With the development of Convolutional Neural Networks (CNNs) in the past decade, Artificial Intelligence has made giant strides in medical image classification. Different CNN architectures, like Dense-Net, Res-Net, etc., are used in the medical industry to identify patterns and features that would lead to a faster diagnosis. The fundamental motivation behind this research article is to study the application of different variants of Dense-Net architecture (DenseNet121, 169, and 201) towards breast cancer detection and provide a comparative analysis of Dense-net variants to the intended area of research with the support of digital mammography. A single patient's two mediolateral oblique (MLO) and two craniocaudal (CC) views are used to extract the distinct features of breast cancer during detection. The proposed research utilizes 9695 digital mammography images for this study. All input images are classified into three categories: Benign, Cancer, and Normal, with the help of expert radiologists as ground truth. All the proposed classifier's performances are tested with different testing matrices such as precision, responsiveness, and specificity. The concluding results demonstrate that these intended Dense-net architecture variants have delivered an exemplary performance with the highest accuracy of 94.90% during training and 96.924% during testing on CC views. Precision, Recall, and F1 score are found to be 0.965, 0.969, and 0.967, respectively. A comparative analysis of the proposed model with its variants and other state-of-the-art methods is provided. Comparative research shows that DenseNet architecture can provide more accurate results when only left CC views are used as input. Acquired outcomes are again validated qualitatively with a radiologist expert in the field of breast cancer. The proposed architecture achieved state-of-the-art results with a smaller number of images and with less computation power.