Learning quality-guided multi-layer features for classifying visual types with ball sports application.

Nowadays, breast cancer is one of the leading causes of death among women. This highlights the need for precise X-ray image analysis in the medical and imaging fields. In this study, we present an advanced perceptual deep learning framework that extracts key features from large X-ray datasets, mimicking human visual perception. We begin by using a large dataset of breast cancer images and apply the BING objectness measure to identify relevant visual and semantic patches. To manage the large number of object-aware patches, we propose a new ranking technique in the weak annotation context. This technique identifies the patches that are most aligned with human visual judgment. These key patches are then aggregated to extract meaningful features from each image. We leverage these features to train a multi-class SVM classifier, which categorizes the images into various breast cancer stages. The effectiveness of our deep learning model is demonstrated through extensive comparative analysis and visual examples.