Med-IC: Fusing a Single Layer Involution with Convolutions for Enhanced Medical Image Classification and Segmentation

TL;DR

This paper introduces Med-IC, a method that combines a single involution layer with CNNs to improve medical image classification and segmentation, achieving better results with fewer parameters.

Contribution

The study demonstrates that adding a single involution layer before CNNs enhances performance in medical imaging tasks, with minimal increase in model complexity.

Findings

Single involution layer improves classification accuracy.

Adding only one involution layer outperforms previous methods.

Excessive involution layers can decrease prediction accuracy.

Abstract

The majority of medical images, especially those that resemble cells, have similar characteristics. These images, which occur in a variety of shapes, often show abnormalities in the organ or cell region. The convolution operation possesses a restricted capability to extract visual patterns across several spatial regions of an image. The involution process, which is the inverse operation of convolution, complements this inherent lack of spatial information extraction present in convolutions. In this study, we investigate how applying a single layer of involution prior to a convolutional neural network (CNN) architecture can significantly improve classification and segmentation performance, with a comparatively negligible amount of weight parameters. The study additionally shows how excessive use of involution layers might result in inaccurate predictions in a particular type of medical image. According to our findings from experiments, the strategy of adding only a single involution layer before a CNN-based model outperforms most of the previous works.