BREAST CANCER DETECTION USING PROBABILISTIC NEURAL NETWORKS
International Research Journal of Modernization in Engineering Technology and Science · 2024
Breast cancer is one of the leading diseases for women in the world.Ranked second among all types of cancers in women, this disease is a leading cause of mortality.Early detection is crucial for reducing its impact on mortality rates.The early detection of breast cancer and treatment leads to an increase in the survival rate of women.Mammography is a standard radiological screening technique, which is used for checking of breast cancer in women when there are no symptoms.In the literature, several machine learning (ML) methods, such as support vector machine (SVM), have been applied to detect and classify breast cancer.However, SVM often yields inaccurate results.To address this issue, our research implements an advanced deep learning-based classification mechanism using Probabilistic Neural Networks (PNN).Initially, k-means clustering based segmentation approach is used for efficient detection the region of breast cancer.Finally, to archive the maximum efficiency of the system, PNN developed for classification of breast cancer with the gray level cooccurrence matrix (GLCM) and Statistical Color features respectively.Thus, this research work can be effectively used for classification of Benign and Malignant breast cancers.The simulation analysis indicates that the proposed method out performs state-of-the-art approaches in both qualitative and quantitative assessments.