accurate diagnosis of skin diseases, particularly melanoma is crucial for effective treatment. Traditional approaches for skin disease classification face challenges due to the diverse and subjective nature of skin lesion, therefore, it is difficult to distinguish between situations that have visual similarities. This paper presents a novel deep learning based model to address these challenges. The model integrates dual attention mechanisms with a bilinear Convolution Neural Network (CNN) to extract discriminative features from dermoscopic images. Generate an Attention Heat Maps (AHMs) for the crop regions of the input images to focus on significant regions and a novel loss function, which combined complementary entropy and cross-entropy for mitigating class imbalance issues and improve model performance. The proposed model achieving an accuracy of 94.24% on ISIC2019 and 92.86% on HAM10000.