Breast cancer is a commonly diagnosed disease in women. Early detection, a personalized treatment approach, and better understanding are necessary for cancer patients to survive. In this work, both Deep learning Network and traditional Convolution Network has been employed for Digital Database for Screening Mammography (DDSM) dataset. In this work, breast cancer images are subjected to removal of background images followed by Weiner filtering and Contrast Limited Histogram Equalization (CLAHE) filter for image restoration. Wavelet Packet Decomposition (WPD) using Daubechies wavelet level 3 (db3) is employed to improve the smoothness of the images. In the first part of breast cancer recognition, these preprocessed images are fed to a deep convolution neural network, namely GoogleNet and AlexNetfor ADAM. RMSPROP and SGDM optimizers for different learning rates such as 0.01, 0.001, and 0.0001. As medical image necessitates discriminative features for classification, the pre-trained GoogleNet architectures extract the complicated features from the image and increase the recognition rate. In the latter part of this paper, Particle Swarm Optimization based Multi-Layer Perceptron (PSO-MLP), and Ant Colony Optimization based Multi-Layer Perceptron (ACO-MLP) are employed for breast cancer recognition using statistical features like skewness, kurtosis, variance, entropy, contrast, correlation, energy, homogeneity, mean which are extracted from the preprocessed image. The performance of this GoogleNet has been compared with AlexNet, PSO-MLP, and ACO-MLP in terms of accuracy, loss rate, and runtime and achieves an accuracy of 99%, with less loss rate of 0.1547 and the lowest run time of 4.14 minutes.