Computer-aided diagnosis of liver cancer with improved SegNet and deep stacking ensemble model

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry Pub Date : 2024-10-19 DOI:10.1016/j.compbiolchem.2024.108243

Vinnakota Sai Durga Tejaswi, Venubabu Rachapudi

{"title":"Computer-aided diagnosis of liver cancer with improved SegNet and deep stacking ensemble model","authors":"Vinnakota Sai Durga Tejaswi, Venubabu Rachapudi","doi":"10.1016/j.compbiolchem.2024.108243","DOIUrl":null,"url":null,"abstract":"<div><div>Liver cancer is a leading cause of cancer-related deaths, often diagnosed at advanced stages due to reliance on traditional imaging methods. Existing computer-aided diagnosis systems struggle with noise, anatomical complexity, and ineffective feature integration, leading to inaccuracies in lesion segmentation and classification. By effectively addressing these challenges, the model aims to enhance early detection and assist clinicians in making informed decisions. Ultimately, this research seeks to contribute to more efficient and accurate liver cancer diagnosis. This paper presents a novel model for liver cancer classification, called SegNet-based Liver Cancer Classification via SqueezeNet (SgN-LCC-SqN). The model effectively executes liver cancer segmentation and classification through four key steps: preprocessing, segmentation, feature extraction, and classification. During preprocessing, Quadratic Mean Estimated Wiener Filtering (QMEWF) is utilized to minimize image noise. Segmentation divides the image into segments using Enhanced Feature Pyramid SegNet (EFP-SgN), which is essential for precise diagnosis. Feature extraction encompasses color features, Local Directional Pattern Variance, and Correlation Filtering-Local Gradient Increasing Pattern (CF-LGIP) features. The extracted features are then processed through an ensemble model, Deep Convolutional, Recurrent, Long Short Term Memory with SqueezeNet (DCR-LSTM-SqN), which includes Deep Convolutional Neural Network (DCNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Modified Loss Function in SqueezeNet (MLF-SqN) classifiers, sequentially analyzing the feature sets through DCNN, RNN, and LSTM before classification by MLF-SqN. The performance of the suggested DCR-LSTM-SqN model is evaluated over conventional methods for positive, negative and other metrics. The DCR-LSTM-SqN model consistently demonstrates superior accuracy, ranging from 0.947 to 0.984, across all training data percentages. Thus, the proposed model effectively segments liver lesions and classifies cancerous areas, demonstrating its potential as a valuable resource for clinicians to enhance the efficiency and accuracy of liver cancer diagnosis.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108243"},"PeriodicalIF":2.6000,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Biology and Chemistry","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1476927124002317","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

Liver cancer is a leading cause of cancer-related deaths, often diagnosed at advanced stages due to reliance on traditional imaging methods. Existing computer-aided diagnosis systems struggle with noise, anatomical complexity, and ineffective feature integration, leading to inaccuracies in lesion segmentation and classification. By effectively addressing these challenges, the model aims to enhance early detection and assist clinicians in making informed decisions. Ultimately, this research seeks to contribute to more efficient and accurate liver cancer diagnosis. This paper presents a novel model for liver cancer classification, called SegNet-based Liver Cancer Classification via SqueezeNet (SgN-LCC-SqN). The model effectively executes liver cancer segmentation and classification through four key steps: preprocessing, segmentation, feature extraction, and classification. During preprocessing, Quadratic Mean Estimated Wiener Filtering (QMEWF) is utilized to minimize image noise. Segmentation divides the image into segments using Enhanced Feature Pyramid SegNet (EFP-SgN), which is essential for precise diagnosis. Feature extraction encompasses color features, Local Directional Pattern Variance, and Correlation Filtering-Local Gradient Increasing Pattern (CF-LGIP) features. The extracted features are then processed through an ensemble model, Deep Convolutional, Recurrent, Long Short Term Memory with SqueezeNet (DCR-LSTM-SqN), which includes Deep Convolutional Neural Network (DCNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Modified Loss Function in SqueezeNet (MLF-SqN) classifiers, sequentially analyzing the feature sets through DCNN, RNN, and LSTM before classification by MLF-SqN. The performance of the suggested DCR-LSTM-SqN model is evaluated over conventional methods for positive, negative and other metrics. The DCR-LSTM-SqN model consistently demonstrates superior accuracy, ranging from 0.947 to 0.984, across all training data percentages. Thus, the proposed model effectively segments liver lesions and classifies cancerous areas, demonstrating its potential as a valuable resource for clinicians to enhance the efficiency and accuracy of liver cancer diagnosis.

查看原文本刊更多论文

利用改进的 SegNet 和深度堆叠集合模型进行肝癌计算机辅助诊断。

肝癌是导致癌症相关死亡的主要原因之一，由于依赖传统的成像方法，肝癌往往在晚期才被诊断出来。现有的计算机辅助诊断系统难以应对噪声、解剖复杂性和无效的特征整合等问题，导致病变分割和分类不准确。通过有效应对这些挑战，该模型旨在加强早期检测，协助临床医生做出明智的决定。最终，这项研究旨在为更高效、更准确的肝癌诊断做出贡献。本文提出了一种新颖的肝癌分类模型，称为基于 SegNet 的挤压网肝癌分类（SgN-LCC-SqN）。该模型通过预处理、分割、特征提取和分类四个关键步骤有效地执行肝癌分割和分类。在预处理过程中，利用二次均值估计维纳滤波法（QMEWF）将图像噪声降至最低。分割利用增强型特征金字塔分割网（EFP-SgN）将图像分割成不同的部分，这对精确诊断至关重要。特征提取包括颜色特征、局部方向模式方差和相关过滤-局部梯度增加模式（CF-LGIP）特征。然后，提取的特征通过一个集合模型--SqueezeNet 深度卷积、递归、长短期记忆（DCR-LSTM-SqN）进行处理，该模型包括深度卷积神经网络（DCNN）、递归神经网络（RNN）、长短期记忆（LSTM）和 SqueezeNet 中的修正损失函数（MLF-SqN）分类器，在 MLF-SqN 分类之前，依次通过 DCNN、RNN 和 LSTM 对特征集进行分析。在正向、负向和其他指标方面，对建议的 DCR-LSTM-SqN 模型的性能进行了评估，结果优于传统方法。在所有训练数据百分比中，DCR-LSTM-SqN 模型的准确率始终保持在 0.947 到 0.984 之间。因此，所提出的模型能有效地分割肝脏病变并对癌变区域进行分类，为临床医生提高肝癌诊断的效率和准确性提供了宝贵的资源。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Computational Biology and Chemistry 生物-计算机：跨学科应用

CiteScore

6.10

自引率

3.20%

发文量

142

审稿时长

24 days

期刊介绍： Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.