Abstract
Automatic diagnostic tools have been extensively implemented in medical diagnosis processes of different diseases. In this regard, breast cancer diagnosis is particularly important as it becomes one of the most dangerous diseases for women. Consequently, regular and preemptive screening for breast cancer could help initiate treatment earlier and more effectively. In this regard, hospitals and clinics are in need to a robust diagnostic tool that could provide reliable results. The accuracy of diagnostic tools is an important factor that should be taken into consideration when designing a new system. This has motivated us to develop an automatic diagnostic system combining two methodologies: Deep Feedforward Neural Networks (DFNNs) and swarm intelligence algorithms. Swarm intelligence techniques are based on Particle Swarm Optimization (PSO) as well as the Mother Tree Optimization (MTO) algorithm we proposed in the past. In order to asses the performance, in terms of accuracy, of the proposed system, we have conducted several experiments using the Wisconsin Breast Cancer Dataset (WBCD). The results show that the DFNN combined with a variant of our MTO attains a high classification performance, reaching 100% precision.
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Korani, W., Mouhoub, M. (2020). Breast Cancer Diagnostic Tool Using Deep Feedforward Neural Network and Mother Tree Optimization. In: Dorronsoro, B., Ruiz, P., de la Torre, J., Urda, D., Talbi, EG. (eds) Optimization and Learning. OLA 2020. Communications in Computer and Information Science, vol 1173. Springer, Cham. https://doi.org/10.1007/978-3-030-41913-4_19
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