Breast Cancer Detection Using Mammogram Images with Improved Multi-Fractal Dimension Approach and Feature Fusion

• Published in: Applied sciences 2021-12, Vol.11 (24), p.12122
• Main Authors: Zebari, Dilovan Asaad, Ibrahim, Dheyaa Ahmed, Zeebaree, Diyar Qader, Mohammed, Mazin Abed, Haron, Habibollah, Zebari, Nechirvan Asaad, Damaševičius, Robertas, Maskeliūnas, Rytis
• Format: Article
• Language: English
• Subjects: Accuracy; Breast cancer; breast cancer detection; Classification; Datasets; Decision making; Evaluation; feature fusion; Fractal geometry; Fractals; Genetic algorithms; Image classification; Image retrieval; Learning algorithms; Learning theory; Machine learning; mammogram images; Mammography; Medical imaging; Mortality; multi-fractal dimension; Neural networks; Noise; Standard deviation; Support vector machines; Wavelet transforms
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app112412122

Breast cancer detection using mammogram images at an early stage is an important step in disease diagnostics. We propose a new method for the classification of benign or malignant breast cancer from mammogram images. Hybrid thresholding and the machine learning method are used to derive the region of interest (ROI). The derived ROI is then separated into five different blocks. The wavelet transform is applied to suppress noise from each produced block based on BayesShrink soft thresholding by capturing high and low frequencies within different sub-bands. An improved fractal dimension (FD) approach, called multi-FD (M-FD), is proposed to extract multiple features from each denoised block. The number of features extracted is then reduced by a genetic algorithm. Five classifiers are trained and used with the artificial neural network (ANN) to classify the extracted features from each block. Lastly, the fusion process is performed on the results of five blocks to obtain the final decision. The proposed approach is tested and evaluated on four benchmark mammogram image datasets (MIAS, DDSM, INbreast, and BCDR). We present the results of single- and double-dataset evaluations. Only one dataset is used for training and testing in the single-dataset evaluation, whereas two datasets (one for training, and one for testing) are used in the double-dataset evaluation. The experiment results show that the proposed method yields better results on the INbreast dataset in the single-dataset evaluation, whilst better results are obtained on the remaining datasets in the double-dataset evaluation. The proposed approach outperforms other state-of-the-art models on the Mini-MIAS dataset.