Cervical cancer classification using a novel hybrid approach

When compared to CNN models, self-attention blocks of vision transformer models are more accurate in classification tests and are generally better at capturing global contextual information inside an input image. The architecture's CSP blocks are ideal for classification tasks with constrained resources where efficiency and speed are balanced; as a result, they are suitable for local feature extraction. Again, in cervical cells, objects are of varying sizes. Therefore, SPPF records contextual information at different receptive fields and performs multi-scale feature extraction. Hence, we can understand the images more precisely and reliably by incorporating all these benefits in our suggested CASPNet model.