Abstract
The major challenge in cancer diagnosis is the number of patients who are incorrectly diagnosed. To address this, we have developed and tested different expert diagnostic systems which differentiate among patients with and without breast cancer based on samples describing characteristics of the cell nuclei present in the digitized image of a fine needle aspirate (FNA). Data was collected from the UCI machine learning respiratory, specifically 699 samples. Our results demonstrate that a Feed Forward Backpropagation single hidden layer neural network with 20 neurons and TANSIG transfer function has the highest classification accuracy (98.9% and 99% accuracy in training and test set, respectively). The accuracy of multilayer architectures was significantly lower, and valued between a range of 74.9–86.3%, where the average was 81.37%. A developed expert system with a proven accuracy can be used in the future in laboratory conditions as a promising method for early classification diagnosis for breast cancer.
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References
Schneider, A. P., Zainer, C. M., Kubat, C. K., Mullen, N. K., & Windisch, A. K. (2014). The breast cancer epidemic: 10 facts. The Linacre Quarterly, 81(3), 244–277.
Al-Hajj, M., Wicha, M. S., Benito-Hernandez, A., Morrison, S. J., & Clarke, M. F. (2003). Prospective identification of tumorigenic breast cancer cells. Proceedings of the National Academy of Sciences, 100(7), 3983–3988.
Sinn, H. P., & Kreipe, H. (2013). A brief overview of the WHO classification of breast tumors. Breast Care, 8(2), 149–154.
Osmanović, A., Abdel-Ilah, L., Hodžić, A., Kevric, J., & Fojnica, A. (2017). Ovary Cancer Detection using Decision Tree Classifiers based on Historical Data of Ovary Cancer Patients. In CMBEBIH 2017 (pp. 503–510). Springer, Singapore.
Bušatlić, E., Osmanović, A., Jakupović, A., Nuhić, J., & Hodžić, A. (2017). Using Neural Networks and Ensemble Techniques based on Decision Trees for Skin Permeability Prediction. In CMBEBIH 2017 (pp. 41–50). Springer, Singapore.
Fojnica, A., Osmanović, A., & Badnjević, A. (2016, June). Dynamical model of Tuberculosis-Multiple Strain Prediction based on artificial neural network. In Embedded Computing (MECO), 2016 5th Mediterranean Conference on (pp. 290–293). IEEE.
Alić, B., Gurbeta, L., & Badnjević, A. (2017, June). Machine learning techniques for classification of diabetes and cardiovascular diseases. In Embedded Computing (MECO), 2017 6th Mediterranean Conference on (pp. 1–4). IEEE.
Badnjevic A., Cifrek M., Gurbeta L., Feric E., “Classification of Chronic Obstructive Pulmonary Diseases Based on Neuro-Fuzzy Software” in the Book: “Chronic Obstructive Pulmonary Disease (COPD): Clinical Symptoms, Emerging Treatment Strategies and Impact on Quality of Life”, 2016, Nova Science Publisher, ISBN: 978-1-63484-500-7, 2016.
Alic B, Sejdinovic D, Gurbeta L, Badnjevic A, “Classification of Stress Recognition using Artificial Neural Network”, IEEE 5th Mediterranean Conference on Embedded Computing (MECO), 12–16 June 2016, Bar, Montenegro.
Halilovic S, Avdihodžić H, Gurbeta L, (2016) Micro cell culture analog Apparatus (µCCA) output prediction using Artificial Neural Network, 5th Mediterranean Conference on Embedded Computing (IEEE MECO 2016), Bar, Montenegro;
Fojnica, A., Osmanović, A., Tarakčija, D., & Demirović, S. (2017). Quantification of protein concentration adsorbed on gold nanoparticles using Artificial Neural Network. In CMBEBIH 2017 (pp. 142–146). Springer, Singapore.
Lamamra, K., Vaidyanathan, S., Azar, A. T., & Salah, C. B. (2017). Chaotic System Modelling Using a Neural Network with Optimized Structure. In Fractional Order Control and Synchronization of Chaotic Systems (pp. 833–856). Springer International Publishing.
Santos-Andre, T. C., & da Silva, A. C. R. (1999). A neural network made of a Kohonen’s SOM coupled to a MLP trained via backpropagation for the diagnosis of malignant breast cancer from digital mammograms. In Neural Networks, 1999. IJCNN’99. International Joint Conference on (Vol. 5, pp. 3647–3650). IEEE.
Revett, K., Gorunescu, F., Gorunescu, M., El-Darzi, E., & Ene, M. (2005, November). A breast cancer diagnosis system: a combined approach using rough sets and probabilistic neural networks. In Computer as a Tool, 2005. EUROCON 2005. The International Conference on (Vol. 2, pp. 1124–1127). IEEE.
Padmavati, J. (2011). A comparative study on breast cancer prediction using RBF and MLP. International Journal of Scientific & Engineering Research, 2(1), 1–5.
Asuncion, A., & Newman, D. (2007). UCI machine learning repository.
Shahin, M. A., Maier, H. R., & Jaksa, M. B. (2004). Data division for developing neural networks applied to geotechnical engineering. Journal of Computing in Civil Engineering, 18(2), 105–114.
Rani, K. U. (2010). Parallel approach for diagnosis of breast cancer using neural network technique. International Journal of Computer Applications, 10(3), 1–5.
Abdel-Ilah, L., & Šahinbegović, H. (2017). Using machine learning tool in classification of breast cancer. In CMBEBIH 2017 (pp. 3–8). Springer, Singapore.
Taktak, A. F., & Fisher, A. C. (Eds.). (2006). Outcome prediction in cancer. Elsevier.
Swathi, S., Rizwana, S., Babu, G. A., Kumar, P. S., & Sarma, P. V. G. K. (2012). Classification of Neural Network Structures for Breast Cancer Diagnosis. International Journal of Computer Science and Communication, 3(1), 227–231.
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Osmanović, A., Halilović, S., Ilah, L.A., Fojnica, A., Gromilić, Z. (2019). Machine Learning Techniques for Classification of Breast Cancer. In: Lhotska, L., Sukupova, L., Lacković, I., Ibbott, G.S. (eds) World Congress on Medical Physics and Biomedical Engineering 2018. IFMBE Proceedings, vol 68/1. Springer, Singapore. https://doi.org/10.1007/978-981-10-9035-6_35
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DOI: https://doi.org/10.1007/978-981-10-9035-6_35
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