1
|
Khan S, Khan A. SkinViT: A transformer based method for Melanoma and Nonmelanoma classification. PLoS One 2023; 18:e0295151. [PMID: 38150449 PMCID: PMC10752524 DOI: 10.1371/journal.pone.0295151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 11/14/2023] [Indexed: 12/29/2023] Open
Abstract
Over the past few decades, skin cancer has emerged as a major global health concern. The efficacy of skin cancer treatment greatly depends upon early diagnosis and effective treatment. The automated classification of Melanoma and Nonmelanoma is quite challenging task due to presence of high visual similarities across different classes and variabilities within each class. According to the best of our knowledge, this study represents the classification of Melanoma and Nonmelanoma utilising Basal Cell Carcinoma (BCC) and Squamous Cell Carcinoma (SCC) under the Nonmelanoma class for the first time. Therefore, this research focuses on automated detection of different skin cancer types to provide assistance to the dermatologists in timely diagnosis and treatment of Melanoma and Nonmelanoma patients. Recently, artificial intelligence (AI) methods have gained popularity where Convolutional Neural Networks (CNNs) are employed to accurately classify various skin diseases. However, CNN has limitation in its ability to capture global contextual information which may lead to missing important information. In order to address this issue, this research explores the outlook attention mechanism inspired by vision outlooker, which improves important features while suppressing noisy features. The proposed SkinViT architecture integrates an outlooker block, transformer block and MLP head block to efficiently capture both fine level and global features in order to enhance the accuracy of Melanoma and Nonmelanoma classification. The proposed SkinViT method is assessed by different performance metrics such as recall, precision, classification accuracy, and F1 score. We performed extensive experiments on three datasets, Dataset1 which is extracted from ISIC2019, Dataset2 collected from various online dermatological database and Dataset3 combines both datasets. The proposed SkinViT achieved 0.9109 accuracy on Dataset1, 0.8911 accuracy on Dataset3 and 0.8611 accuracy on Dataset2. Moreover, the proposed SkinViT method outperformed other SOTA models and displayed higher accuracy compared to the previous work in the literature. The proposed method demonstrated higher performance efficiency in classification of Melanoma and Nonmelanoma dermoscopic images. This work is expected to inspire further research in implementing a system for detecting skin cancer that can assist dermatologists in timely diagnosing Melanoma and Nonmelanoma patients.
Collapse
Affiliation(s)
- Somaiya Khan
- School of Electronics Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Ali Khan
- School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| |
Collapse
|
2
|
Chato L, Regentova E. Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data. J Pers Med 2023; 13:1703. [PMID: 38138930 PMCID: PMC10744730 DOI: 10.3390/jpm13121703] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 12/01/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023] Open
Abstract
Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.
Collapse
Affiliation(s)
- Lina Chato
- Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, NV 89154, USA;
| | | |
Collapse
|
3
|
Tang VH, Duong STM, Nguyen CDT, Huynh TM, Duc VT, Phan C, Le H, Bui T, Truong SQH. Wavelet radiomics features from multiphase CT images for screening hepatocellular carcinoma: analysis and comparison. Sci Rep 2023; 13:19559. [PMID: 37950031 PMCID: PMC10638447 DOI: 10.1038/s41598-023-46695-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 11/03/2023] [Indexed: 11/12/2023] Open
Abstract
Early detection of liver malignancy based on medical image analysis plays a crucial role in patient prognosis and personalized treatment. This task, however, is challenging due to several factors, including medical data scarcity and limited training samples. This paper presents a study of three important aspects of radiomics feature from multiphase computed tomography (CT) for classifying hepatocellular carcinoma (HCC) and other focal liver lesions: wavelet-transformed feature extraction, relevant feature selection, and radiomics features-based classification under the inadequate training samples. Our analysis shows that combining radiomics features extracted from the wavelet and original CT domains enhance the classification performance significantly, compared with using those extracted from the wavelet or original domain only. To facilitate the multi-domain and multiphase radiomics feature combination, we introduce a logistic sparsity-based model for feature selection with Bayesian optimization and find that the proposed model yields more discriminative and relevant features than several existing methods, including filter-based, wrapper-based, or other model-based techniques. In addition, we present analysis and performance comparison with several recent deep convolutional neural network (CNN)-based feature models proposed for hepatic lesion diagnosis. The results show that under the inadequate data scenario, the proposed wavelet radiomics feature model produces comparable, if not higher, performance metrics than the CNN-based feature models in terms of area under the curve.
