Resnet Based Feature Extraction with Decision Tree Classifier for Classificaton of Mammogram Images

Abstract

Right now, breast cancer is considered as a most important health problem among women over the world. The detection of breast cancer in the beginning stage can reduce the mortality rate to a considerable extent. Mammogram is an effective and regularly used technique for the detection and screening of breast cancer. The advanced deep learning (DL) techniques are utilized by radiologists for accurate finding and classification of medical images. This paper develops a new deep segmentation with residual network (DS-RN) based breast cancer diagnosis model using mammogram images. The presented DS-RN model involves preprocessing, Faster Region based Convolution Neural Network (R-CNN) (Faster R-CNN) with Inception v2 model based segmentation, feature extraction and classification. To classify the mammogram images, decision tree (DT) classifier model is used. A detailed simulation process is performed to ensure the betterment of the presented model on the Mini-MIAS dataset. The obtained experimental values stated that the DS-RN model has reached to a maximum classification performance with the maximum sensitivity, specificity, accuracy and F-Measure of 98.15%, 100%, 98.86% and 99.07% respectively.

Value of machine learning-based transrectal multimodal ultrasound combined with PSA-related indicators in the diagnosis of clinically significant prostate cancer

“…An artificial neural network (ANN) was established using the 10 independent factors of clinically significant prostate cancer noted above ( 24 ). The network ANN, logistic regression (LR) ( 25 ), support vector machine (SVM) ( 26 ), K-nearest neighbor (KNN) ( 27 ), decision tree (DT) ( 28 ), random forest (RF) ( 29 ), and six common machine learning models were analyzed. The results showed that the sensitivity of the ANN model to significant prostate cancer was the highest, up to 0.80, and the sensitivity of SVM was the lowest, at 0.2.…”