Local Features Based Deep Learning for Mammographic Image Classification: In Comparison to CNN Models
Ardiant Utomo, Edwin Farrel Juniawan, Vincent Lioe, Diaz D. Santika · Procedia Computer Science · 2021
Convolutional neural networks (CNN) have achieved tremendous success in tackling various tasks of computer vision problems such as classification of images and recognition of objects. The general trend has been to make networks larger and more complex in order to achieve higher precision. In any case, these advances to improve precision are not really making the systems increasingly effective for size and speed. In this report, an attempt to overcome the aforementioned issues is described. Some well-known scale and rotation invariant local features such as SIFT, SURF, ORB, BRISK, and KAZE were first observed to check which of these having the strong potentiality to replace inclusively the convolutional layers of CNN models. To do this, while retaining the fully connected layers of the corresponding CNN model as the classifier, the convolutional parts of the model were inclusively taken out and assigned the high-level feature maps at the flatten layer with scale and rotation invariant local features. The computational experiments were carried out using the challenging mammographic images provided by MIAS (mammographic image analysis society). It contains 322 grayscale images from 2 different severity, each of which has 1024x1024 pixels. Employing only the fully connected layers of the VGG16 model as the classifier, it is found that SIFT and SURF based features offer much better training and testing accuracies compared to ORB, BRISK, and KAZE in classifying MIAS image datasets. Using SIFT and SURF local features and employing only VGG16 or MobileNetV2 fully connected layers as a classifier, it can be shown that SIFT and SURF based deep learning exhibit state-of-the-art performance on MIAS datasets. SIFT and SURF based deep learning are capable of outperforming the performance of the original VGG16 and MobileNetV2 configuration in terms of accuracy and training time needed on the same datasets.