This paper presents a complete pipeline for automatic diagnosis of various liver masses, consisting of components based on deep learning models for both the automatic segmentation of liver and its lesions, as well as the classification of masses into 5 classes, namely simple cysts, hemangiomas, hepatocellular carcinoma, calcifications and metastases.

It simultaneously receives 3 Computed Tomography (CT) scans as input for a single patient, representing different acquisition phases, all necessary for an accurate diagnostic.

All visible liver masses are automatically segmented into a single class, then extracted and preprocessed as 2D images in order to be sent as input to a classifier that determines the type of lesion. The study is performed on CT scans obtained in collaboration with a regional clinic from 184 symptomatic patients.

A thorough experimental analysis comparatively evaluates several deep learning models, leveraging the latest advances in the computer vision field for tackling segmentation and classification tasks.

The best models record a mean Dice score of 0.8653 for the segmentation component and an accuracy of 0.9272 for the classification component with an F1 score of 0.9135. The results demonstrate the feasibility of constructing fully automated systems to support the diagnosis of liver disease.

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