A Multiscale Fusion Network Integrating ConvNeXtSmall and EfficientNetB0 with Enhanced Image Preprocessing for Robust Classification

In this paper, we introduce the Multiscale Fusion Network (MFNet), a pioneering approach that combines ConvNeXtSmall and EfficientNetB0 architectures with advanced preprocessing techniques to revolutionize image classification. By integrating Multiscale Retinex and GrabCut segmentation, MFNet enhances perceptual quality, extracts meaningful features, and minimizes background noise. Trained on a meticulously prepared dataset, our model demonstrates superior performance, achieving high accuracy and robustness across diverse datasets. This paper delves into the architectural synergy, preprocessing innovations, and rigorous evaluation that establish MFNet as a leading solution for image classification tasks.