Abstract
In the medical image segmentation field, automation is a vital step toward illness detection and thus prevention. Once the segmentation is completed, brain tumors are easily detectable. Automated segmentation of brain tumor is an important research field for assisting radiologists in effectively diagnosing brain tumors. Many deep learning techniques like convolutional neural networks, deep belief networks, and others have been proposed for the automated brain tumor segmentation. The latest deep learning models are discussed in this study based on their performance, dice score, accuracy, sensitivity, and specificity. It also emphasizes the uniqueness of each model, as well as its benefits and drawbacks. This review also looks at some of the most prevalent concerns about utilizing this sort of classifier, as well as some of the most notable changes in regularly used MRI modalities for brain tumor diagnosis. Furthermore, this research establishes limitations, remedies, and future trends or offers up advanced challenges for researchers to produce an efficient system with clinically acceptable accuracy that aids radiologists in determining the prognosis of brain tumors.
Authors Roohi Sille and Tanupriya Choudhury contributed equally and all are the first author.
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Sille, R., Choudhury, T., Chauhan, P., Mehdi, H.F., Sharma, D. (2023). Empirical Study on Categorized Deep Learning Frameworks for Segmentation of Brain Tumor. In: Reddy, K.A., Devi, B.R., George, B., Raju, K.S., Sellathurai, M. (eds) Proceedings of Fourth International Conference on Computer and Communication Technologies. Lecture Notes in Networks and Systems, vol 606. Springer, Singapore. https://doi.org/10.1007/978-981-19-8563-8_51
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