1st International Conference on Chemo and BioInformatics, ICCBIKG 2021, (243-246)
AUTHOR(S) / AUTOR(I): Ivan Lorencin, Klara Smolić, Dean Markić, Josip Španjol
E-ADRESS / E-ADRESA: ilorencin@riteh.hr, klara.smolic@gmail.com , dean.markic@medri.uniri.hr , josip.spanjol@medri.uniri.hr
DOI: 10.46793/ICCBI21.243L
ABSTRACT / SAŽETAK:
Bladder cancer is one of the most common malignancies of the urinary tract. It is characterized by high metastatic potential and a high recurrence rate, which significantly complicates diagnosis and treatment. In order to increase the accuracy of the diagnostic procedure, algorithms based on artificial intelligence are introduced. This paper presents the principle of selection of convolutional neural network (CNN) models based on a multi-objective approach that maximizes classification and generalization performance. Model selection is performed on two standard CNN architectures, AlexNet and VGG-16. Classification performances are measured by using ROC analysis and the resulting AUC value. On the other hand, generalization performances are evaluated by using a 5-fold cross-validation procedure. By using these two metrics, a multi-objective fitness function, used in meta-heuristic algorithms, is designed. The multi-objective search was performed using a Genetic algorithm (GA) and a Discrete Particle Swarm (D-PS) algorithm. From obtained results, it can be noticed that such an approach has resulted in CNN models that are defined with high classification and generalization performances. When a GA-based approach is used, fitness values up to 0.97 are achieved. On the other hand, by using the D-PS approach, fitness values up to 0.99 are achieved pointing towards the conclusion that such an approach has provided models with higher classification and generalization performances.
KEY WORDS / KLJUČNE REČI:
Convolutional neural network, Discrete particle swarm algorithm, Genetic algorithm, Urinary bladder cancer
REFERENCES / LITERATURA:
- Zlatev DV, Altobelli E, Liao JC: Advances in Imaging Technologies in the Evaluation of High Grade Bladder Care System. Urol Clin North 2015;42(2):147-157.
- Emil A. Tanagho, Jack W. McAninch. Smith’s General Urology. 17th edition, McGraw-Hill Companies, Inc., 2008
- Lorencin, , Anđelić, N., Španjol, J., & Car, Z. (2020). Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis. Artificial Intelligence in Medicine, 102, 101746.
- Lorencin, I., Baressi Šegota, S., Anđelić, N., Mrzljak, V., Ćabov, T., Španjol, J., & Car, Z. (2021). On Urinary Bladder Cancer Diagnosis: Utilization of Deep Convolutional Generative Adversarial Networks for Data Augmentation. Biology, 10(3),
- Ikeda, A., Nosato, H., Kochi, Y., Kojima, T., Kawai, K., Sakanashi, H., … & Nishiyama, H. (2020). Support system of cystoscopic diagnosis for bladder cancer based on artificial Journal of Endourology, 34(3), 352-358.
- Hashemi, S. M. R., Hassanpour, H., Kozegar, E., & Tan, T. (2020). Cystoscopic Image Classification Based on Combining MLP and International Journal of Nonlinear Analysis and Applications, 11(1), 93-105.
- Baressi Šegota, , Anđelić, N., Lorencin, I., Saga, M., & Car, Z. (2020). Path planning optimization of six-degree-of-freedom robotic manipulators using evolutionary algorithms. International Journal of Advanced Robotic Systems, 17(2), 1729881420908076.
- Katoch, S., Chauhan, S. S., & Kumar, V. (2021). A review on genetic algorithm: past, present, and future. Multimedia Tools and Applications, 80(5), 8091-8126.
- Lorencin, I., Anđelić, N., Mrzljak, V., & Car, Z. (2019). Genetic algorithm approach to design of multi-layer perceptron for combined cycle power plant electrical power output Energies, 12(22), 4352.