The emergence of deep learning has ushered in a transformative era within the realm of image processing, notably in the context of Computed Tomography (CT). Nevertheless, it is noteworthy that a majority of image processing algorithms traditionally rely on processed or reconstructed images, often overlooking the raw sensor data. This thesis, however, shifts its focus toward the utilization of unprocessed computed tomography data, which we refer to as sinogram. Within this framework, we present a comprehensive three-step deep learning algorithm, leveraging a UNet-based architecture, designed to identify and analyze defects within objects without resorting to image reconstruction. The initial phase entails sinogram segmentation, facilitating the extraction of defect masks within the sinogram. Subsequently, instance segmentation is employed to effectively segregate these masks, resulting in their individualization. Lastly, the isolated masks are subjected to thorough defect analysis. Our research endeavors encompass comprehensive experimentation, conducted on both simulated datasets and real-world data.