Combining Gabor-local and contextual-global deep features for cholesteatoma classification

A key challenge in classifying cholesteatoma images lies in the effective extraction and integration of both local and global features. This paper addresses this challenge by introducing a Gabor-local and contextual-global network. This network enhances local feature extraction through the incorporation of Gabor filtering alongside a ResNet architecture, which captures richer local information at varying scales and orientations. Moreover, the attention mechanism generates an importance score map, enabling the network to dynamically focus on the most relevant regions within the input image by normalizing scores into a weight matrix and supporting Generalized-Mean (GeM) pooling, resulting in a more robust global descriptor. Finally, a novel dynamic feature fusion strategy combines local features, global features, and outputs from the last residual block based on their computed importance scores, further enhancing the model's overall performance. Experimental results show the network's superior performance, effectively leveraging both local and global information for enhanced classification outcomes.