Classification Of Liver Tumors Research Articles

Feature extraction-based liver tumor classification using Machine Learning and Deep Learning methods of computed tomography images

Feature extraction-based liver tumor classification using Machine Learning and Deep Learning methods of computed tomography images

Advanced Machine Learning Techniques for Liver Tumor Classification in MRI Imaging

In this research into liver tumor categorization within MRI images, diverse machine learning methodologies were scrutinized for their efficacy. The study delved into the integration of shape and texture features, aiming to bolster classification accuracy. Among the algorithms explored, the Extra Trees model emerged as the most promising contender, exhibiting superior performance compared to its counterparts. Leveraging the distinctive capabilities of the Extra Trees model, the study underscored its effectiveness in accurately categorizing liver tumors. This highlights its potential to enhance diagnostic precision in clinical contexts. Through rigorous experimentation and analysis, the research elucidated the significance of incorporating shape and texture features into machine learning frameworks for improved tumor classification. The findings not only contribute to advancing the field of medical imaging but also underscore the importance of leveraging innovative methodologies to address healthcare challenges. Overall, the study sheds light on the promising prospects of employing advanced machine learning techniques in medical imaging for more accurate and efficient diagnosis of liver tumors.

A novel liver tumor classification using improved probabilistic neural networks with Bayesian optimization

One of the most common forms of cancer is liver cancer, and successful treatment depends greatly on an early diagnosis. The two primary steps in liver tumor segmentation are liver identification and then tumor segmentation. A sophisticated meta-heuristic technique called Improved Probabilistic Neural Networks and Bayesian Optimization (IPNN-BO) is also introduced to carry out the best classification. In the study, liver computed tomography (CT) scan pictures were gathered for training purposes and added to the LiTs17 dataset. Utilizing performance matrices like accuracy, precision, recall, specificity, sensitivity, and F Measure, the suggested technique is implemented in MATLAB 2021(a). Modern technologies such as KNN, CNN, and DCNN, among others, are used to organize and validate possible policies. The comparison results reveal that the proposed technique outperforms and outperforms the most recent methodologies. IPNN-BO may obtain validation accuracy of up to 99.25 % after applying Bayesian optimization, while KNN, CNN, and DCNN only manage validation accuracy of 88 %, 92.85 %, and 93.75 %, respectively.

Epstein-Barr virus-associated lymphoepithelioma-like intrahepatic cholangiocarcinoma: A report of 3 cases and literature review.

Hepatic lymphoepithelioma-like carcinoma (LELC) is an extremely rare malignant tumor characterized by undifferentiated malignant epithelial cells and significant lymphatic infiltration. Hepatic LELC mainly includes lymphoepithelioma-like hepatocellular carcinoma (LEL-HCC) and lymphoepithelioma-like intrahepatic cholangiocarcinoma (LEL-CC). Epstein-Barr virus (EBV) infection is considered as an important factor in LELC carcinogenesis. Since 2005, Xiangya Hospital of Central South University has treated a total of 3 patients with EBV-associated LEL-CC, which all showed liver masses by CT scans. After surgical resection, the EBV encoded RNA (EBER) and CK19 expression in all 3 patients were positive, and pathological examination confirmed EBV-associated LEL-CC. Two patients had a good postoperative prognosis, while 1 patient received relevant immunotherapy and chemotherapy after surgery. Based on the analysis of existing literature, the author believes that hepatic LELC can be included in the classification of liver tumors, which will provide new ideas for the accurate diagnosis and treatment of hepatic LELC.

