Computational Intelligence based Classification Method for Breast Cancer Diagnosis in Mammograms

Abstract

With the recent research and development in deep learning since 2012, the emerging of Convolutional Neural Networks (CNNs) in bioinformatics, especially medical imaging, achieved obvious and tremendous improvement. Nowadays, from the raised critical challenges are the cancer detection in breast mammograms followed by the classification of the pathology of the localized lesions. Till now, the evaluation process of the screening mammograms is held by radiologists and physicians. Due to a large number of mammograms screened daily, this mammograms evaluation process becomes very monotonous, tiring, lengthy, costly, and significantly prone to errors. So, in the last two decades, the development of Computer Aided Detection (CAD) systems became very essential for early diagnosis. However, they should be improved for more accurate results which can help to obtain more confidence to the radiologist’s decision. In this thesis, I present the recently approaches that are machine learning based models developed to detect cancer in breast mammograms and classify them by analyzing them in the form of comparative study and analysis. Also, the mammographic datasets that are publicly available and popular as well are listed in the recent work to facilitate any new experiments and comparisons. It is conducted from the comparative study that the You Only Look Once (YOLO) model is one from the recent efficient and fast object detectors that obtains high accuracy compared with other detectors. Based on the conducted comparative analysis, an end-to-end computer-aided diagnosis system based on You Only Look Once (YOLO) is proposed. The proposed system first preprocesses the mammograms from their DICOM format to images without losing data. Then, YOLO is utilized to take each mammogram and checks it in one shot to detect any existing lesions. Finally, the localized masses are classified into malignant and benign lesions without any human intervention. YOLO has three different architectures, and in this thesis, the three versions are trained on breast mammograms for mass detection and classification to compare their performance against each other. I utilized the anchor boxes upgrade in YOLO-V3 but in different manner. In order to achieve high detection accuracy, all anchor boxes used through training YOLO, are updated to sizes related to the masses I need to detect in mammograms, i.e., data related anchors. This is carried on by applying the k-means clustering on the sizes of all masses of the mammograms dataset to cluster them in a specific number of boxes which used later while YOLO training. Using the experimental results, it is proved how the idea of using YOLO-V3 by regenerated anchor boxes has a noticeable impact on the detection of masses and their classification as well. The proposed model is proved its ability to detect most of the challenging cases of masses and classify them correctly comparing with other recent detectors and the earlier versions of YOLO as well.