Abstract: The new generation of radio telescopes, such as the MeerKAT, ASKAP (Australian Square Kilometre Array Pathfinder) and the future Square Kilometre Array (SKA), are expected to produce vast amounts of data and images in the petabyte region. Therefore, the amount of incoming data at a specific time will overwhelm any current traditional data analysis method deployed. Deep learning architectures have been applied in many fields, such as social network filtering and medical image analysis. They have produced results which are comparable to human expert performance. Hence, it is appealing to apply it to radio astronomy data. The images from these telescopes have a high density of radio sources, making it difficult to classify the sources in the image. Identifying and segmenting sources from radio images is a pre-processing step before sources are put into different classes. Thus, it is necessary to automatically segment the sources from the images before they can be classified. This work uses the Unet architecture to segment radio sources from radio images with 99.6 % accuracy. Thereafter, we use OpenCV tools to detect the sources and draw borders around them. PyBDSF and Unet were compared in the same environment ( computing power, images and dataset size), and it occurred that Unet is $35$ times faster than pyBDSF. This is the unique selling point of traditional vs deep learning approaches to radio images. Since MeerKAT is expected to produce a catalogue of about 70 million galaxies, this tool will speed up the runtime to produce this catalogue. The limitation of this work is that it uses pyBDSF as the ground truth image and cannot outperform it. For future improvements, hand-crafted mask images will be created as ground truth.