Data Fusion Architecture Empowered with Deep Learning for Breast Cancer Classification

Breast cancer (BC) is the most widespread tumor in females worldwide and is a severe public health issue. BC is the leading reason of death affecting females between the ages of 20 to 59 around the world. Early detection and therapy can help women receive effective treatment and, as a result, decrease the rate of breast cancer disease. The cancer tumor develops when cells grow improperly and attack the healthy tissue in the human body. Tumors are classified as benign or malignant, and the absence of cancer in the breast is considered normal. Deep learning, machine learning, and transfer learning models are applied to detect and identify cancerous tissue like BC. This research assists in the identification and classification of BC. We implemented the pre-trained model AlexNet and proposed model Breast cancer identification and classification (BCIC), which are machine learning-based models, by evaluating them in the form of comparative research. We used 3 datasets, A, B, and C. We fuzzed these datasets and got 2 datasets, A2C and B3C. Dataset A2C is the fusion of A, B, and C with 2 classes categorized as benign and malignant. Dataset B3C is the fusion of datasets A, B, and C with 3 classes classified as benign, malignant, and normal. We used customized AlexNet according to our datasets and BCIC in our proposed model. We achieved an accuracy of 86.5% on Dataset B3C and 76.8% on Dataset A2C by using AlexNet, and we achieved the optimum accuracy of 94.5% on Dataset B3C and 94.9% on Dataset A2C by using proposed model BCIC at 40 epochs with 0.00008 learning rate. We proposed fuzzed dataset model using transfer learning. We fuzzed three datasets to get more accurate results and the proposed model achieved the highest prediction accuracy using fuzzed dataset transfer learning technique.