Automatic MRI Image Classification Using Attention and Residual CNNs With Enhanced Image Denoising Filters

In clinical diagnosis, magnetic resonance imaging (MRI) plays a vital role in analyzing soft tissues. However, the images are affected by noise that is random in nature. The noise affects the quality of the image, which impacts the accuracy of diagnosis. To address this, in this paper, four enhanced versions of filters: enhanced Lee filter (ELF), enhanced Frost filter (EFF), enhanced Kuan filter (EKF) and enhanced Boxcar filter (EBCF) are used. The enhanced image features are used for classification with three different CNN classification models: Convolutional Neural Network (CNN), CNN with attention module (CNN-AM) and CNN with a residual module (CNN-RM). The enhanced filters help in improving the features. In the proposed architecture, the spatial attention module achieves these benefits by applying operations such as average pooling, max pooling, and sigmoid activation to selectively highlight key spatial regions in MRI images, leading to improved classification accuracy and overall performance. The proposed methods are compared with the state-of-the-art methods, and they ensure better image enhancement with high PSNR values of 32.1, 36.7 and 39.1, respectively. The classification models ensure high accuracy of 94%, 95% and 98%.