Breast cancer is one of the common reasons for deaths of women over the globe. It has been found that a Computer- Aided Diagnosis (CAD) system can be designed using X-ray mammograms for early-stage detection of breast cancer, which can decrease the death rate to a large extend. This paper work proposes a novel 2-way threshold based Intelligent water drops (IWD) algorithm for feature selection to design an effective and efficient CAD system that can detect breast cancer in early stage. This approach first extracts the Local Binary Patterns (LBP) in wavelet domain from mammograms and then apply our introduced 2-way threshold based (IWD) algorithm to extract most important subset of features from the extracted features set. 2-way thresholding is a technique to find a lower bound (LB) and an upper bound (UB) on the number of features to be selected in the optimal subset. So, using these threshold values IWD is capable of producing multiple optimal subsets of features rather than producing a single optimal subset of features. The best subset among the above subsets is then used train and deploy Support Vector Machine (SVM) to classify new mammograms. The results have shown that the proposed model outperforms many of the existing CAD systems. Further we have compared our introduced feature selection technique with other meta heuristic features selection techniques such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Genetic Algorithm (GA), Gravitational Search Algorithm (GSA), Inclined Planes System Optimization (IPO) and Grey Wolf Optimization Algorithm (GWO) and found that it outperforms the others. The accuracy, precision, recall, specificity and F1-score of our proposed framework are measured as 99%, 98.7% ,98.123%, 96.2% and 98.4% respectively.