Accurate artificial intelligence method for abnormality detection of CT liver images

Liver cancer is a leading cause of death worldwide and poses a significant challenge to physicians in terms of accurate diagnosis and treatment. AI-powered segmentation and classification algorithms can play a vital role in assisting physicians in detecting and diagnosing liver tumors. However, liver tumor classification is a difficult task due to factors such as noise, non-homogeneity, and significant appearance variations in cancerous tissue. In this study, we propose a novel approach to automatically segmenting and classifying liver tumors. Our proposed framework comprises three main components: a preprocessing unit to enhance picture contrast, a Masked Recurrent Convolutional Neural Network (RCNN) for liver segmentation, and a pixel-wise classification unit for identifying abnormalities in the liver. When our models are applied to the challenging MICCAI’2027 liver tumor segmentation (LITS) database, we achieve Dice similarity coefficients of 96% and 98% for liver segmentation and lesion identification, respectively. We also demonstrate the efficiency of our proposed framework by comparing it with similar strategies for tumor segmentations. The proposed approach achieved high accuracy, sensitivity, specificity, and F1 score parameters for liver segmentation and lesion identification. These results were evaluated using the Dice similarity coefficient and compared with similar strategies for tumor segmentation. Our approach holds promise for improving the accuracy and speed of liver tumor detection and diagnosis, which could have significant implications for patient outcomes.

A Comprehensive Review on Machine Learning Methods for Categorizing Liver Tumors

This comprehensive review delves into the application of machine learning methods for the categorization of liver tumors, offering a thorough examination of the current landscape in medical imaging and diagnostics. The escalating prevalence of liver tumors necessitates precise and efficient classification methods, and this paper systematically explores the diverse array of machine learning techniques employed in this context. From traditional approaches such as support vector machines and decision trees to more advanced deep learning algorithms, the review synthesizes existing literature to provide a holistic understanding of their strengths, limitations, and comparative performances. Furthermore, the article discusses key challenges in the domain, such as data scarcity and interpretability, proposing potential avenues for future research and innovation. With a focus on bridging the gap between clinical needs and technological advancements, this review contributes valuable insights to the evolving field of medical imaging, offering a roadmap for the development of robust and clinically relevant liver tumor classification systems.

Unified automated deep learning framework for segmentation and classification of liver tumors

Unified automated deep learning framework for segmentation and classification of liver tumors

Improving the sensitivity of liver tumor classification in ultrasound images via a power-law shot noise model.

Power laws have been observed in various fields and help us understand natural phenomena. Power laws have also been observed in ultrasound images. This study used the power spectrum of the signal identified from the reflected ultrasound signal observed in ultrasonography based on the power-law shot noise (PLSN) model. The power spectrum follows a power law, which has a scaling factor that depends on the characteristics of the tissue in the region where the ultrasound wave propagates. To distinguish between a tumor and blood vessels in the liver, we propose a classification model that includes a scaling factor based on ResNet, a deep learning model for image classification. In a task to classify 6 types of tissue - a tumor, the inferior vena cava, the descending aorta, the Gleason sheath, the hepatic vein, and small blood vessels - tumor sensitivity increased 3.8% and the F-score for a tumor improved 2% while precision was maintained. The scaling factor obtained using the PLSN model was validated for classification of liver tumors.

Evaluation of the safety and efficacy of ultrasound-guided percutaneous radiofrequency ablation for hepatocellular carcinoma and liver metastases adjacent to the gallbladder

Objective To evaluate the safety and efficacy of ultrasound-guided percutaneous radiofrequency ablation (RFA) in patients with hepatocellular carcinoma (HCC) and liver metastases adjacent to the gallbladder (GB). Materials and methods A total of 113 patients with 118 liver lesions (63 HCC lesions and 55 liver metastases) adjacent to the gallbladder underwent RFA between March 2011 and June 2019. Gallbladder-related complications and technique effectiveness rates were evaluated based on the classification of liver tumors and the distance between the lesion and the gallbladder. Results Gallbladder-related complications were observed in 13 patients. Among the patients with HCC, there was no significant difference between the ≤0.5 cm and >0.5 cm groups (p = .282). However, among the patients with liver metastases, the incidence of gallbladder-related complications in the ≤0.5 cm group was significantly higher than that in the >0.5 cm group (p = .025). The overall incidence of complications was significantly higher in the ≤0.5 cm group than in the >0.5 cm group (p = .020). Among the patients with lesions ≤3 cm, the technical effectiveness rate in the HCC group was significantly higher than in the liver metastasis group (p = .036). Conclusion RFA is a safe and effective treatment option for liver tumors adjacent to the gallbladder. Patients with lesions ≤0.5 cm from the gallbladder had higher gallbladder-related complications, especially patients with liver metastases. Among patients with lesions ≤3 cm, RFA showed greater technical effectiveness for treating HCC than for treating liver metastases.

