Automatic Diagnosing of Suspicious Lesions in Digital Mammograms

Abstract

Abstract-Breast cancer is the most common cancer and the leading cause of morbidity and mortality among women's age between 50 and 74 years across the worldwide. In this paper we've proposed a method to detect the suspicious lesions in mammograms, extracting their features and classify them as Normal or Abnormal and Benign or Malignant for diagnosing of breast cancer. This method consists of two major parts: The first one is detection of regions of interest (ROIs). The second one is diagnosing of detected ROIs. This method was tested by Mini Mammography Image Analysis Society (Mini-MIAS) database. To check method's performance, we've used FROC (Free-Receiver Operating Characteristics) curve in the detection part and ROC (Receiver Operating Characteristics) curve in the diagnosis part. Obtained results show that the performance of detection part has sensitivity of 94.27% at 0.67 false positive per image. The performance of diagnosis part has 94.29% accuracy, with 94.11% sensitivity, 94.44% specificity in the classification as normal or abnormal mammogram, and has achieved 94.4% accuracy, with 96.15% sensitivity and 94.54% specificity in the classification as Benign or Malignant mammogram.

A Diagnostic Model of Breast Cancer Based on Digital Mammogram Images Using Machine Learning Techniques

“…A proposal provided by [23] uses the Bayesian techniques with a Markovian random field to partition mammogram images into three diverse regions, the pectoral muscle, the fatty, and the fibroglandular regions. Other approaches were using the LBP, the K-means, SVM, and the GLCM algorithms for identifying the ROI regions from mammogram images like in [24][25][26]. e proposals presented by [2][3][4] introduce adaptive thresholding methods based on multiresolution for detecting suspicious lesions in mammogram images.…”

Anomaly classification in digital mammography based on multiple‐instance learning

“…First, all CADe models based on the segmentation of results. This is true for the detection [12][13][14][15][16][17][18][19][20][21][22], false positive reduction [23][24][25][26][27], and segmentation [28][29][30] of masses. In other words, CADx systems are focused on mass classification systems [13,22,[31][32][33][34][35][36][37][38], or malignant/no-malignant classification systems in general [39].…”