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Objective: Breast cancer patients who have a rapid diagnosis have been better prognosis than late diagnosis. The popular screening is mammogram or ultrasound. In recent years, researchers try to develop data driven models to predict early cancer staging from the first screening. However, data elements are not complete such as lymph node status. Therefore, the Integrated dataset approach will be challenging. Methods: Because the data elements are not collected from the same source, joining between mammography and biopsy data were performed using latent variables that determine by tumor severity. The datasets consist of 445 mammography reports and 183 pathological reports. The latent variables of the mammogram dataset were determined by the severity of mass, while latent variables of the pathological dataset were determined by TNM Staging. The latent variables were used to join between two datasets. Then, the prediction models were built using the machine learning technique. The modeling is divided into three steps; staging prediction, lymph node prediction, and prognosis. Results: Integrated dataset from mammography and biopsy extend more factors and built the models to predict breast cancer staging in the mammography process. The staging prediction is 100% accuracy. The lymph node prediction are 72.47% accuracy, 73.94% specificity, and 72.5% sensitivity. An area under ROC curve is 0.74. The prognosis model prediction are 72.72% accuracy, 80% specificity, and 77% sensitivity. An area under ROC curve is 0.87. There are also built the rule for early staging, diagnosis, and prognosis. Conclusion:This study aims to build the models for early staging, diagnosis, and prognosis using the less aggressive method. The advantages are (1) predict staging from the first screening (2) estimate the lymph node metastases for planning to ALND or SLNB (3) evaluate overall survival time. These advantages help the physician planning the best treatment for cancer patients.

Objective: This study aims to develop automatic breast tumor detection and classification including automatic tumor volume estimation using deep learning techniques based on computerized analysis of breast ultrasound images. When the skill levels of the radiologists and image quality are important to detect and diagnose the tumor using handheld ultrasound, the ability of this approach tends to assist the radiologist's decision for breast cancer diagnosis. Material and Methods: Breast ultrasound images were provided by the

Chest radiography (CXR) image is usually required for lung severity assessment. However, chest X-rays in COVID-19 interpretation is required expert radiologists’ knowledge. This study aims to improve the COVID-19 X-ray image classification using feature selection technique by the regression mutual information deep convolution neuron networks (RMI Deep-CNNs). The dataset consists of 219 COVID-19, 500 viral pneumonias, and 500 normal chest X-ray images. CXR images were comprehensively pre-trained using DCNNs to extract the very large image features, then, the feature selection could reduce the complexity of a model and reduce the model overfitting. Therefore, the critical features were selected using regression mutual information followed by the fully connected with softmax layer for classification. For the classification of two alternative systems, these networks were compared (ResNet152V2 and InceptionV3). The classification performance for both schemes were 92.21%, 100%, 90% and 91.39%, 100%, 82.50%, respectively. In addition, RMI Deep-CNNs not only improve the accuracy but also reduce trainable features by over 80%. This approach tends to significantly improve the computation time and model accuracy for COVID‐19 classification.

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