An Efficient Lightweight Multi Head Attention Gannet Convolutional Neural Network Based Mammograms Classification

Abstract

Background: This research aims to use deep learning to create automated systems for better breast cancer detection and categorisation in mammogram images, helping medical professionals overcome challenges such as time consumption, feature extraction issues and limited training models. Methods: This research introduced a Lightweight Multihead attention Gannet Convolutional Neural Network (LMGCNN) to classify mammogram images effectively. It used wiener filtering, unsharp masking, and adaptive histogram equalisation to enhance images and remove noise, followed by Grey-Level Co-occurrence Matrix (GLCM) for feature extraction. Ideal feature selection is done by a self-adaptive quantum equilibrium optimiser with artificial bee colony. Results: The research assessed on two datasets, CBIS-DDSM and MIAS, achieving impressive accuracy rates of 98.2% and 99.9%, respectively, which highlight the superior performance of the LMGCNN model while accurately detecting breast cancer compared to previous models. Conclusion: This method illustrates potential in aiding initial and accurate breast cancer detection, possibly leading to improved patient outcomes.