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One of the problems that often arise during the application of medical research to real life is the high number of false positive cases. This situation causes experts to be warned with false alarms unnecessarily and increases their workload. This study proposes a new data centric approach to reduce bias-based false positive predictions in brain MRI-specific medical object detection applications. The proposed method has been tested using two different datasets: Gazi Brains 2020 and BraTS 2020, and three different deep learning-based object detection models: Mask R-CNN, YOLOv5, and EfficientDet. According to the results, the proposed pipeline outperformed the classical pipeline, up to 18% on the Gazi Brains 2020 dataset, and up to 24% on the BraTS 2020 dataset for mean specificity value without much change in sensitivity metric. It means that the proposed pipeline reduces false positive rates due to bias in real-life applications and it can help to reduce the workload of experts.

Big data phenomenon is a concept for large, heterogeneous and complex data sets and having many challenges in storing, preparing, analyzing and visualizing as well as techniques and technologies for making better decision and services. Uncovered hidden patterns, unknown or unpredicted relations and secret correlations are achieved via big data analytics. This might help companies and organizations to have new ideas, get richer and deeper insights, broaden their horizons, get advantages over their competitors, etc. To make big data analytics easy and efficient, a lot of big data techniques and technologies have been developed. In this article, the chronological development of batch, real-time and hybrid technologies, their advantages and disadvantages have been reviewed. A number of criticism have been focused on available processing techniques and technologies. This paper will be a roadmap for researchers who work on big data analytics.