BC (Breast Cancer) is predominantly prevalent in women and is the leading cause of mortality due to cancers which can be reduced using mammography screenings. The use of CNNs (Convolution Neural Networks), a type of deep learning method has proven to be highly successful in image identifications. However, the quality of acquired mammographic images is found to be low while being used by detection models and hence this work proposed hybrid MLTs (Machine learning Techniques) for overcoming low-quality issues in the prediction of BCs. Initially, Statistical correlation analysis-based pre-processing is introduced for improving classifier performances followed by a hybrid model which predicts BCs effectively. This work also introduces a novel building block called Fuzzy Scoring based Resnets (Residual Networks) and CNNs called FS-Resnet CNNs for optimizing networks. The proposed FS-Resnet CNN model is computationally efficient, less sensitive to noises, and efficient in memory usage. Experimental results show that the proposed model achieves 95% accuracy ,95.45% precision, 93% recall rate, 94.21% f-measure and 18.113(S)-time complexity.