Classification of Cancer Cells and Dental Caries Detection using Deep Learning Algorithms
Journal Title: International Journal for Modern Trends in Science and Technology - Year 2024, Vol 10, Issue 11
Abstract
Detecting cancer cells, particularly within dental cavities, is not typical, as dental cavities are mainly connected with tooth decay caused by bacterial activity. However, cancers of the oral cavity, such as oral squamous cell carcinoma, can sometimes be found in the mouth, including on the gums, tongue, and other tissues. Dentists often thoroughly examine the oral cavity to look for abnormal areas. A biopsy may be performed to determine if cancer cells are present if a suspicious lesion is found. This process, while effective, can be time-consuming. In this paper, an Automated Deep Model (ADM) is developed to detect and classify cancer cells and teeth caries based on the regions affected, potentially speeding up the detection and diagnosis process. The proposed approach works for both cancer and dental caries detection, involving steps like training, preprocessing, and segmentation to improve model performance. Deep learning algorithms have been increasingly applied to classify cancer cells and detect dental caries in the mouth. The proposed approach combines pre-trained RESNET50 with transfer learning and classification model Support Vector Machines (SVMs) with Deep Features. The segmentation model Fully Convolutional Networks (FCNs) is used for pixel-wise segmentation of dental images to identify dental caries and abnormal cancer cells. The performance of the proposed approach shows a massive detection and classification rate compared with existing models.
Authors and Affiliations
Sunkara. Naga Sindhu and Dr Raavi. Satya Prasad
Keywords
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- EP ID EP752209
- DOI https://doi.org/10.46501/IJMTST1011002
- Views 35
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