A Novel BioInspired Deep Learning Approach for Liver Cancer Diagnosis Current research on computeraided diagnosis (CAD) of liver cancer is based on traditional feature engineering methods, which have several drawbacks including redundant features and high computational cost. Recent deep learning models overcome these problems by implicitly capturing intricate structures from largescale medical image data. However, they are still affected by network hyperparameters and topology. Hence, the state of the art in this area can be further optimized by integrating bioinspired concepts into deep learning models. This work proposes a novel bioinspired deep learning approach for optimizing predictive results of liver cancer. This approach contributes to the literature in two ways. Firstly, a novel hybrid segmentation algorithm is proposed to extract liver lesions from computed tomography (CT) images using SegNet network, UNet network, and artificial bee colony optimization (ABC), namely, SegNetUNetABC. This algorithm uses the SegNet for separating liver from the abdominal CT scan, then the UNet is used to extract lesions from the liver. In parallel, the ABC algorithm is hybridized with each network to tune its hyperparameters, as they highly affect the segmentation performance. Secondly, a hybrid algorithm of the LeNet5 model and ABC algorithm, namely, LeNet5ABC, is proposed as feature extractor and classifier of liver lesions. The LeNet5ABC algorithm uses the ABC to select the optimal topology for constructing the LeNet5 network, as network structure affects learning time and classification accuracy. For assessing performance of the two proposed algorithms, comparisons have been made to the stateoftheart algorithms on liver lesion segmentation and classification. The results reveal that the SegNetUNetABC is superior to other compared algorithms regarding Jaccard index, Dice index, correlation coefficient, and convergence time. Moreover, the LeNet5ABC algorithm outperforms other algorithms regarding specificity, F1score, accuracy, and computational time.

CUNet: Cascaded UNet Model for Automated Liver and Lesion Segmentation and Summarization One of the leading causes of cancer death is liver cancer. The most common type of primary liver cancer in adults is Hepatocellular carcinoma (HCC), and it is also the most common cause of death among people suffering from chronic liver diseases. In clinical practices, accurate and automated liver and tumor segmentation mechanisms are highly desired to help oncologists with early diagnosis and treatment of HCC. Manual segmentation of tumor and liaisons, from abdominal computerized tomography (CT) scans, is timeconsuming, requires domain expertise, and is prone to inaccuracies in many cases. Automated segmentation, on the other hand, is a challenging task due to the nature of medical images such as heterogeneous modalities and formats, the minimal labeled training data, and the highclass imbalance in the labeled data. To solve these problems, we propose a 2dimensional network consists of two CNN models, called CUNet. To perform double segmentation, the first model segments the liver from the CT scan of the abdominal cavity and second detects the tumor on the liver. Our model also goes one step further and provides useful information for interpretation purposes and reports on the number of tumors, size of each tumor, size of the liver, and percentage of the liver that is tumorous, which is not provided by any other liverlesion segmentation model. We have used the Liver Tumor Segmentation (LiTS) challenge data for training and validation purposes, and with this data, we have achieved a Dice score of 0.894 for liver segmentation and 0.595 for tumor detection.