Sonographische Differenzialdiagnostik fokaler Leberläsionen

Rapid diagnostic assessment is crucial for the clinical management of focal liver lesions. In addition to standard B‑mode sonography, color-coded Doppler sonography and contrast-enhanced ultrasound are widely used for the classification of liver tumors. In gastroenterology, clinical sonography plays an important role in the detection of tumors, in the follow-up of oncological patients and for the screening for hepatocellular carcinoma. Thereby, correctly classifying focal liver lesions essentially requires the interpretation of ultrasound findings in the clinical context (e.g., incidental finding, known risk factors, history of liver cirrhosis or malignant disease). In this review, we summarize the available ultrasound techniques for the characterization of focal liver lesions and discuss their importance and relevance for the differential diagnosis and further diagnostic and therapeutic management.

Liver tumor segmentation and classification using FLAS-UNet++ and an improved DenseNet.

The incidence of liver tumors is among the top three in China. The treatments of benign and malignant tumors are different. Accurate diagnosis plays an important role in guiding the treatment of tumors. The aim of this study is to solve the following: (1) blurred boundary between the liver tumor and other organs causes incorrect segmentation of liver tumor boundaries; (2) large difference in tumor size and the diversity in texture and grayscale are major challenges in liver tumor classification tasks. Firstly, the liver tumor is segmented from the original CT images by a tumor segmentation network, UNet++ with fusion loss and atrous spatial pyramid pooling (FLAS-UNet++). The proposed segmentation method can solve the problem of tumor edge segmentation error by learning the tumor edge information. Secondly they are adaptively cropped according to the tumor volume to reduce the over-fitting and over-sensitivity of the deep network. Thirdly an improved Dense Block is designed to pay more attention to the changes in grayscale and texture between benign and malignant tumors. Finally, the features extracted from the network combined with tumor volume, patient's sex and age, are sent to a classifier for diagnosis. Liver tumor segmentation results show that the dice, HD95 reached 71.9%, 12.1 mm, respectively. The classification results show that the accuracy, specificity, sensitivity and area under curve reached 82.4%, 79.8%, 84.4%, 87.5%, respectively. The segmentation and classification results are both better than other's methods and mainstream networks. In order to solve existing problems of liver tumor CT image classification methods, our method realizes the accurate segmentation and classification of liver tumors in CT images and has important clinical application value.

Liver Tumor Classification Using Optimal Opposition-Based Grey Wolf Optimization

Image processing plays a significant role in various fields like military, business, healthcare and science. Ultrasound (US), Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the various image tests used in the treatment of the cancer. Detecting the liver tumor by these tests is a complex process. Hence, in this research work, a novel approach utilizing a deep learning model is used. That is Deep Belief Network (DBN) with Opposition-Based Learning (OBL)-Grey Wolf Optimization (GWO) is used for the classification of liver cancer. This process undergoes five major processes. Initially, in pre-processing the color contrast is improved by Contrast Limited Adaptive Histogram Equalization (CLAHE) and the noise is removed by Wiener Filtering (WF). The liver is segmented by adaptive thresholding following pre-processing. Following that, the kernelizedFuzzy C Means (FCM) method is used to segment the tumor area. The form, color, and texture features are then extracted during the feature extraction process. Finally, these traits are categorized using DBN, and OBL-GWO is employed to enhance system performance. The entire evaluation is done on Liver Tumor Segmentation (LiTS) benchmark dataset. Finally, the performance of the proposed DBN-OBL-GWO is compared to other models and their achievements are proved. The proposed DBN-OBL-GWO achieves a better accuracy of 0.995, precision of 0.948 and false positive rate (FPR) of 0.116, respectively.

