Mammogram Breast Tumor Abnormalities Detection Using DeepCNN with Discrete Cosine Transform Features - Priyadharsini M., Suriya; Sathiaseelan, J. G. R. (International Journal of Intelligent Systems and Applications in Engineering, 2023)
In the past few years, especially among younger people, breast cancer has become one of the most dangerous diseases. The goal of this study was to find out if DBT could be used in the clinic to measure the growth of breast cancer tumors. First, malignant and healthy mammograms are separated, and then, in stage 2, the size of the tumor is determined. Radiologists want to know how far along the cancer is so they can find the best way to cure and treat the person. This can be done with mammography by finding the right kind of abnormality to measure how bad the cancer is. With IBT, they may be able to get a clearer picture of the kind of abnormality. In this study, we show a brand-new way to divide malignant mammograms into six different groups. To figure out if a mammogram is malignant and how big a tumor is, features are taken from pre-processed images and run through different classifiers. The results of the best method, in this case DeepCNN, are then taken into account for further analysis. DeepCNN has divided mammograms that show cancer into six different groups. Again, the classifier (DeepCNN) is used for multi-classification using the "one against all" method. Using the maximum, median, and mean, the average of all the results from each category is found. It has been pointed out that the results are very encouraging and that the max rule is more than 96% accurate at classifying anomalies. For an experiment, a set of data from MIAS is used.