Collapse
Affiliation(s)
- Van Ha Tang
- VinBrain JSC., 458 Minh Khai, Hanoi, 11619, Vietnam
- Le Quy Don Technical University, 236 Hoang Quoc Viet, Hanoi, 11917, Vietnam
| | - Soan T M Duong
- VinBrain JSC., 458 Minh Khai, Hanoi, 11619, Vietnam.
- Le Quy Don Technical University, 236 Hoang Quoc Viet, Hanoi, 11917, Vietnam.
| | - Chanh D Tr Nguyen
- VinBrain JSC., 458 Minh Khai, Hanoi, 11619, Vietnam
- VinUniversity, Vinhomes Ocean Park, Hanoi, 12406, Vietnam
| | - Thanh M Huynh
- VinBrain JSC., 458 Minh Khai, Hanoi, 11619, Vietnam
- VinUniversity, Vinhomes Ocean Park, Hanoi, 12406, Vietnam
| | - Vo T Duc
- University Medical Center Ho Chi Minh City, 215 Hong Bang, Ho Chi Minh City, 12406, Vietnam
| | - Chien Phan
- University Medical Center Ho Chi Minh City, 215 Hong Bang, Ho Chi Minh City, 12406, Vietnam
| | - Huyen Le
- University Medical Center Ho Chi Minh City, 215 Hong Bang, Ho Chi Minh City, 12406, Vietnam
| | - Trung Bui
- Adobe Research, San Francisco, CA, 94103, USA
| | - Steven Q H Truong
- VinBrain JSC., 458 Minh Khai, Hanoi, 11619, Vietnam
- VinUniversity, Vinhomes Ocean Park, Hanoi, 12406, Vietnam
| |
Collapse
|
4
|
Jacobson MJ, Masry ME, Arrubla DC, Tricas MR, Gnyawali SC, Zhang X, Gordillo G, Xue Y, Sen CK, Wachs J. Autonomous Multi-modality Burn Wound Characterization using Artificial Intelligence. Mil Med 2023; 188:674-681. [PMID: 37948279 DOI: 10.1093/milmed/usad301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 04/05/2023] [Accepted: 08/10/2023] [Indexed: 11/12/2023] Open
Abstract
INTRODUCTION Between 5% and 20% of all combat-related casualties are attributed to burn wounds. A decrease in the mortality rate of burns by about 36% can be achieved with early treatment, but this is contingent upon accurate characterization of the burn. Precise burn injury classification is recognized as a crucial aspect of the medical artificial intelligence (AI) field. An autonomous AI system designed to analyze multiple characteristics of burns using modalities including ultrasound and RGB images is described. MATERIALS AND METHODS A two-part dataset is created for the training and validation of the AI: in vivo B-mode ultrasound scans collected from porcine subjects (10,085 frames), and RGB images manually collected from web sources (338 images). The framework in use leverages an explanation system to corroborate and integrate burn expert's knowledge, suggesting new features and ensuring the validity of the model. Through the utilization of this framework, it is discovered that B-mode ultrasound classifiers can be enhanced by supplying textural features. More specifically, it is confirmed that statistical texture features extracted from ultrasound frames can increase the accuracy of the burn depth classifier. RESULTS The system, with all included features selected using explainable AI, is capable of classifying burn depth with accuracy and F1 average above 80%. Additionally, the segmentation module has been found capable of segmenting with a mean global accuracy greater than 84%, and a mean intersection-over-union score over 0.74. CONCLUSIONS This work demonstrates the feasibility of accurate and automated burn characterization for AI and indicates that these systems can be improved with additional features when a human expert is combined with explainable AI. This is demonstrated on real data (human for segmentation and porcine for depth classification) and establishes the groundwork for further deep-learning thrusts in the area of burn analysis.
