An automated liver tumour segmentation and classification model by deep learning based approaches

Roy, Sayan Saha; Roy, Shraban; Mukherjee, P. K.; Roy, Anirban

doi:10.1080/21681163.2022.2099300

Cited by 25 publications

(12 citation statements)

References 40 publications

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“…Anter et al [13] presented a detailed study on liver tumor diagnosis optimization using ML. Another study highlights the use of deep learning in segmenting and classifying liver tumors [14]. Traditional image processing, computer vision, and machine learning approaches classify the features extracted from CT images [15].…”

Section: Literature Surveymentioning

confidence: 99%

Deep Learning Framework for Liver Tumor Segmentation

Gupta,

Aggarwal,

Jha

et al. 2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Segregating hepatic tumors from the liver in computed tomography (CT) scans is vital in hepatic surgery planning. Extracting liver tumors in CT images is complex due to the low contrast between the malignant and healthy tissues and the hazy boundaries in CT images. Moreover, manually detecting hepatic tumors from CT images is complicated, time-consuming, and needs clinical expertise. OBJECTIVES: An automated liver and hepatic malignancies segmentation is essential to improve surgery planning, therapy, and follow-up evaluation. Therefore, this study demonstrates the creation of an intuitive approach for segmenting tumors from the liver in CT scans. METHODS: The proposed framework uses residual UNet (ResUNet) architecture and local region-based segmentation. The algorithm begins by segmenting the liver, followed by malignancies within the liver envelope. First, ResUNet trained on labeled CT images predicts the coarse liver pixels. Further, the region-level segmentation helps determine the tumor and improves the overall segmentation map. The model is tested on a public 3D-IRCADb dataset. RESULTS: Two metrics, namely dice coefficient and volumetric overlap error (VOE), were used to evaluate the performance of the proposed method. ResUNet model achieved dice of 0.97 and 0.96 in segmenting liver and tumor, respectively. The value of VOE is also reduced to 1.90 and 0.615 for liver and tumor segmentation. CONCLUSION: The proposed ResUNet model performs better than existing methods in the literature. Since the proposed model is built using U-Net, the model ensures quality and precise dimensions of the output.

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Section: Literature Surveymentioning

confidence: 99%

Deep Learning Framework for Liver Tumor Segmentation

Gupta,

Aggarwal,

Jha

et al. 2024

EAI Endorsed Trans Perv Health Tech

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“…In study [35], the researchers used a two-step method to determine the presence of liver tumors. The liver region was first partitioned using mask-RCNN, followed by the identification of tumors using MSER (maximally stable external regions).…”

Section: Related Workmentioning

confidence: 99%

“…The identification accuracy proves that it can easily be used to find out about any type of unexpected thing in the liver. In the work [35] the scholars applied a cascaded fully convolutional neural network in the ACC, DC, and F1 scores. The accuracy of segmentations and dice coefficient values indicated the detection, and its accuracy was significantly higher compared to other researchers.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Method for Exploring Deep Learning Techniques for Subtyping and Prediction of Liver Disease

Hendi,

Hossain,

Majrashi

et al. 2024

Applied Sciences

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The term “Liver disease” refers to a broad category of disorders affecting the liver. There are a variety of common liver ailments, such as hepatitis, cirrhosis, and liver cancer. Accurate and early diagnosis is an emergent demand for the prediction and diagnosis of liver disease. Conventional diagnostic techniques, such as radiological, CT scan, and liver function tests, are often time-consuming and prone to inaccuracies in several cases. An application of machine learning (ML) and deep learning (DL) techniques is an efficient approach to diagnosing diseases in a wide range of medical fields. This type of machine-related learning can handle various tasks, such as image recognition, analysis, and classification, because it helps train large datasets and learns to identify patterns that might not be perceived by humans. This paper is presented here with an evaluation of the performance of various DL models on the estimation and subtyping of liver ailment and prognosis. In this manuscript, we propose a novel approach, termed CNN+LSTM, which is an integration of convolutional neural network (CNN) and long short-term memory (LSTM) networks. The results of the study prove that ML and DL can be used to improve the diagnosis and prognosis of liver disease. The CNN+LSTM model achieves a better accuracy of 98.73% compared to other models such as CNN, Recurrent Neural Network (RNN), and LSTM. The incorporation of the proposed CNN+LSTM model has better results in terms of accuracy (98.73%), precision (99%), recall (98%), F1 score (98%), and AUC (Area Under the Curve)-ROC (Receiver Operating Characteristic) (99%), respectively. The use of the CNN+LSTM model shows robustness in predicting the liver ailment with an accurate diagnosis and prognosis.

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“…For the former, Balasubramanian et al [4] proposed the APESTNet that segmented first and then classified, Swin Transformer block is used for classification. Similarly, Roy et al [8] first segmented the tumor by mask-CNN, and then classified the tumor by MSER. However, the above-mentioned methods first segment out the tumor, still cannot handle the lesions at the segmentation boundary well, which is likely to affect the classification.…”

Section: Liver Lesion Classificationmentioning

confidence: 99%

“…Recently, Convolutional Neural Networks (CNNs) have achieved wonderful results in various image classification tasks, attracting more and more attention from researchers, and they have thus been used to effectively process a wide range of visual tasks [4][5][6][7]. Deep learning methods have rapidly become a mainstream technique for medical image analysis [3,4,8]. CNNsbased Computer-Aided Diagnosis (CAD) can help doctors not only reduce erroneous judgments but also save on manual costs.…”

Section: Introductionmentioning

confidence: 99%

Imbalance multiclass Problem: A robust Feature Enhancement-based Framework for Liver Lesion Classification

Rui

Song

Liu

et al. 2023

Preprint

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The classification of liver lesions in CT images is essential for the diagnosis and treatment of liver diseases. Since the characteristics of different classes of lesions are similar and the degree of differentiation is not obvious, it is difficult to accurately classify different classes of liver lesions, especially imbalanced multiclass distribution. To this end, we propose a novel feature enhancement-based framework for imbalanced liver lesion classification. Specifically, a liver lesion processing method is introduced to expand data based on Augmentor. Besides, dual feature enhancement based on feature refinement extraction and feature global correlation is designed to enhance feature extraction ability. To further alleviate the problem of class imbalance, the improved loss function based on standard Cross-Entropy (CE) is also adopted to make the network pay more attention to the class with fewer samples. Experiments on 551 liver lesions in 120 patients showed that: 1) Our proposed framework improved the classification performance of liver tumors under imbalanced multiclass data distribution; 2) The dual feature enhancement was lightweight and efficient which enhanced the semantic expression of overall features without introducing additional parameters.

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An automated liver tumour segmentation and classification model by deep learning based approaches

Cited by 25 publications

References 40 publications

Deep Learning Framework for Liver Tumor Segmentation

Deep Learning Framework for Liver Tumor Segmentation

Adaptive Method for Exploring Deep Learning Techniques for Subtyping and Prediction of Liver Disease

Imbalance multiclass Problem: A robust Feature Enhancement-based Framework for Liver Lesion Classification

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