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

Autor:	Liang DD; Ward Melville High School, East Setauket, New York, United States., Liang DD; University of Chicago, Department of Computer Science, Chicago, Illinois, United States., Pomeroy MJ; State University of New York, Department of Biomedical Engineering, Stony Brook, New York, United States.; State University of New York, Department of Radiology, Stony Brook, New York, United States., Gao Y; State University of New York, Department of Radiology, Stony Brook, New York, United States., Kuo LR; State University of New York, Department of Biomedical Engineering, Stony Brook, New York, United States.; State University of New York, Department of Radiology, Stony Brook, New York, United States., Li LC; City University of New York/CSI, Department of Engineering and Environment Science, Staten Island, New York, United States.
Jazyk:	angličtina
Zdroj:	Journal of medical imaging (Bellingham, Wash.) [J Med Imaging (Bellingham)] 2024 Jul; Vol. 11 (4), pp. 044501. Date of Electronic Publication: 2024 Jul 09.
DOI:	10.1117/1.JMI.11.4.044501
Abstrakt:	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. (© 2024 Society of Photo-Optical Instrumentation Engineers (SPIE).)
Databáze:	MEDLINE
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