Collapse
Affiliation(s)
- Maxwell J Jacobson
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Mohamed El Masry
- School of Medicine, Indiana University, Indianapolis, IN 46202, USA
| | | | - Maria Romeo Tricas
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Surya C Gnyawali
- School of Medicine, Indiana University, Indianapolis, IN 46202, USA
| | - Xinwei Zhang
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Gayle Gordillo
- School of Medicine, Indiana University, Indianapolis, IN 46202, USA
| | - Yexiang Xue
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Chandan K Sen
- School of Medicine, Indiana University, Indianapolis, IN 46202, USA
| | - Juan Wachs
- School of Industrial Engineering, Purdue University, West Lafayette, IN 47907, USA
| |
Collapse
|
5
|
Rhyou SY, Yoo JC. Aggregated micropatch-based deep learning neural network for ultrasonic diagnosis of cirrhosis. Artif Intell Med 2023; 139:102541. [PMID: 37100510 DOI: 10.1016/j.artmed.2023.102541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 03/15/2023] [Accepted: 03/27/2023] [Indexed: 04/28/2023]
Abstract
Despite the advancements in the diagnosis of early-stage cirrhosis, the accuracy in the diagnosis using ultrasound is still challenging owing to the presence of various image artifacts, which results in poor visual quality of the textural and lower-frequency components. In this study, we propose an end-to-end multistep network called CirrhosisNet that includes two transfer-learned convolutional neural networks for semantic segmentation and classification tasks. It uses a uniquely designed image, called an aggregated micropatch (AMP), as an input image to the classification network, thereby assessing whether the liver is in a cirrhotic stage. With a prototype AMP image, we synthesized a bunch of AMP images while retaining the textural features. This synthesis significantly increases the number of insufficient cirrhosis-labeled images, thereby circumventing overfitting issues and optimizing network performance. Furthermore, the synthesized AMP images contained unique textural patterns, mostly generated on the boundaries between adjacent micropatches (μ-patches) during their aggregation. These newly created boundary patterns provide rich information regarding the texture features of the ultrasound image, thereby making cirrhosis diagnosis more accurate and sensitive. The experimental results demonstrated that our proposed AMP image synthesis is extremely effective in expanding the dataset of cirrhosis images, thus diagnosing liver cirrhosis with considerably high accuracy. We achieved an accuracy of 99.95 %, a sensitivity of 100 %, and a specificity of 99.9 % on the Samsung Medical Center dataset using 8 × 8 pixels-sized μ-patches. The proposed approach provides an effective solution to deep-learning models with limited-training data, such as medical imaging tasks.
Collapse
Affiliation(s)
- Se-Yeol Rhyou
- Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 440-746, South Korea
| | - Jae-Chern Yoo
- Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 440-746, South Korea.
| |
Collapse
|
6
|
Ansari MY, Yang Y, Meher PK, Dakua SP. Dense-PSP-UNet: A neural network for fast inference liver ultrasound segmentation. Comput Biol Med 2023; 153:106478. [PMID: 36603437 DOI: 10.1016/j.compbiomed.2022.106478] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 11/29/2022] [Accepted: 12/21/2022] [Indexed: 01/02/2023]
Abstract
Liver Ultrasound (US) or sonography is popularly used because of its real-time output, low-cost, ease-of-use, portability, and non-invasive nature. Segmentation of real-time liver US is essential for diagnosing and analyzing liver conditions (e.g., hepatocellular carcinoma (HCC)), assisting the surgeons/radiologists in therapeutic procedures. In this paper, we propose a method using a modified Pyramid Scene Parsing (PSP) module in tuned neural network backbones to achieve real-time segmentation without compromising the segmentation accuracy. Considering widespread noise in US data and its impact on outcomes, we study the impact of pre-processing and the influence of loss functions on segmentation performance. We have tested our method after annotating a publicly available US dataset containing 2400 images of 8 healthy volunteers (link to the annotated dataset is provided); the results show that the Dense-PSP-UNet model achieves a high Dice coefficient of 0.913±0.024 while delivering a real-time performance of 37 frames per second (FPS).
Collapse
Affiliation(s)
| | - Yin Yang
- Hamad Bin Khalifa Uinversity, Doha, Qatar
| | | | | |
Collapse
|
7
|
Explainable Ensemble Machine Learning for Breast Cancer Diagnosis Based on Ultrasound Image Texture Features. FORECASTING 2022. [DOI: 10.3390/forecast4010015] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Image classification is widely used to build predictive models for breast cancer diagnosis. Most existing approaches overwhelmingly rely on deep convolutional networks to build such diagnosis pipelines. These model architectures, although remarkable in performance, are black-box systems that provide minimal insight into the inner logic behind their predictions. This is a major drawback as the explainability of prediction is vital for applications such as cancer diagnosis. In this paper, we address this issue by proposing an explainable machine learning pipeline for breast cancer diagnosis based on ultrasound images. We extract first- and second-order texture features of the ultrasound images and use them to build a probabilistic ensemble of decision tree classifiers. Each decision tree learns to classify the input ultrasound image by learning a set of robust decision thresholds for texture features of the image. The decision path of the model predictions can then be interpreted by decomposing the learned decision trees. Our results show that our proposed framework achieves high predictive performance while being explainable.
