Exploring Breast Cancer Texture Analysis through Multilayer Neural Networks

Abstract

Breast cancer is a significant health problem for women globally; however, timely detection can reduce female morbidity and mortality. Early breast screening has become imperative for all women, though, adequate screening facilities are necessarily required in developing countries like Pakistan, where breast cancer is a leading cause of death. To encounter this chronic disease, various image processing techniques have been introduced to automatically diagnose breast cancer from digital mammograms. The current study deployed data from a population of 35 participants. The mammograms used for screening were 5 normal, 15 benign, and 15 malignant patients. The breast images were marked by the radiologist and the system was trained with normal, benign, and malignant classes. Moreover, Multilayer Neural Networks (MNN) based texture analysis methodology was adopted to distinguish normal, benign, and malignant breast images. Reportedly, an automated approach was used to detect breast conditions after conducting the analysis of digital mammograms. Statistical parameters, namely sum, mean, variance, standard deviation, kurtosis, skewness, energy, and entropy were calculated, analyzed, and compared for the normal, malignant, and benign breast images. The results indicated a 100% accuracy after the analysis. The results of the extracted statistical parameters were promising and reliable in distinguishing between normal, malignant, and benign breast mammograms, again indicating the need for early detection of the disease to minimize the risk of breast cancer among women.