A comparative performance study of random-grid model for hyperparameters selection in detection of abnormalities in digital breast images

Deep learning models have been widely reported to have achieved significant performance in image processing and classification tasks. They have mainly been harnessed and applied to the problem of detecting abnormalities in digital breast images. However, the significant number and high dimensional space requirement of hyperparameters in deep learning models often make it challenging to find the best configuration for such parameters when tuning for best performance. In appropriately handling this performance tuning often lead to difficulty in striking balance between underfitting and overfitting. This article proposes an optimized convolutional neural network (CNN) architecture through application of hybrid selection model in obtaining best hyperparameter configuration which outperforms similar existing models. We approached this non-trivial challenge by defining a hybrid of the grid-based and random-based model for the selection of hyperparameters and then investigate the performance of the configurations. To further improve the performance of the CNN model, data augmentation technique was applied. Furthermore, the study undertook a comparative study of the performance of the best configuration on some benchmarked datasets. The resulting model was applied to publicly available benchmark datasets, namely, the DDSM and MIAS datasets. Findings from the experimentations revealed that hyperparameters with Adam optimization algorithm showed superiority by yielding an accuracy of 1.0 using DDSM dataset, while SGD, RMSprop, Adam, and Adagrad output an accuracy of 0.9375 with MIAS dataset. The outcome of this study further strengthens the appropriateness of Adam optimizer and has also produced a state-of-the-art CNN model suitable for solving the problem of detection and classification of breast cancer from digital mammography.