Keywords : Mean Weight Convolution Neural Network (MWCNN)
BLENDED KERNEL FUZZY LOCAL INFORMATION C-MEANS (BKFLICM) CLUSTERING BASED EDGE DETECTION FOR LUNG IMAGES
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 9, Pages 1920-1937
The medical diagnosis and clinical practice greatly demands medical image classification, an emerging area of research which includes modern medical imaging technology. Recently, Fuzzy Bat Algorithm (FBA) with Mean Weight Convolution Neural Network (MWCNN) algorithm was proposed for Region of Interest (RoI) area detection in the lung images in order to increase the classification accuracy. The image processing system outcomes are influenced by edge detection e.g. region segmentation, objects detection. Edge detection is done through Blended Kernel Based Fuzzy Local Information C-Means (BKFLICM) technique and construction of gradients in the scale is achieved by clustering of all image pixels in a feature space. The image segmentation mainly relies on the pixel intensity which is used for assessing resemblance amidst pixels. The edge detection using BKFLICM is performed by formation of new kernel range which is obtained by merging hyperbolic tangent kernel and Gaussian kernel. The special feature of BKFLICM is the fuzzy local (gray level) similarity measure through the kernel function. This does the edge detection perfectly while preserving the image details following which FBA and MWCNN classifier are utilized for segmentation and classification respectively. The training of lung image classification deprived of severe over-fitting is mainly done through MWCNN with sufficient labelled images and improved accuracy is also obtained for (LIDC-IDRI) database. The performance metrics such as accuracy, precision, recall, and F-measure values are also enhanced using the proposed algorithm which is validated by the experimental outcomes.