Breast cancer is the most common cancer among women worldwide. Early detection of different breast cancer cell types is crucial for successful treatment. Deep learning models have shown great promise in detecting cancer cells from medical images.

In our study, we utilized DeepLabV3Plus with EfficientNet-B0 as the backbone for detecting different breast cancer cell types, including LCIS, DCIS, ILC, IDC, and OC. We selected DeepLabV3Plus as it is a state-of-the-art semantic segmentation model that has shown superior performance in medical image analysis tasks. EfficientNet-B0 was chosen as the backbone due to its compact size and high accuracy.

To improve the robustness of our model, we used various augmentation techniques such as Flip, Affine, Elastic, Noise, and Normalize. These techniques help the model to learn various orientations, deformations, and lighting conditions, making it more effective in detecting cancer cells.

We also used Imagenet as the pretrained model. Pretraining on a large dataset like Imagenet helps the model to learn high-level features and patterns, which can then be fine-tuned on our specific task of detecting breast cancer cell types.

Our experimental results showed that our model achieved high accuracy in detecting different breast cancer cell types. The use of deep learning models in medical imaging is a promising area of research that has the potential to significantly improve early detection and treatment of various diseases.