Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Information Sciences: an International Journal
Half-Against-Half multi-class support vector machines
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Journal of Medical Systems
Thermography Based Breast Cancer Detection Using Texture Features and Support Vector Machine
Journal of Medical Systems
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Breast cancer is the second leading cause of death in women. It occurs when cells in the breast begin to grow out of control and invade nearby tissues or spread throughout the body. The limitations of mammography as a screening modality, especially in young women with denser breasts, necessitated the development of novel and more effective screening strategies with acceptable sensitivity and specificity. The aim of this study was to develop a feasible interpretive software system which was able to detect and classify breast cancer patients by employing techniques of different analytical software. The protocol described uses 6,000 pieces of thermal data collected from 16-sensors, eight placed on the surface of each breast. Data was collected every 5 min for the duration of the test period. Placement of sensors was accomplished with the use of a template design from information provided by the national tumor registry to insure that the information was collected in areas of the breast where most breast cancers develop. Data in this study was collected from 90 individuals exhibiting four different breast conditions, namely: normal, benign, cancer and suspected-cancer. The temperature data collected from these 16 sensors placed on the surface of each breast were fed as inputs to the classifiers. Comparisons were made on five different kinds of classifiers: back-propagation algorithm, probabilistic neural network, fuzzy (Sugeno-type), Gaussian mixture model and support vector machine. These classifiers were able to attain approximately 80% accuracy in classifying the four different diagnoses (normal, benign, cancer and suspected-cancer). Gaussian mixture model was the most sensitive classifier, achieving the highest sensitivity of 94.8%. Support vector machine was considered the best classifier as it was able to produce the most specific and accurate results. Based on these evaluations, this current effort shows the feasibility of applying analytical software techniques together with the real-time functional thermal analysis to develop a potential tool for the detection and classification of breast cancer.