Query: Breast cancer diagnosis using mamogram

title: A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI
abstract: Multiparametric magnetic resonance imaging (mpMRI) has been shown to improve radiologists' performance in the clinical diagnosis of breast cancer. This machine learning study develops a deep transfer learning computer aided diagnosis (CADx) methodology to diagnose breast cancer using mpMRI. The retrospective study included clinical MR images of 927 unique lesions from 616 women. Each MR study included a dynamic contrast enhanced (DCE) MRI sequence and a T2 weighted (T2w) MRI sequence. A pretrained convolutional neural network (CNN) was used to extract features from the DCE and T2w sequences, and support vector machine classifiers were trained on the CNN features to distinguish between benign and malignant lesions. Three methods that integrate the sequences at different levels (image fusion, feature fusion, and classifier fusion) were investigated. Classification performance was evaluated using the receiver operating characteristic (ROC) curve and compared using the DeLong test. The single sequence classifiers yielded areas under the ROC curves (AUCs) [95% confidence intervals] of AUC DCE 0.85 [0.82, 0.88] and AUC T2w 0.78 [0.75, 0.81] The multiparametric schemes yielded AUC ImageFusion 0.85 [0.82, 0.88] AUC FeatureFusion 0.87 [0.84, 0.89] and AUC ClassifierFusion 0.86 [0.83, 0.88] The feature fusion method statistically significantly outperformed using DCE alone P 0.001) In conclusion, the proposed deep transfer learning CADx method for mpMRI may improve diagnostic performance by reducing the false positive rate and improving the positive predictive value in breast imaging interpretation.

title: Breast cancer diagnosis using a multi verse optimizer based gradient boosting decision tree
abstract: Breast cancer is among the most common cancers women got, which can be effectively cured providing that it is diagnosed at the early stages. In the current study, we attempted to classify breast cancer into two groups of malignant and benign by proposing a new ensemble learning method using Multi Verse Optimizer (MVO) and Gradient Boosting Decision Tree (GBDT) Moreover, the prediction rate of GBDT has been shown to be desirable, its efficiency and classification accuracy are significantly dependent on feature selection and parameter setting. Based on the MVO, we attempted to propose an efficient approach to optimize feature selection and GBDT's parameters at the same time. In other words, the MVO algorithm is able to play the role of a tuner to set the GBDT's main parameters and optimize feature selection results. To implement and test the proposed approach, standard criteria (i.e. accuracy, sensitivity, specificity, etc. was used for performance evaluation. Also, the datasets of Wisconsin Diagnostic Breast Cancer and Wisconsin Breast Cancer were considered for this purpose. Comparing the results of GBDT MVO model with other proposed models demonstrated that this model is more precise and has considerably lower variance in the case of a breast cancer diagnosis.

title: A Comparative Analysis of Data Mining Techniques on Breast Cancer Diagnosis Data using WEKA Toolbox
abstract: Breast cancer is considered the second most common cancer in women compared to all other cancers. It is fatal in less than half of all cases and is the main cause of mortality in women. It accounts for 16% of all cancer mortalities worldwide. Early diagnosis of breast cancer increases the chance of recovery. Data mining techniques can be utilized in the early diagnosis of breast cancer. In this paper, an academic experimental breast cancer dataset is used to perform a data mining practical experiment using the Waikato Environment for Knowledge Analysis (WEKA) tool. The WEKA Java application represents a rich resource for conducting performance metrics during the execution of experiments. Pre processing and feature extraction are used to optimize the data. The classification process used in this study was summarized through thirteen experiments. Additionally, 10 experiments using various different classification algorithms were conducted. The introduced algorithms were: Naive Bayes, Logistic Regression, Lazy IBK (Instance Bases learning with parameter K) Lazy Kstar, Lazy Locally Weighted Learner, Rules ZeroR, Decision Stump, Decision Trees J48, Random Forest and Random Trees. The process of producing a predictive model was automated with the use of classification accuracy. Further, several experiments on classification of Wisconsin Diagnostic Breast Cancer and Wisconsin Breast Cancer, were conducted to compare the success rates of the different methods. Results conclude that Lazy IBK classifier k NN can achieve 98% accuracy among other classifiers. The main advantages of the study were the compactness of using 13 different data mining models and 10 different performance measurements, and plotting figures of classifications errors.

