Combining modified hyper learning binary dragonfly algorithm and deep learning for BI‐RADS classification of breast masses in mammograms

Abstract

AbstractBackgroundIt has always been difficult and challenging to quantify the breast imaging reporting and data system (BI‐RADS) criteria into several categories. Automatic quantitation can assist clinicians in the early diagnosis and treatment eventually reducing the mortality rate. As a result, in the recent years, early BC diagnosis methods based on pathological breast images have been in high demand.MethodWe propose a computer‐aided diagnosis (CAD) system that combines the transfer learning approach with meta‐heuristic optimization, and machine learning to classify BI‐RADS breast masses categories within levels 3 and 4. Transfer learning technique ResNet‐18 is used for high‐level feature extraction. The clinically important features are then chosen using a modified feature selection technique based on the Hyper Learning Binary DragonFly Algorithm (M‐HLBDA). Finally, a Fine K‐nearest neighbour (KNN) is employed for classification.ResultA series of mammography breast mass images from the curated breast imaging subset of DDSM (CBIS‐DDSM) are evaluated in order to categorize within BI‐RADS levels 3 and 4. Experimental findings demonstrated M‐HLBDA capability to identify the optimal feature subset, which minimizes the number of selected features and maximizes the classification. Our system attained classification accuracy of 87.5%, Sensitivity of 88.8%, Specificity of 86.5%, and AUC of 0.82 using KNN on selected features using M‐HLBDA.ConclusionOur model can annotate and classify BI‐RADS levels 3 and 4 with better classification accuracy, and it may be used as an automated system to help radiologists.