Collapse
|
8
|
|
9
|
Garg V, Sahoo A, Saxena V. A cognitive approach to endometrial tuberculosis identification using hierarchical deep fusion method. Soft comput 2021. [DOI: 10.1007/s00500-021-06474-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
10
|
Yang Y, Nie J, Kan Z, Yang S, Zhao H, Li J. Cotton stubble detection based on wavelet decomposition and texture features. PLANT METHODS 2021; 17:113. [PMID: 34727933 PMCID: PMC8561878 DOI: 10.1186/s13007-021-00809-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 10/12/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND At present, the residual film pollution in cotton fields is crucial. The commonly used recycling method is the manual-driven recycling machine, which is heavy and time-consuming. The development of a visual navigation system for the recovery of residual film is conducive, in order to improve the work efficiency. The key technology in the visual navigation system is the cotton stubble detection. A successful cotton stubble detection can ensure the stability and reliability of the visual navigation system. METHODS Firstly, it extracts the three types of texture features of GLCM, GLRLM and LBP, from the three types of images of stubbles, residual films and broken leaves between rows. It then builds three classifiers: Random Forest, Back Propagation Neural Network and Support Vector Machine in order to classify the sample images. Finally, the possibility of improving the classification accuracy using the texture features extracted from the wavelet decomposition coefficients, is discussed. RESULTS The experiment proves that the GLCM texture feature of the original image has the best performance under the Back Propagation Neural Network classifier. As for the different wavelet bases, the vertical coefficient texture feature of coif3 wavelet decomposition, combined with the texture feature of the original image, is the feature having the best classification effect. Compared with the original image texture features, the classification accuracy is increased by 3.8%, the sensitivity is increased by 4.8%, and the specificity is increased by 1.2%. CONCLUSIONS The algorithm can complete the task of stubble detection in different locations, different periods and abnormal driving conditions, which shows that the wavelet coefficient texture feature combined with the original image texture feature is a useful fusion feature for detecting stubble and can provide a reference for different crop stubble detection.
Collapse
Affiliation(s)
- Yukun Yang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 832000, Xinjiang, China
- Industrial Technology Research Institute - XPCC, Xinjiang Production and Construction Corps (XPCC), Shihezi, 832000, Xinjiang, China
| | - Jing Nie
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 832000, Xinjiang, China
- Industrial Technology Research Institute - XPCC, Xinjiang Production and Construction Corps (XPCC), Shihezi, 832000, Xinjiang, China
| | - Za Kan
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 832000, Xinjiang, China
- Industrial Technology Research Institute - XPCC, Xinjiang Production and Construction Corps (XPCC), Shihezi, 832000, Xinjiang, China
| | - Shuo Yang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 832000, Xinjiang, China
- Industrial Technology Research Institute - XPCC, Xinjiang Production and Construction Corps (XPCC), Shihezi, 832000, Xinjiang, China
| | - Hangxing Zhao
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 832000, Xinjiang, China
- Industrial Technology Research Institute - XPCC, Xinjiang Production and Construction Corps (XPCC), Shihezi, 832000, Xinjiang, China
| | - Jingbin Li
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 832000, Xinjiang, China.
- Industrial Technology Research Institute - XPCC, Xinjiang Production and Construction Corps (XPCC), Shihezi, 832000, Xinjiang, China.
| |
Collapse
|
11
|
Santhosh Reddy D, Rajalakshmi P, Mateen M. A deep learning based approach for classification of abdominal organs using ultrasound images. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.05.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
12
|
Focal Liver Lesion Detection in Ultrasound Image Using Deep Feature Fusions and Super Resolution. MACHINE LEARNING AND KNOWLEDGE EXTRACTION 2020. [DOI: 10.3390/make2030010] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
This research presents a machine vision approach to detect lesions in liver ultrasound as well as resolving some issues in ultrasound such as artifacts, speckle noise, and blurring effect. The anisotropic diffusion is modified using the edge preservation conditions which found better than traditional ones in quantitative evolution. To dig for more potential information, a learnable super-resolution (SR) is embedded into the deep CNN. The feature is fused using Gabor Wavelet Transform (GWT) and Local Binary Pattern (LBP) with a pre-trained deep CNN model. Moreover, we propose a Bayes rule-based informative patch selection approach to reduce the processing time with the selective image patches and design an algorithm to mark the lesion region from identified ultrasound image patches. To train this model, standard data ensures promising resolution. The testing phase considers generalized data with a varying resolution and test the performance of the model. Exploring cross-validation, it finds that a 5-fold strategy can successfully eradicate the overfitting problem. Experiment data are collected using 298 consecutive ultrasounds comprising 15,296 image patches. This proposed feature fusion technique confirms satisfactory performance compared to the current relevant works with an accuracy of 98.40%.
Collapse
|
13
|
Polyphenolic extracts from Wushan tea leaves attenuate hepatic injury in CCl4-treated mice. J Funct Foods 2020. [DOI: 10.1016/j.jff.2020.103826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
|