Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for models that efficiently diagnose cancer is still challenging. In this work, we proposed data exploratory techniques (DET) and developed four different predictive models to improve breast cancer diagnostic accuracy. Prior to models, four-layered essential DET, e.g., feature distribution, correlation, elimination, and hyperparameter optimization, were deep-dived to identify the robust feature classification into malignant and benign classes. These proposed techniques and classifiers were implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets. Standard performance metrics, including confusion matrices and K-fold cross-validation techniques, were applied to assess each classifier’s efficiency and training time. The models’ diagnostic capability improved with our DET, i.e., polynomial SVM gained 99.3%, LR with 98.06%, KNN acquired 97.35%, and EC achieved 97.61% accuracy with the WDBC dataset. We also compared our significant results with previous studies in terms of accuracy. The implementation procedure and findings can guide physicians to adopt an effective model for a practical understanding and prognosis of breast cancer tumors.

Automatic Check-Out (ACO) provides an object detection based mechanism for retailers to process the purchases of customers automatically. However, it suffers a lot from the domain shift problem because of different data distribution between the single item in training exemplar images and mixed items in testing checkout images. In this paper, we propose a new iterative knowledge distillation method to solve the domain adaptation problem for this task. First, we develop a new augmentation data strategy to generate synthesized checkout images. It can extract segmented items from the training images by the coarse-to-fine strategy and filter items with unrealistic poses by pose pruning. Second, we propose a dual pyramid scale network (DPSNet) to exploit the multi-scale feature representation in joint detection and counting views. Third, the iterative knowledge distillation training strategy is developed to make full use of both image-level and instance-level samples to narrow the semantic gap between source domain and target domain. Extensive experiments on the large-scale Retail Product Checkout (RPC) dataset show the proposed DPSNet can achieve state-of-the-art performance compared with existing methods. The source codes can be found at https://isrc.iscas.ac.cn/gitlab/research/dpsnet.