This paper presents a lightweight and accurate convolution neural network (CNN) based on encoder in vision transformer structure, which uses multigroup convolution rather than multilayer perceptron and multiheaded self-attention. We propose a group convolution block called multigroup convolution attention (MGCA) and squeeze and excitation (SE). The MGCA includes two parts: three 1 \(\times\) 1 convolutions concatenated along the channel dimension and depth-wise separable convolution. SE is used as a skip connection to provide long-range dependencies. MGCA-SE is introduced to reduce the number of parameters in state-of-the-art network in order to use fewer datasets for training CNN. Furthermore, we provide a lightweight network based on MGCA-SE in Resnet architecture called Resnet-multigroup convolution attention-squeeze and excitation (Res-MGCA-SE) in order to have early detection and treatment of medical images. Finally, Res-MGCA-SE is evaluated on lung cancer and Covid-19 chest X-ray and CT and images. According to our research findings, MGCA-SE can change convolutional layers in state-of-the-art networks and switch them to lightweight networks with properties comparable to heavy-weight networks.