title: Improving 3 D Imaging Breast Cancer Diagnosis Systems Using a New Method for Placement of Near infrared Sources and Detectors
abstract: The objective of this research is to improve the 3 D imaging system using near infrared light emission in breast tissue to achieve a more accurate diagnosis of the tumor. Experimental results in this research on this imaging system indicate that a more accurate diagnosis of abnormal areas depends on the location of the sources and detectors. Therefore, an improved location model has been proposed to determine a more suitable placement of sources and detectors. In this article, no human breast cancer samples were examined due to inaccessibility to a 3 D imaging system using near infrared lights. Since such experiments should be conducted several times to obtain more accurate reconstructed images, the proposed method was evaluated using the optical images reconstruction toolbox of NIRFAST 7.2 in the MATLAB programming environment. The results were then compared with the results of similar articles. The obtained results showed that the proposed placement of sources and detectors can detect abnormal areas with a much lower error rate. Furthermore, the proposed placement of sources and detectors achieved a good result in simultaneously diagnosing two abnormal areas.

title: A Comparative Analysis of Breast Cancer Detection and Diagnosis Using Data Visualization and Machine Learning Applications
abstract: In the developing world, cancer death is one of the major problems for humankind. Even though there are many ways to prevent it before happening, some cancer types still do not have any treatment. One of the most common cancer types is breast cancer, and early diagnosis is the most important thing in its treatment. Accurate diagnosis is one of the most important processes in breast cancer treatment. In the literature, there are many studies about predicting the type of breast tumors. In this research paper, data about breast cancer tumors from Dr. William H. Walberg of the University of Wisconsin Hospital were used for making predictions on breast tumor types. Data visualization and machine learning techniques including logistic regression, k nearest neighbors, support vector machine, naive Bayes, decision tree, random forest, and rotation forest were applied to this dataset. R, Minitab, and Python were chosen to be applied to these machine learning techniques and visualization. The paper aimed to make a comparative analysis using data visualization and machine learning applications for breast cancer detection and diagnosis. Diagnostic performances of applications were comparable for detecting breast cancers. Data visualization and machine learning techniques can provide significant benefits and impact cancer detection in the decision making process. In this paper, different machine learning and data mining techniques for the detection of breast cancer were proposed. Results obtained with the logistic regression model with all features included showed the highest classification accuracy (98.1% and the proposed approach revealed the enhancement in accuracy performances. These results indicated the potential to open new opportunities in the detection of breast cancer.

title: Application of Machine Learning and Word Embeddings in the Classification of Cancer Diagnosis Using Patient Anamnesis
abstract: Currently, one of the main challenges for information systems in healthcare is focused on support for health professionals regarding disease classifications. This work presents an innovative method for a recommendation system for the diagnosis of breast cancer using patient medical histories. In this proposal, techniques of natural language processing (NLP) were implemented on real datasets: one comprised 160, 560 medical histories of anonymous patients from a hospital in Chile for the following categories: breast cancer, cysts and nodules, other cancer, breast cancer surgeries and other diagnoses; and the other dataset was obtained from the MIMIC III dataset. With the application of word embedding techniques, such as word2vec's skip gram and BERT, and machine learning techniques, a recommendation system as a tool to support the physician's decision making was implemented. The obtained results demonstrate that using word embeddings can define a good quality recommendation system. The results of 20 experiments with 5 fold cross validation for anamnesis written in Spanish yielded an F1 of 0.980 0.0014 on the classification of 'cancer' versus 'not cancer' and 0.986 0.0014 for 'breast cancer' versus 'other cancer' Similar results were obtained with the MIMIC III dataset.

