A hybrid architecture with separable convolutions and attention for lung and colon cancer detection

Early and accurate detection of lung and colon cancer is critical for improving patient survival rates. However, conventional classification techniques often struggle with the complexity of medical imaging data. In this study, we propose a novel hybrid architecture, the Separable Convolution and Attention Mechanism (SCA-mechanism), to enhance the accuracy of cancer classification. The proposed model integrates separable convolutions with residual connections to facilitate efficient feature extraction while incorporating attention mechanisms to emphasize key regions indicative of malignancy. We evaluated the performance of the SCA-mechanism model against state-of-the-art deep learning techniques using a benchmark dataset of lung and colon cancer images. Experimental results demonstrate that our proposed approach achieves superior classification performance, outperforming conventional deep learning methods in both accuracy and computational efficiency. These findings highlight the potential of the SCA-mechanism as a promising tool for automated cancer diagnosis, offering significant advancements in computationally efficient medical applications and clinical decision making.