural Network and Empirical Mode Decomposition

Abstract

Breast cancer which is the most common and lethal cancer in women around the age of 20-59 has the mortality rate only lower than lung/bronchus cancer Breast cancer is metastatic cancer hence cancer detection before the metastasis might save many lives Mammography is the primary screening tool for early detection of breast cancer but the difficulty in image interpretation causes a high recall rate and overdiagnosis To decrease the rate of false-positive the amount of research on the computer-aided diagnosis (CAD) system has been growing since 1993 Empirical mode decomposition (EMD) will be used in this research to reveal the characteristics of the different lesions in the breast EMD decomposes the original signal into a finite number of components called intrinsic mode function (IMF) There are many algorithms of EMD to decompose the 2D data In this study the traditional multi-dimensional EMD and Radon transform-based 2DEMD were employed However the evaluation of 2D IMFs is limited since there are fewer exact metrics This study proposed some modified metrics which depend on the characteristics of the IMFs to improve the method The result indicated that the performance of 2DEMD is better than multi-dimensional EMD Next 2DEMD was applied to mammograms to extract the IMFs These IMFs would be used as the training data for the model Finally several CAD systems performances were compared in pairs: CNN with original images CNN with all IMFs and CNN with the reconstructed image without n-th IMF The model performance drastically reduced if the training data was lacking meaningful IMFs fewer meaningless IMFs would improve the model performance Concerning prospects for the future this system provides a promising method to train the model with more meaningful IMFs to different lesion types hence further improve the performance of the CAD system

Date of Award	2020
Original language	English
Supervisor	Kuo-Sheng Cheng (Supervisor)