Computer-aided diagnosis of hepatic cystic echinococcosis based on deep transfer learning features from ultrasound images
Abstract Hepatic cystic echinococcosis (HCE), a life-threatening liver disease, has 5 subtypes, i.e., single-cystic, polycystic, internal capsule collapse, solid mass, and calcified subtypes. And each subtype has different treatment methods. An accurate diagnosis is the prerequisite for effective HC...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-85004-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841559608411291648 |
|---|---|
| author | Miao Wu Chuanbo Yan Gan Sen |
| author_facet | Miao Wu Chuanbo Yan Gan Sen |
| author_sort | Miao Wu |
| collection | DOAJ |
| description | Abstract Hepatic cystic echinococcosis (HCE), a life-threatening liver disease, has 5 subtypes, i.e., single-cystic, polycystic, internal capsule collapse, solid mass, and calcified subtypes. And each subtype has different treatment methods. An accurate diagnosis is the prerequisite for effective HCE treatment. However, clinicians with less diagnostic experience often make misdiagnoses of HCE and confuse its 5 subtypes in clinical practice. Computer-aided diagnosis (CAD) techniques can help clinicians to improve their diagnostic performance. This paper aims to propose an efficient CAD system that automatically differentiates 5 subtypes of HCE from the ultrasound images. The proposed CAD system adopts the concept of deep transfer learning and uses a pre-trained convolutional neural network (CNN) named VGG19 to extract deep CNN features from the ultrasound images. The proven classifier models, k - nearest neighbor (KNN) and support vecter machine (SVM) models, are integrated to classify the extracted deep CNN features. 3 distinct experiments with the same deep CNN features but different classifier models (softmax, KNN, SVM) are performed. The experiments followed 10 runs of the five-fold cross-validation process on a total of 1820 ultrasound images and the results were compared using Wilcoxon signed-rank test. The overall classification accuracy from low to high was 90.46 ± 1.59% for KNN classifier, 90.92 ± 2.49% for transfer learned VGG19, and 92.01 ± 1.48% for SVM, indicating SVM classifiers with deep CNN features achieved the best performance (P < 0.05). Other performance measures used in the study are specificity, sensitivity, precision, F1-score, and area under the curve (AUC). In addition, the paper addresses a practical aspect by evaluating the system with smaller training data to demonstrate the capability of the proposed classification system. The observations of the study imply that transfer learning is a useful technique when the availability of medical images is limited. The proposed classification system by using deep CNN features and SVM classifier is potentially helpful for clinicians to improve their HCE diagnostic performance in clinical practice. |
| format | Article |
| id | doaj-art-8d6b780e997a4198b7e7bc546ba226c1 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8d6b780e997a4198b7e7bc546ba226c12025-01-05T12:21:00ZengNature PortfolioScientific Reports2045-23222025-01-0115111210.1038/s41598-024-85004-9Computer-aided diagnosis of hepatic cystic echinococcosis based on deep transfer learning features from ultrasound imagesMiao Wu0Chuanbo Yan1Gan Sen2College of Medical Engineering and Technology, Xinjiang Medical UniversityCollege of Medical Engineering and Technology, Xinjiang Medical UniversityCollege of Medical Engineering and Technology, Xinjiang Medical UniversityAbstract Hepatic cystic echinococcosis (HCE), a life-threatening liver disease, has 5 subtypes, i.e., single-cystic, polycystic, internal capsule collapse, solid mass, and calcified subtypes. And each subtype has different treatment methods. An accurate diagnosis is the prerequisite for effective HCE treatment. However, clinicians with less diagnostic experience often make misdiagnoses of HCE and confuse its 5 subtypes in clinical practice. Computer-aided diagnosis (CAD) techniques can help clinicians to improve their diagnostic performance. This paper aims to propose an efficient CAD system that automatically differentiates 5 subtypes of HCE from the ultrasound images. The proposed CAD system adopts the concept of deep transfer learning and uses a pre-trained convolutional neural network (CNN) named VGG19 to extract deep CNN features from the ultrasound images. The proven classifier models, k - nearest neighbor (KNN) and support vecter machine (SVM) models, are integrated to classify the extracted deep CNN features. 3 distinct experiments with the same deep CNN features but different classifier models (softmax, KNN, SVM) are performed. The experiments followed 10 runs of the five-fold cross-validation process on a total of 1820 ultrasound images and the results were compared using Wilcoxon signed-rank test. The overall classification accuracy from low to high was 90.46 ± 1.59% for KNN classifier, 90.92 ± 2.49% for transfer learned VGG19, and 92.01 ± 1.48% for SVM, indicating SVM classifiers with deep CNN features achieved the best performance (P < 0.05). Other performance measures used in the study are specificity, sensitivity, precision, F1-score, and area under the curve (AUC). In addition, the paper addresses a practical aspect by evaluating the system with smaller training data to demonstrate the capability of the proposed classification system. The observations of the study imply that transfer learning is a useful technique when the availability of medical images is limited. The proposed classification system by using deep CNN features and SVM classifier is potentially helpful for clinicians to improve their HCE diagnostic performance in clinical practice.https://doi.org/10.1038/s41598-024-85004-9Hepatic cystic echinococcosisMulti-classificationConvolutional neural networksUltrasound imagesTransfer learningSupport vector machine |
| spellingShingle | Miao Wu Chuanbo Yan Gan Sen Computer-aided diagnosis of hepatic cystic echinococcosis based on deep transfer learning features from ultrasound images Scientific Reports Hepatic cystic echinococcosis Multi-classification Convolutional neural networks Ultrasound images Transfer learning Support vector machine |
| title | Computer-aided diagnosis of hepatic cystic echinococcosis based on deep transfer learning features from ultrasound images |
| title_full | Computer-aided diagnosis of hepatic cystic echinococcosis based on deep transfer learning features from ultrasound images |
| title_fullStr | Computer-aided diagnosis of hepatic cystic echinococcosis based on deep transfer learning features from ultrasound images |
| title_full_unstemmed | Computer-aided diagnosis of hepatic cystic echinococcosis based on deep transfer learning features from ultrasound images |
| title_short | Computer-aided diagnosis of hepatic cystic echinococcosis based on deep transfer learning features from ultrasound images |
| title_sort | computer aided diagnosis of hepatic cystic echinococcosis based on deep transfer learning features from ultrasound images |
| topic | Hepatic cystic echinococcosis Multi-classification Convolutional neural networks Ultrasound images Transfer learning Support vector machine |
| url | https://doi.org/10.1038/s41598-024-85004-9 |
| work_keys_str_mv | AT miaowu computeraideddiagnosisofhepaticcysticechinococcosisbasedondeeptransferlearningfeaturesfromultrasoundimages AT chuanboyan computeraideddiagnosisofhepaticcysticechinococcosisbasedondeeptransferlearningfeaturesfromultrasoundimages AT gansen computeraideddiagnosisofhepaticcysticechinococcosisbasedondeeptransferlearningfeaturesfromultrasoundimages |