Abstract
Advance perception and treatment of oral cancer are crucial for boosting survival chances. There are many deep learning techniques like Convolution Neural Network (CNN), Support vector Machine (SVM), and Region-Based CNN (R-CNN) that are being employed to identify cancer cells in their early stages. Incorrect labeling is a well-known problem in deep learning. It can take a long time to label a sufficiently large dataset. Uneven distribution of classes, accuracy, and insignificant characteristics are some of the major problems. This review paper offers a comprehensive review of the literature on several strategies for resolving the aforementioned problems. Review publications from several reputable online libraries, including IEEE, Springer Link, and ScienceDirect, have undergone thorough and critical evaluation. There are three specific study problems that have been addressed regarding the accuracy, class imbalance, and feature extraction. To address the questions, recent papers that were pertinent to the subjects were thoroughly studied. Throughout this Review paper, the authors looked into numerous methods for enhancing the deep learning algorithm’s capacity for prediction. The researchers offered effective methods like TOP-GAN, EfficientNet-B0, Resent-50, and VGG-16 for enhancing deep learning algorithms.
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Sharma, Y., Kaur, J. (2023). A Review of Deep Learning Algorithms for Early Detection of Oral Mouth Cancer. In: Murthy, B.K., Reddy, B.V.R., Hasteer, N., Van Belle, JP. (eds) Decision Intelligence. InCITe 2023. Lecture Notes in Electrical Engineering, vol 1079. Springer, Singapore. https://doi.org/10.1007/978-981-99-5997-6_18
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DOI: https://doi.org/10.1007/978-981-99-5997-6_18
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