METHODS: Clinicopathological data were retrieved from the archived formal pathology reports for surgical specimens diagnosed as invasive ductal carcinoma, NOS. Microvessels were immunohistochemically stained with anti-CD34 antibody and quantified as microvessel density.

RESULTS: At least 50% of 94 cases of invasive breast ductal carcinoma in the study were advanced stage. The majority had poor prognosis factors such as tumor size larger than 50mm (48.9%), positive lymph node metastasis (60.6%), and tumor grade III (52.1%). Higher percentages of estrogen and progesterone receptor negative cases were recorded (46.8% and 46.8% respectively). Her-2 overexpression cases and triple negative breast cancers constituted 24.5% and 22.3% respectively. Significantly higher microvessel density was observed in the younger patient age group (p=0.012). There were no significant associations between microvessel density and other clinicopathological factors (p>0.05).

METHODS: Breast cancer MRI images were classified into BA, BF, BPT, BTA, MDC, MLC, MMC, and MPC using a proposed Deep Learning model with additional 5 fine-tuned Deep learning models consisting of Xception, InceptionV3, VGG16, MobileNet and ResNet50 trained on ImageNet database. The dataset was collected from Kaggle depository for breast cancer detection and classification. That Dataset was boosted using GAN technique. The images in the dataset have 4 magnifications (40X, 100X, 200X, 400X, and Complete Dataset). Thus we evaluated the proposed Deep Learning model and 5 pre-trained models using each dataset individually. That means we carried out a total of 30 experiments. The measurement that was used in the evaluation of all models includes: F1-score, recall, precision, accuracy.

RESULTS: The classification F1-score accuracies of Xception, InceptionV3, ResNet50, VGG16, MobileNet, and Proposed Model (BCCNN) were 97.54%, 95.33%, 98.14%, 97.67%, 93.98%, and 98.28%, respectively.