Morphomolecular Classification Update on Hepatocellular Adenoma, Hepatocellular Carcinoma, and Intrahepatic Cholangiocarcinoma

Hepatocellular adenomas (HCAs), hepatocellular carcinomas (HCCs), and intrahepatic cholangiocarcinomas (iCCAs) are a highly heterogeneous group of liver tumors with diverse pathomolecular features and prognoses. High-throughput gene sequencing techniques have allowed discovery of distinct genetic and molecular underpinnings of these tumors and identified distinct subtypes that demonstrate varied clinicobiologic behaviors, imaging findings, and complications. The combination of histopathologic findings and molecular profiling form the basis for the morphomolecular classification of liver tumors. Distinct HCA subtypes with characteristic imaging findings and complications include HNF1A-inactivated, inflammatory, β-catenin-activated, β-catenin-activated inflammatory, and sonic hedgehog HCAs. HCCs can be grouped into proliferative and nonproliferative subtypes. Proliferative HCCs include macrotrabecular-massive, TP53-mutated, scirrhous, clear cell, fibrolamellar, and sarcomatoid HCCs and combined HCC-cholangiocarcinoma. Steatohepatitic and β-catenin-mutated HCCs constitute the nonproliferative subtypes. iCCAs are classified as small-duct and large-duct types on the basis of the level of bile duct involvement, with significant differences in pathogenesis, molecular signatures, imaging findings, and biologic behaviors. Cross-sectional imaging modalities, including multiphase CT and multiparametric MRI, play an essential role in diagnosis, staging, treatment response assessment, and surveillance. Select imaging phenotypes can be correlated with genetic abnormalities, and identification of surrogate imaging markers may help avoid genetic testing. Improved understanding of morphomolecular features of liver tumors has opened new areas of research in the targeted therapeutics and management guidelines. The purpose of this article is to review imaging findings of select morphomolecular subtypes of HCAs, HCCs, and iCCAs and discuss therapeutic and prognostic implications. Online supplemental material is available for this article. ©RSNA, 2022.

Computer Vision Approach for Liver Tumor Classification Using CT Dataset

ABSTRACT The liver tumor is one of the most foremost critical causes of death in the world. Nowadays, Medical Imaging (MI) is one of the prominent Computer Vision fields (CV), which helps physicians and radiologists to detect and diagnose liver tumors at an early stage. Radiologists and physicians use manual or semi-automated systems to read hundreds of images, such as Computed Tomography (CT) for the diagnosis. Therefore, there is a need for a fully-automated method to diagnose and detect the tumor early using the most popular and widely used imaging modality, CT images. The proposed work focuses on the Machine Learning (ML) methods: Random Forest (RF), J48, Logistic Model Tree (LMT), and Random Tree (RT) with multiple automated Region of Interest (ROI) for multiclass liver tumor classification. The dataset comprises four tumor classes: hemangioma, cyst, hepatocellular carcinoma, and metastasis. Converted the images into gray-scale, and the contrast of images was improved by applying histogram equalization. The noise was reduced using the Gabor filter, and image quality was improved by applying an image sharpening algorithm. Furthermore, 55 features were acquired for each ROI of different pixel dimensions using texture, binary, histogram and rotational, scalability, and translational (RST) techniques. The correlation-based feature selection (CFS) technique was deployed to obtain 20 optimized features from these 55 features for classification. The results showed that RF and RT performed better than J48 and LMT, with an accuracy of 97.48% and 97.08%, respectively. The proposed novel framework will help radiologists and physicians better diagnose liver tumors.

Performance analysis of liver tumor classification using machine learning algorithms

Performance analysis of liver tumor classification using machine learning algorithms

