An Efficient Breast Cancer Detection Framework for Medical Diagnosis Applications

Breast cancer is the most common type of cancer, and it reason for death toll in women recent years. Early diagnosis essential to handle breast patients treatment at right time. Screening with mammography preferred examination as available worldwide inexpensive. Computer-Aided Detection (CAD) systems are used analyze medical images detect early. The rate has decreased by detecting tumors early having appropriate after operations. Processing mammogram four main steps: pre-processing, segmentation region interest, feature extraction classification into normal or abnormal classes. This paper presents an efficient framework processing introduces algorithm masses. pre-processing step includes removal digitization noise using a 2D median filter, artifacts morphological operations, contrast enhancement fuzzy technique. proposed image technique analyzed compared conventional techniques based on Enhancement Measure (EME) local metrics. comparison shows outstanding performance from visual numerical perspectives. process performed Otsu's multiple thresholding method. method segments regions five classes variable intensities thresholds. Its effectiveness measured quality output, gives details about positions Dice coefficient, Hausdorff, Peak Signal-to-Noise Ratio (PSNR) segmented tumor 81% ground truth provided expert. Hence, achieves promising results aiding radiologists screening mammograms, accurately.