<p>Among the feminine, Infiltrating Ductal Carcinoma (IDC) is one of the most common but threatening types of breast cancer that originates in the milk ducts of the breast. With the timespan, it moves to the nearby healthy tissue and starts destroying the healthy cells and is thus considered life-threatening. To diagnose the IDC in the early stages many Computer Aided Diagnosis (CAD) systems were introduced in the past for making precise judgments. The objective of such systems is to make a judgment about the presence of the abnormality in the microscopic imaging with good accuracy. To make such an intelligent system there is always a scope to enhance the existing architectures for making better interpretations of the sample cases. To fulfill such an objective this paper presents the new deep-learning network architecture for classification of the microscopic images. The proposed model architecture helps to differentiate between normal and abnormal microscopic images when tested clinically using CAD systems. The designed model architecture uses the patch-intuited methodology to process the input images using Patch-Intuited Dense Deep Network blocks. Experimentally, it is proved that the proposed model performance is providing acceptable classification accuracy of 98.6% when tested on randomly selected microscopic images. Also, the comparative analysis with existing models proves the efficacy of the model.</p>