2024
DOI: 10.1088/2057-1976/ad5207
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Prediction of cervix cancer stage and grade from diffusion weighted imaging using EfficientNet

Souha Aouadi,
Tarraf Torfeh,
Othmane Bouhali
et al.

Abstract: Purpose: This study aims to introduce an innovative noninvasive method that leverages a single image for both grading and staging prediction. The grade and the stage of cervix cancer (CC) are determined from diffusion-weighted imaging (DWI) in particular apparent diffusion coefficient (ADC) mapping using deep convolutional neural networks (DCNN).
Methods: datasets composed of 85 patients having annotated tumor stage (I, II, III, and IV), out of this, 66 were with grade (II and III) and the remaining pa… Show more

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