Abstract: Disclosed is a computer-implemented three-dimensional image classification system (CIS) for processing and/or analyzing non-contrast computed tomography (CT) medical imaging data. The CIS is a deep neural network containing multiple Convolutional Block Attention Module (CBAM) blocks, which contain convolutional layers for feature extraction followed by CBAMs. The CBAM applies channel attention to highlight more relevant features and spatial attention to focus on more important regions. Max pooling layers operably link adjacent pairs of CBAM blocks. The output of the final CBAM block is passed to two terminal fully connected layers to generate a diagnosis. This classification system can be used to perform efficient diagnosis of hepatocellular carcinoma using solely non-contrast CT images, with diagnostic performance comparable to that of a radiologist using the current LIRADS system.

Abstract: A computer-implemented system (CIS), based on the DenseNet model, for processing and/or analyzing computer tomography (CT) medical imaging input data is described. The CIS contains two or more dense blocks containing one or more modules. Within each dense block, output from preceding modules containing convolutional layers are transmitted to succeeding modules containing convolutional layers, via a gate that is controlled by a predefined or trainable threshold. The CIS also includes transition layers between the dense blocks, operably linked to pairs of consecutive dense blocks in the series configuration. The CIS can be used in a computer-implemented method for enhanced diagnoses of hepatocellular carcinoma, based analysis of one or more CT medical images.