title: ENHANCED SVM CLASSIFIER FOR BREAST CANCER DIAGNOSIS
abstract: *1 Professor, ECE, Mookambigai College of Engineering, Pudukottai, India 2 UG Student, ECE, Mookambigai College of Engineering, Pudukottai, India  Breast cancer is the leading disease to cause death especially in women. In this paper, a frame work based algorithm for the classification of cancerous/non cancerous data is developed using application of supervised machine learning. In feature selection, we derive basis set in the kernel space and then we extend the margin based feature selection algorithm. We are trying to explore several feature selection, extraction techniques and combine the optimal feature subsets with various learning classification methods such as KNN, PNN and Support Vector Machine (SVM) classifiers. The best classification performance for breast cancer diagnosis is attained equal to 99.17% between radius and compact features using SVM classifier. And also derive the features of a breast image in the WBCD dataset.

title: Diagnosis of breast cancer based on modern mammography using hybrid transfer learning
abstract: Breast cancer is a common cancer in women. Early detection of breast cancer in particular and cancer, in general, can considerably increase the survival rate of women, and it can be much more effective. This paper mainly focuses on the transfer learning process to detect breast cancer. Modified VGG (MVGG) is proposed and implemented on datasets of 2D and 3D images of mammograms. Experimental results showed that the proposed hybrid transfer learning model (a fusion of MVGG and ImageNet) provides an accuracy of 94.3% On the other hand, only the proposed MVGG architecture provides an accuracy of 89.8% So, it is precisely stated that the proposed hybrid pre trained network outperforms other compared Convolutional Neural Networks. The proposed architecture can be considered as an effective tool for radiologists to decrease the false negative and false positive rates. Therefore, the efficiency of mammography analysis will be improved.

title: Classification with 2 D Convolutional Neural Networks for breast cancer diagnosis
abstract: Breast cancer is the most common cancer in women. Classification of cancer/non cancer patients with clinical records requires high sensitivity and specificity for an acceptable diagnosis test. The state of the art classification model Convolutional Neural Network (CNN) however, cannot be used with clinical data that are represented in 1 D format. CNN has been designed to work on a set of 2 D matrices whose elements show some correlation with neighboring elements such as in image data. Conversely, the data examples represented as a set of 1 D vectors apart from the time series data cannot be used with CNN, but with other classification models such as Artificial Neural Networks or RandomForest. We have proposed some novel preprocessing methods of data wrangling that transform a 1 D data vector, to a 2 D graphical image with appropriate correlations among the fields to be processed on CNN. We tested our methods on Wisconsin Original Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) datasets. To our knowledge, this work is novel on non image to image data transformation for the non time series data. The transformed data processed with CNN using VGGnet 16 shows competitive results for the WBC dataset and outperforms other known methods for the WDBC dataset.

title: Breast cancer diagnosis based on genomic data and extreme learning machine
abstract: According to cancer.org news in 2018, the most common cancer diagnosed in women is breast, lung, and colorectal cancers. About 30% of all new cancer diagnoses in women refer to breast cancer. Therefore, predicting breast cancer in its early stages stays a controversial challenge. In this study, we focus on DNA methylation gene expression profiles of patients (series GSE32393 NCBI dataset) For dimension reduction, Non negative matrix factorization (NMF) is employed and combined with a new method called column splitting. The reason of superiority of NMF algorithm over other popular ones is its usage for both supervised and unsupervised feature matrix transformation into lower dimensional feature matrix. Afterward the main algorithms that are used for classification are extreme learning machine and support vector machine algorithms. The achieved prediction performances are comparable to deep learning models. The best attained performance has zero error rate on NCBI 137 samples. While it has shown that, the best deep model on the mentioned data has 2.7 error rate.