Examining feature extraction and classification modules in machine learning for diagnosis of low-dose computed tomographic screening-detected in vivo lesions

Daniel D. Liang; David D. Liang; Marc J. Pomeroy; Yongfeng Gao; Licheng R. Kuo; Lihong C. Li

doi:10.1117/1.JMI.11.4.044501

9 July 2024 Examining feature extraction and classification modules in machine learning for diagnosis of low-dose computed tomographic screening-detected in vivo lesions

Daniel D. Liang, David D. Liang, Marc J. Pomeroy, Yongfeng Gao, Licheng R. Kuo, Lihong C. Li

Author Affiliations +

Journal of Medical Imaging, Vol. 11, Issue 4, 044501 (July 2024). https://doi.org/10.1117/1.JMI.11.4.044501

Abstract

Purpose

Medical imaging-based machine learning (ML) for computer-aided diagnosis of in vivo lesions consists of two basic components or modules of (i) feature extraction from non-invasively acquired medical images and (ii) feature classification for prediction of malignancy of lesions detected or localized in the medical images. This study investigates their individual performances for diagnosis of low-dose computed tomography (CT) screening-detected lesions of pulmonary nodules and colorectal polyps.

Approach

Three feature extraction methods were investigated. One uses the mathematical descriptor of gray-level co-occurrence image texture measure to extract the Haralick image texture features (HFs). One uses the convolutional neural network (CNN) architecture to extract deep learning (DL) image abstractive features (DFs). The third one uses the interactions between lesion tissues and X-ray energy of CT to extract tissue-energy specific characteristic features (TFs). All the above three categories of extracted features were classified by the random forest (RF) classifier with comparison to the DL-CNN method, which reads the images, extracts the DFs, and classifies the DFs in an end-to-end manner. The ML diagnosis of lesions or prediction of lesion malignancy was measured by the area under the receiver operating characteristic curve (AUC). Three lesion image datasets were used. The lesions’ tissue pathological reports were used as the learning labels.

Results

Experiments on the three datasets produced AUC values of 0.724 to 0.878 for the HFs, 0.652 to 0.965 for the DFs, and 0.985 to 0.996 for the TFs, compared to the DL-CNN of 0.694 to 0.964. These experimental outcomes indicate that the RF classifier performed comparably to the DL-CNN classification module and the extraction of tissue-energy specific characteristic features dramatically improved AUC value.

Conclusions

The feature extraction module is more important than the feature classification module. Extraction of tissue-energy specific characteristic features is more important than extraction of image abstractive and characteristic features.

Citation Download Citation

Daniel D. Liang, David D. Liang, Marc J. Pomeroy, Yongfeng Gao, Licheng R. Kuo, and Lihong C. Li "Examining feature extraction and classification modules in machine learning for diagnosis of low-dose computed tomographic screening-detected in vivo lesions," Journal of Medical Imaging 11(4), 044501 (9 July 2024). https://doi.org/10.1117/1.JMI.11.4.044501

Received: 6 April 2024; Accepted: 3 June 2024; Published: 9 July 2024

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KEYWORDS

Feature extraction

Image classification

Computed tomography

Polyps

Tissues

Machine learning

In vivo imaging

Purpose

Approach

Results

Conclusions

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