Exploring Breast Cancer Texture Analysis through Multilayer Neural Networks

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