Performance of binary classification of breast cancer suffers from high imbalance between classes. In this article we present the preprocessing module designed to negate the discrepancy in training examples. Preprocessing module is based on standardization, Synthetic Minority Oversampling Technique and undersampling. We show how each algorithm influences classification accuracy. Results indicate that described module improves overall Area Under Curve up to 10% on the tested dataset. Furthermore we propose other methods of dealing with imbalanced datasets in breast cancer classification.
Thermographic images of breast taken by the Braster device are uploaded into web application which uses different classification algorithms to automatically decide whether a patient should be more thoroughly examined. This article presents the approach to the task of classifying contours visible on thermographic images of breast taken by the Braster device in order to make the decision about the existence of cancerous tumors in breast. It presents the results of the researches conducted on the different classification algorithms.
The computer system for an automatic interpretation of thermographic pictures created by the Br-aster devices uses image processing and machine learning algorithms. The huge set of attributes analyzed by this software includes the asymmetry measurements between corresponding images, and these features are analyzed in presented paper. The system was tested on real data and achieves accuracy comparable to other popular techniques used for breast tumour detection.
This article describes the processing and classification of thermographic examinations taken with device developed by Braster SA. The device records the surface temperature of the breast skin using the liquid crystal matrices. Images are analyzed with the use of machine learning algorithms. The result of classification is available after a few minutes and when it detects suspicious changes patient may be referred for detailed examinations.
Using classification learning algorithms for medical applications may require not only refined model creation techniques and careful unbiased model evaluation, but also detecting the risk of misclassification at the time of model application. This is addressed by novelty detection, which identifies instances for which the training set is not sufficiently representative and for which it may be safer to restrain from classification and request a human expert diagnosis. The paper investigates two techniques for isolated instance identification, based on clustering and one-class support vector machines, which represent two different approaches to multidimensional outlier detection. The prediction quality for isolated instances in breast cancer image data is evaluated using the random forest algorithm and found to be substantially inferior to the prediction quality for non-isolated instances. Each of the two techniques is then used to create a novelty detection model which can be combined with a classification model and used at the time of prediction to detect instances for which the latter cannot be reliably applied. Novelty detection is demonstrated to improve random forest prediction quality and argued to deserve further investigation in medical applications.
This article presents the application of machine learning algorithms for early detection of breast cancer on the basis of thermographic images. Supervised learning model: Support vector machine (SVM) and Sequential Minimal Optimization algorithm (SMO) for the training of SVM classifier were implemented. The SVM classifier was included in a client-server application which enables to create a training set of examinations and to apply classifiers (including SVM) for the diagnosis and early detection of the breast cancer. The sensitivity and specificity of SVM classifier were calculated based on the thermographic images from studies. Furthermore, the heuristic method for SVM's parameters tuning was proposed.
This contribution introduces the method of cancer pathologies detection on breast skin temperature distribution images. The use of thermosensitive foils applied to the breasts skin allows to create thermograms, which displays the amount of infrared energy emitted by all breast cells. The significant foci of hyperthermia or inflammation are typical for cancer cells. That foci can be recognized on thermograms as a contours, which are the areas of higher temperature. Every contour can be converted to a feature set that describe it, using the raw, central, Hu, outline, Fourier and colour moments of image pixels processing. This paper defines also the new way of describing a set of contours through theirs neighbourhood relations. Contribution introduces moreover the way of ranking and selecting most relevant features. Authors used Neural Network with Gevrey`s concept and recursive feature elimination, to estimate feature importance.