Liver Tumor Grade Detection using CNN Based LSTM Model with Corelated Feature Set from CT Images

Gavini, Venkateswarlu; Lakshmi, G. Vijaya

doi:10.1109/icacrs55517.2022.10029307

Cited by 4 publications

(1 citation statement)

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“…To better plan liver treatments, classify therapeutic responses, classify hepatic tumours, and estimate patient survival, it can be helpful to accurately segment tumours so volumebased quantitative information, including textural qualities, can be measured. Manual delineation is still used for liver tumor segmentation a lot of the time, although it's time-consuming, hard-working, and might vary from operator to operator [3][4][5][6]. For liver and lesion segmentation, many computer-aided approaches have been suggested, all based on conventional image processing algorithms [7][8][9].…”

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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Section: Introductionmentioning

confidence: 99%