Machine learning is one of the prime aspects that is used in various applications of artificial intelligence and is widely used. In the area of machine learning, deep learning is observed to be of great interest to the researchers with the improvement of computer-based data processing. Recent works in this area of deep learning have paved way to the new innovations in science, technology and applied research which has a wide application for identification and classification of images. Such a classification is quite imperative when security is of prime concern. Deep learning is basically an artificial intelligence-machine learning hybrid. This has provided a versatile and precise model that can result in better accuracy. However, theoretical designs and experiments that are existing till date are very complex. So, there is a need to develop techniques that reduce the computational complexity. Deep learning can be used to solve various problems in the study of images and patterns. Image Segmentation is one of such applications. This paper explores the recent work that is carried out in image segmentation using Deep Learning. Many methods that are introduced for image segmentation are based on supervised classification. In general, such methods work well if the training set are representative of the test images in the segment. However, issues can occur in the course of training and test results, due to the impairment in the hardware and the concerned protocols that are existing in various distributions. The weights that are assigned to the features need to be adaptively chosen for proper classification of the segmentation area. This further improves the processing capability of the algorithm so developed.

Deep Learning, Artificial Intelligence, Data Science, Image Segmentation, Supervised Classification

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