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Endoscopy plays a crucial role in the early diagnosis of colon cancer. The manual processing of images by skilled endoscopists is time-consuming, making automatic image classification highly valuable. We propose a novel multi-label classification method that integrates complementary learning from both local and global approaches. The model comprises a Swin Transformer branch for global feature extraction and a modified VGG16-based CNN branch for local feature analysis. The learning capability of the CNN branch is enhanced by concatenating a saliency map and the prediction of a texture feature vector through a multi-task learning framework. The proposed method outperformed state-of-the-art techniques, achieving an F1-score of 96.08% and an accuracy of 96.06% on the classification of the Kvasir-v2 dataset of endoscopic images.Clinical Relevance-Experimental results demonstrate the superiority of the proposed model for classifying endoscopic images, paving the way for enhanced diagnostic performance in clinical settings.