A multi-task convolutional neural network for classification and segmentation of chronic venous disorders
Abstract Chronic Venous Disorders (CVD) of the lower limbs are one of the most prevalent medical conditions, affecting 35% of adults in Europe and North America. Due to the exponential growth of the aging population and the worsening of CVD with age, it is expected that the healthcare costs and the...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-27089-8 |
| _version_ | 1797952539503624192 |
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| author | Bruno Oliveira Helena R. Torres Pedro Morais Fernando Veloso António L. Baptista Jaime C. Fonseca João L. Vilaça |
| author_facet | Bruno Oliveira Helena R. Torres Pedro Morais Fernando Veloso António L. Baptista Jaime C. Fonseca João L. Vilaça |
| author_sort | Bruno Oliveira |
| collection | DOAJ |
| description | Abstract Chronic Venous Disorders (CVD) of the lower limbs are one of the most prevalent medical conditions, affecting 35% of adults in Europe and North America. Due to the exponential growth of the aging population and the worsening of CVD with age, it is expected that the healthcare costs and the resources needed for the treatment of CVD will increase in the coming years. The early diagnosis of CVD is fundamental in treatment planning, while the monitoring of its treatment is fundamental to assess a patient’s condition and quantify the evolution of CVD. However, correct diagnosis relies on a qualitative approach through visual recognition of the various venous disorders, being time-consuming and highly dependent on the physician’s expertise. In this paper, we propose a novel automatic strategy for the joint segmentation and classification of CVDs. The strategy relies on a multi-task deep learning network, denominated VENet, that simultaneously solves segmentation and classification tasks, exploiting the information of both tasks to increase learning efficiency, ultimately improving their performance. The proposed method was compared against state-of-the-art strategies in a dataset of 1376 CVD images. Experiments showed that the VENet achieved a classification performance of 96.4%, 96.4%, and 97.2% for accuracy, precision, and recall, respectively, and a segmentation performance of 75.4%, 76.7.0%, 76.7% for the Dice coefficient, precision, and recall, respectively. The joint formulation increased the robustness of both tasks when compared to the conventional classification or segmentation strategies, proving its added value, mainly for the segmentation of small lesions. |
| first_indexed | 2024-04-10T22:48:00Z |
| format | Article |
| id | doaj.art-3ad64f5e2bff45abbd06c8cf37b6b31c |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-10T22:48:00Z |
| publishDate | 2023-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-3ad64f5e2bff45abbd06c8cf37b6b31c2023-01-15T12:11:14ZengNature PortfolioScientific Reports2045-23222023-01-0113111510.1038/s41598-022-27089-8A multi-task convolutional neural network for classification and segmentation of chronic venous disordersBruno Oliveira0Helena R. Torres1Pedro Morais2Fernando Veloso3António L. Baptista4Jaime C. Fonseca5João L. Vilaça6Life and Health Sciences Research Institute (ICVS), School of Medicine, University of MinhoLife and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho2Ai – School of Technology, IPCALife and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho2Ai – School of Technology, IPCAAlgoritmi Center, School of Engineering, University of Minho2Ai – School of Technology, IPCAAbstract Chronic Venous Disorders (CVD) of the lower limbs are one of the most prevalent medical conditions, affecting 35% of adults in Europe and North America. Due to the exponential growth of the aging population and the worsening of CVD with age, it is expected that the healthcare costs and the resources needed for the treatment of CVD will increase in the coming years. The early diagnosis of CVD is fundamental in treatment planning, while the monitoring of its treatment is fundamental to assess a patient’s condition and quantify the evolution of CVD. However, correct diagnosis relies on a qualitative approach through visual recognition of the various venous disorders, being time-consuming and highly dependent on the physician’s expertise. In this paper, we propose a novel automatic strategy for the joint segmentation and classification of CVDs. The strategy relies on a multi-task deep learning network, denominated VENet, that simultaneously solves segmentation and classification tasks, exploiting the information of both tasks to increase learning efficiency, ultimately improving their performance. The proposed method was compared against state-of-the-art strategies in a dataset of 1376 CVD images. Experiments showed that the VENet achieved a classification performance of 96.4%, 96.4%, and 97.2% for accuracy, precision, and recall, respectively, and a segmentation performance of 75.4%, 76.7.0%, 76.7% for the Dice coefficient, precision, and recall, respectively. The joint formulation increased the robustness of both tasks when compared to the conventional classification or segmentation strategies, proving its added value, mainly for the segmentation of small lesions.https://doi.org/10.1038/s41598-022-27089-8 |
| spellingShingle | Bruno Oliveira Helena R. Torres Pedro Morais Fernando Veloso António L. Baptista Jaime C. Fonseca João L. Vilaça A multi-task convolutional neural network for classification and segmentation of chronic venous disorders Scientific Reports |
| title | A multi-task convolutional neural network for classification and segmentation of chronic venous disorders |
| title_full | A multi-task convolutional neural network for classification and segmentation of chronic venous disorders |
| title_fullStr | A multi-task convolutional neural network for classification and segmentation of chronic venous disorders |
| title_full_unstemmed | A multi-task convolutional neural network for classification and segmentation of chronic venous disorders |
| title_short | A multi-task convolutional neural network for classification and segmentation of chronic venous disorders |
| title_sort | multi task convolutional neural network for classification and segmentation of chronic venous disorders |
| url | https://doi.org/10.1038/s41598-022-27089-8 |
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