EMI-LTI:利用 Gabor 滤波器和 ROI 识别肺部肿瘤的增强型综合模型

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES
MethodsX Pub Date : 2025-02-27 DOI:10.1016/j.mex.2025.103247
Jayapradha J , Su-Cheng Haw , Naveen Palanichamy , Kok-Why Ng , Muskan Aneja , Ammar Taiyab
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引用次数: 0

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本文章由计算机程序翻译,如有差异,请以英文原文为准。

EMI-LTI: An enhanced integrated model for lung tumor identification using Gabor filter and ROI

EMI-LTI: An enhanced integrated model for lung tumor identification using Gabor filter and ROI
In this work, the CT scans images of lung cancer patients are analysed to diagnose the disease at its early stage. The images are pre-processed using a series of steps such as the Gabor filter, contours to label the region of interest (ROI), increasing the sharpening and cropping of the image. Data augmentation is employed on the pre-processed images using two proposed architectures, namely (1) Convolutional Neural Network (CNN) and (2) Enhanced Integrated model for Lung Tumor Identification (EIM-LTI).
  • In this study, comparisons are made on non-pre-processed data, Haar and Gabor filters in CNN and the EIM-LTI models. The performance of the CNN and EIM-LTI models is evaluated through metrics such as precision, sensitivity, F1-score, specificity, training and validation accuracy.
  • The EIM-LTI model's training accuracy is 2.67 % higher than CNN, while its validation accuracy is 2.7 % higher. Additionally, the EIM-LTI model's validation loss is 0.0333 higher than CNN's.
  • In this study, a comparative analysis of model accuracies for lung cancer detection is performed. Cross-validation with 5 folds achieves an accuracy of 98.27 %, and the model was evaluated on unseen data and resulted in 92 % accuracy.
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来源期刊
MethodsX
MethodsX Health Professions-Medical Laboratory Technology
CiteScore
3.60
自引率
5.30%
发文量
314
审稿时长
7 weeks
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