Miri Weiss Cohen, Anna Ghidotti, Daniele Regazzoni

Bi-Level Analysis of CT Images of Malignant Pleural Mesothelioma: Deep Learning-Based Classification and Subsequent 3D Reconstruction

  • Industrial and Manufacturing Engineering
  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Software
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Abstract A Bi-level analysis of CT images of Malignant Pleural Mesothelioma (MPM) is presented in this paper, starting with a deep learning based system for classification, followed by a three-dimensional (3D) reconstruction method. MPM is a highly aggressive cancer caused by asbestos exposure, and accurate diagnosis and determination of the tumor's volume are crucial for effective treatment. The proposed system employs a bi-level approach, utilizing machine learning and deep learning techniques to classify CT lung images and subsequently calculate the tumor's volume. The study addresses challenges related to deep neural networks, such as the requirement for large and diverse datasets, hyperparameter optimization, and potential data bias. To evaluate performance, two convolutional neural network (CNN) architectures, Inception-v3 and ResNet-50, were compared in terms of their features and performance. Additionally, three hyperparameters were optimized for each model to explore a broad range of training scenarios. The results demonstrate the efficacy of the developed system, showcasing the benefits of CNN optimizations and 3D image reconstruction from CT images for the diagnosis and treatment of MPM. This system is capable of improving the accuracy of MPM diagnosis and assisting in the determination of chemotherapy doses, both of which can result in improved patient outcomes.

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