CGPNet: Enhancing Medical Image Classification through Channel Grouping and Partial Convolution Network

With the rapid development of artificial intelligence (AI), various industries have been propelled forward. In the field of medicine, AI has proven to be invaluable in aiding doctors to gain further insights into medical conditions, medical imaging is a prime example. Indeed, the availability of large-scale datasets like ImageNet has significantly contributed to the success of deep learning models in various computer vision tasks. However, when it comes to medical image classification, the availability of large, labeled datasets is relatively limited. As a result, the number of models specifically trained for medical image classification is comparatively smaller. This paper presents a novel neural network, named Channel Grouping and Partial Convolution Network (CGPNet), built upon the foundation of the InceptionNext model. Leveraging the commonly used lightweight technique, along with the introduction of grouped partial convolutions and dynamic convolutions, our proposed network exhibits promising performance. In particular, experiments were conducted on the ISIC-2019 (International Skin Imaging Collaboration) dataset, which demonstrates that our model achieves 2.61% improvement in top-1 accuracy compared to InceptionNext.