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A bi-level analysis of computed tomography (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 …