The very long delay that is suffered by patients of breast cancer in their early stages in low-income countries is due to access barriers and quality deficiencies in the care of cancer giving rise to the need for an alternative and efficient computer-based diagnostic system for the early detection and prevention of the disease. The early detection and improved therapy still remain a crucial approach for the prevention and cure of breast cancer. To this end, recent research looks into the development of different classifier models for the classification of breast cancer. This paper investigates the potentials of applying multiple neural network architectures with increased number of hidden layers and hidden units. The network architectures have one-hidden-layer, two-hidden-layer and three hidden layer (deep neural network) architectures respectively using the backpropagation training algorithm for the training of the models. The experimental results show that by applying this approach the models yield efficient and promising results

The quest to develop software of great quality with timely delivery and tested components gave birth to reuse. Component reusability entails the use (re-use) of existing artefacts to improve the quality and functionalities of software. Many approaches have been used by different researchers and applied to different metrics to assess software component reusability level. In addition to the common quality factors used by many authors, such as customisability, interface complexity, portability and understandability, this study introduces and justifies stability, in the context of volatility as a factor that determines the reusability of software components. Sixty-nine software components were collected from third party software vendors and data extracted from their features were used to compute the metric values of the five (5) selected quality factors. Genetic-Fuzzy System (GFS) was used to predict the level of the components' reusability. The performance of the GFS was compared with that of Adaptive Neuro-Fuzzy Inference System (ANFIS) approach using their corresponding average RMSE (Root Mean Square Error), in order to ascertain the level of accuracy of the prediction. The results of the findings showed that, GFS with an RMSE of 0.0019 provides better reusability prediction accuracy compare to ANFIS with an RMSE of 0.1480.