Comparative Study on the Use of Analytical Software to Identify the Different Stages of Breast Cancer Using Discrete Temperature Data |
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Authors: | Joanna M Y Tan E Y K Ng Rajendra Acharya U Louis G Keith Jim Holmes |
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Institution: | (1) School of Mechanical and Aerospace Engineering, College of Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore;(2) School of Engineering, Division of ECE, Ngee Ann Polytechnic, Singapore, 599489, Singapore;(3) Department of Obstetrics and Gynecology, Feinberg School of Medicine, Northwestern University, 680 Lake Shore Drive, Suite 1015, Chicago, IL 60611, USA;(4) Lifeline Biotechnologies, Inc., 1325 Airmotive Way, Suite 175, Reno, NV 89502, USA;(5) Adjunct NUH Scientist, Office of Biomedical Research, National University Hospital of Singapore, Singapore, Singapore; |
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Abstract: | 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. |
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Keywords: | Breast cancer Neural networks Sensor Temperature Fuzzy Classifier Gaussian mixture model Support vector machine |
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