A Critical Review on Machine Learning based Liver Tumor Classification

A Critical Review on Machine Learning based Liver Tumor Classification

Classification of Liver Tumors from Computed Tomography Using NRSVM

A classification system is used for Benign Tumors (BT) and Malignant Tumors (MT) in the abdominal liver. Computed Tomography (CT) images based on enhanced RGS is proposed. Diagnosis of liver diseases based on observation using liver CT images is essential for surgery and treatment planning. Identifying the progression of cancerous regions and Classification into Benign Tumors and Malignant Tumors are essential for treating liver diseases. The manual process is time-consuming and leads to intra and inter-observer variability. Hence, an automatic method based on enhanced region growing is proposed for the Classification of Liver Tumors (LT). To enhance the Liver Region (LR) from the surrounding tissues, Non-Linear Mapping (NLP) is used. Region Growing Segmentation (RGS) is employed to segment the LR, and Expectation-Maximization (EM) algorithm is used to segment the region of interest. Grey Level Co-occurrence Matrix (GLCM) features are extracted from the tumor region, and Nonlinear Random Support Vector Machine (NRSVM) classification is performed to classify the Benign Tumors and Malignant Tumors. The proposed method is tested on a database of medical images collected from Med all Diagnostic Research Centre and attained an accuracy of 96%. The proposed method is beneficial for better liver tumor diagnosis in an optimized method by the medical expert.

Neutrosophic set with Adaptive Neuro-Fuzzy Inference System for Liver Tumor Segmentation and Classification Model

Lung cancer is the abnormal development of cells in the lung causes serious risk to the health since lung has an interconnected system of blood vessel and lymphatic channel exposed to metastasis. The survival rate of lung cancer depends greatly on the earlier diagnosis and staging of the lung cancer. Computed Tomography (CT) image is commonly employed for lung cancer diagnosis since they offer data regarding distinct portions of the lung. The exactness of finding tumor location, volume and shape acting a major role in positive treatment and diagnosis of tumor. This article designs a novel neutrosophic set with adaptive neuro-fuzzy inference system for liver tumor segmentation and classification (NSANFIS-LTSC) model. The presented NSANFIS-LTSC model aims to identify and classify the presence of liver tumor from medical images. The presented NSANFIS-LTSC model primarily undergoes pre-processing to eradicate the noise. Followed by, the neutrosophic set (NS) based segmentation is applied to identify the affected tumor regions in the CT images. Besides, DenseNet-169 model is utilized to create feature vectors and dragonfly algorithm (DFA) is applied to tune the hyper parameters of the DenseNet-169 model. Finally, ANFIS classifier is exploited for the occurrence and classification of liver tumor. The simulation analysis of the NSANFIS-LTSC model is experimented using benchmark dataset and the results are investigated under several aspects. The simulation outcome reported the betterment of the NSANFIS-LTSC model over the recent methodologies.

A novel ResNet101 model based on dense dilated convolution for image classification

Image classification plays an important role in computer vision. The existing convolutional neural network methods have some problems during image classification process, such as low accuracy of tumor classification and poor ability of feature expression and feature extraction. Therefore, we propose a novel ResNet101 model based on dense dilated convolution for medical liver tumors classification. The multi-scale feature extraction module is used to extract multi-scale features of images, and the receptive field of the network is increased. The depth feature extraction module is used to reduce background noise information and focus on effective features of the focal region. To obtain broader and deeper semantic information, a dense dilated convolution module is deployed in the network. This module combines the advantages of Inception, residual structure, and multi-scale dilated convolution to obtain a deeper level of feature information without causing gradient explosion and gradient disappearance. To solve the common feature loss problems in the classification network, the up- down-sampling module in the network is improved, and multiple convolution kernels with different scales are cascaded to widen the network, which can effectively avoid feature loss. Finally, experiments are carried out on the proposed method. Compared with the existing mainstream classification networks, the proposed method can improve the classification performance, and finally achieve accurate classification of liver tumors. The effectiveness of the proposed method is further verified by ablation experiments.HighlightsThe multi-scale feature extraction module is introduced to extract multi-scale features of images, it can extract deep context information of the lesion region and surrounding tissues to enhance the feature extraction ability of the network.The depth feature extraction module is used to focus on the local features of the lesion region from both channel and space, weaken the influence of irrelevant information, and strengthen the recognition ability of the lesion region.The feature extraction module is enhanced by the parallel structure of dense dilated convolution, and the deeper feature information is obtained without losing the image feature information to improve the classification accuracy.

An Efficient Discrete Wavelet Transform Based Partial Hadamard Feature Extraction and Hybrid Neural Network Based Monarch Butterfly Optimization for Liver Tumor Classification

An Efficient Discrete Wavelet Transform Based Partial Hadamard Feature Extraction and Hybrid Neural Network Based Monarch Butterfly Optimization for Liver Tumor Classification