EARLY DIAGNOSIS AND PREDICTION OF HEAD AND NECK TUMORS USING ARTIFICIAL INTELLIGENCE (CNN)

Sumbul Rajapboyeva

Authors

Rajapboyeva Sumbul Baxrom qizi Muhammad al-Xorazmiy nomidagi TATU

Keywords:

Head and Neck Cancer, Artificial Intelligence (AI), Medical Imaging, Tumor Segmentation, Prognosis, Vision Transformer (ViT), Convolutional Neural Network (CNN), Computed Tomography (CT), Positron Emission Tomography (PET), Deep Learning

Abstract

This work investigates the application of artificial intelligence (AI) to improve the diagnosis and prognosis of head and neck (H&N) cancer. We explore two approaches for H&N tumor segmentation: (i) a comparison of Vision Transformer (ViT)-based and convolutional neural network (CNN)-based models, and (ii) a novel 2D perspective for processing 3D medical images. Furthermore, we propose AI models for patient prognosis, including an ensemble method and a ViT-based framework that integrates imaging and clinical data. Results demonstrate the potential of AI to enhance the accuracy and efficiency of H&N cancer management

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