A machine learning method for improving liver cancer staging

Zhao, Zhengyun; Tian, Yongchao; Yuan, Zheng; Zhao, Peng; Xia, Feng; Yu, Sheng

doi:10.1016/j.jbi.2022.104266

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…Zhao et al [16] proposed a pioneering machine learning methodology aimed at constructing an automated Hepatocellular Carcinoma staging system that incorporates a significantly larger number of clinical features than existing systems. Their approach, based on random survival trees, utilised B-splines to transform functions into vectors in a low-dimensional space, allowing for the grouping of similar patients into staging cohorts.…”

Section: Related Workmentioning

confidence: 99%

Advanced Hybrid Segmentation Model Leveraging AlexNet Architecture for Enhanced Liver Cancer Detection

Nagireddy,

Shaik

2023

ATAIML

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Liver cancer, one of the rapidly escalating forms of cancer, remains a principal cause of mortality globally. Its death rates can be attenuated through vigilant monitoring and early detection. This study aims to develop a sophisticated model to assist medical professionals in the classification of liver tumours using biopsy tissue images, thereby facilitating preliminary diagnosis.The study presents a novel, bio-inspired deep learning strategy purposed for augmenting liver cancer detection. The uniqueness of this approach rests in its two-fold contribution: Firstly, an innovative hybrid segmentation technique, integrating the SegNet network, UNet network, and Al-Biruni Earth Radius (BER) procedure, is introduced to extract liver lesions from Computed Tomography (CT) images. The algorithm initially applies the SegNet to isolate the liver from the abdominal image in a CT scan. Since hyperparameters significantly influence segmentation performance, the BER algorithm is hybridized with each network for optimal tuning. The method proposed herein is inspired by the pursuit of a common objective by swarm members. Al-Biruni's methodology for calculating Earth's radius sets the search space, extending beyond local solutions that require exploration. Secondly, a pre-trained AlexNet model is utilized for diagnosis, further enhancing the method's effectiveness. The proposed segmentation and classification algorithms have been compared with contemporary state-of-the-art techniques. The results demonstrated that in terms of specificity, F1-score, accuracy, and computational time, the proposed method outperforms its competitors, indicating its potential in advancing liver cancer detection.

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Section: Related Workmentioning

confidence: 99%

Advanced Hybrid Segmentation Model Leveraging AlexNet Architecture for Enhanced Liver Cancer Detection

Nagireddy,

Shaik

2023

ATAIML

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“…Deep learning has also found use in the imaging of healthy livers, the staging of liver fibrosis, the classification of liver statuses, the identification of hepatic tumours, and the differentiating of liver masses [18]. The data-driven algorithms that make up deep learning allow for the automatic capture of highlevel features from photographs and performance improvements in these areas [19]. Deep learning has also shown to be quite effective in liver tumour segmentation, as all of the top algorithms in the 2017 Liver Tumour Segmentation (LiTS) competition used it [20].…”

Section: Introductionmentioning

confidence: 99%

Automated Liver Tumor Segmentation in DCE-MRI with 4D Deep Learning Integrating 3D CNNS and ConvLSTM

Sriram,

Karthik

2024

Int Res J Adv Engg Hub

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A state-of-the-art method for automatically segmenting liver tumours using Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is shown in this study. This study is significant because it uses a 4D information deep learning model to tackle the hard problem of liver tumor segmentation. A combination of 3D CNNs and ConvLSTM networks, specifically built to capture spatial and temporal information inside the dynamic imaging sequence of DCE-MRI, is what the suggested model is all about. Utilizing diffusion-computed tomography (DCE-MRI) gives a lot of information vital for precise tumor segmentation by providing a complete picture of the vascular dynamics in the liver. The model makes use of spatial and temporal elements by combining 3D Convolutional Neural Networks (CNNs) with ConvLSTM networks; this allows for a more detailed comprehension of the changes that are happening over time. To overcome the difficulties caused by the constantly changing nature of DCE-MRI data, this integration of 4D information greatly improves the accuracy and consistency of liver tumor segmentation. Implementing and optimizing the suggested deep learning model are the main goals of this work. The training and calibration of the model to accurately capture liver tumor characteristics in dynamic imaging sequences is of utmost importance.

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Machine Learning Survival Models for Relapse Prediction in a Early Stage Lung Cancer Patient

Timilsina

Buosi

Janik

et al. 2023

2023 International Joint Conference on Neural Networks (IJCNN)

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A machine learning method for improving liver cancer staging

Cited by 4 publications

References 25 publications

Advanced Hybrid Segmentation Model Leveraging AlexNet Architecture for Enhanced Liver Cancer Detection

Advanced Hybrid Segmentation Model Leveraging AlexNet Architecture for Enhanced Liver Cancer Detection

Automated Liver Tumor Segmentation in DCE-MRI with 4D Deep Learning Integrating 3D CNNS and ConvLSTM

Machine Learning Survival Models for Relapse Prediction in a Early Stage Lung Cancer Patient

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