Analysis Of Supervised Learning Approaches For Identification Of Oral Squamous Cell Carcinoma: A Multimodal Approach
DOI:
https://doi.org/10.48047/h1j9b871Keywords:
Multimodal, Supervised methods, Oral Cancer, Histopathological imagesAbstract
This research focuses on the early detection of Oral Squamous Cell Carcinoma by using a multimodal approach: putting together features from histopathological images and clinical data. The proposed study
has used 237 histopathological images classified as OSCC, leukoplakia with dysplasia, and leukoplakia
without dysplasia, along with their respective clinical-demographic in- formation. It proposed several image
classification supervised machine learning methods: the support vector machine, KNN, Decision Trees,
Random Forest, Logistic Regression, Naive Bayes, taking ResNet50 features extracted from images. For KNN,
the highest observed accuracy was 89%, while for linear SVM, the accuracy was 81%. Decision Trees and
Random Forests overfitted in this case, whereas the Logistic Regression model presented rather balanced
results. It thus evidences the potentiality of machine learning and multimodal data for the development of
precise and cost-effective diagnostic tools in oral pathology, while among the optimal algorithms, KNN and
linear SVM will be identified.
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