The present work aims to compare and evaluate the performance of five ensemble models in predicting breast cancer using 15 diet and lifestyle factors among the Mizo women. The dataset for developing ensemble learning models contains 148 breast cancer cases and 173 healthy individuals. Learning curves are constructed for five classifiers (AdaBoost, Gradient Boost, extra trees, bagging, and random forest) to find the best fit models for the present dataset. The performances of models are evaluated using 10-fold cross validation (CV), leave one out cross validation (LOOCV), and accuracy. Extra trees classifier outperformed other four ensemble classifiers with an accuracy of 96.3% and 95.5% using LOOCV and CV, respectively. The prediction accuracies of above-two cross validation methods have shown a Pearson correlation index of 97.45% which have strengthened the performances of the ensemble models. Thus, the lack of physical activity, less intake of fruits, vegetables, and water, high consumption of saum (fermented pork fat), smoked meat and vegetables, addiction of chewing pan (beetle leaves with arcane nut), using smokeless tobacco product: sahadh, and tuibur (aqueous tobacco extract) are the possible factors to cause breast cancer in Mizo population. 

Ensemble Modelling for Early Breast Cancer Prediction from Diet and Lifestyle

Sensitivity and specificity of screening mammography without clinical breast examination among Japanese women aged 40–49 years: analysis of data from the J-START results

Selection of diagnosis with oncologic relevance information from histopathology free text reports: A machine learning approach

The early detection and timely treatment of breast cancer can save lives. Mammography is one of the most efficient approaches to screening early breast cancer. An automatic mammographic image classification method could improve the work efficiency of radiologists. Current deep learning-based methods typically use the traditional softmax loss to optimize the feature extraction part, which aims to learn the features of mammographic images. However, previous studies have shown that the feature extraction part cannot learn discriminative features from complex data using the standard softmax loss. In this paper, we design a new architecture and propose respective loss functions. Specifically, we develop a double-classifier network architecture that constrains the extracted features' distribution by changing the classifiers' decision boundaries. Then, we propose the double-classifier constraint loss function to constrain the decision boundaries so that the feature extraction part can learn discriminative features. Furthermore, by taking advantage of the architecture of two classifiers, the neural network can detect the difficult-to-classify samples. We propose a weighted double-classifier constraint method to make the feature extract part pay more attention to learning difficult-to-classify samples' features. Our proposed method can be easily applied to an existing convolutional neural network to improve mammographic image classification performance. We conducted extensive experiments to evaluate our methods on three public benchmark mammographic image datasets. The results showed that our methods outperformed many other similar methods and state-of-the-art methods on the three public medical benchmarks. Our code and weights can be found on GitHub. 

WDCCNet: Weighted Double-Classifier Constraint Neural Network for Mammographic Image Classification

The design of tin-sprayed steel mesh is particularly important in the production of tin-sprayed steel mesh, which is the key to ensuring the function and quality of the tin-sprayed steel mesh. At present, the tin-sprayed steel mesh is still manually designed, and the design process is error-prone and inefficient. Therefore, a geometric cutting assistance system for tin-sprayed steel mesh of the combiner based on AutoCAD and ObjectARX was developed. The system adopts a parameterized design to realize the automatic operation of the steel mesh cutting process. First introduced the system development environment and composition architecture, and briefly explained the system interaction interface. Then it explains two key methods in the software system design and development process, including cutting quality control and finding the intersection of line segments. Finally, the actual case proves that the system greatly improves the efficiency and quality of steel mesh cutting.

Hiroko Tsunoda

Papers

A deep learning-based automated diagnostic system for classifying mammographic lesions