Figure 6 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Confusion matrices. (A) The confusion matrix of the standard CNN model with Adam optimizer and a learning rate of 0.0001 after a 25% test split. (B) The confusion matrix of the customized CNN model with RMSprop optimizer and a learning rate of 0.001 after 25% test split.
Source publication
Anterior cruciate ligament (ACL) tear is caused by partially or completely torn ACL ligament in the knee, especially in sportsmen. There is a need to classify the ACL tear before it fully ruptures to avoid osteoarthritis. This research aims to identify ACL tears automatically and efficiently with a deep learning approach. A dataset was gathered, co...
Contexts in source publication
Context 1
... Figure 6 shows the value of healthy, partial, and fully ruptured tears in the confusion matrix. Then, the confusion matrix plots are taken from the best technique of both of our models. ...
Context 2
... the confusion matrix plots are taken from the best technique of both of our models. Figure 6A shows the confusion matrix of the standard CNN model with Adam optimizer and a learning rate of 0.0001. Similarly, Figure 6B shows the confusion matrix of the customized CNN model with RMS optimizer and a learning rate of 0.001 after a 25% test split. ...
Context 3
... 6A shows the confusion matrix of the standard CNN model with Adam optimizer and a learning rate of 0.0001. Similarly, Figure 6B shows the confusion matrix of the customized CNN model with RMS optimizer and a learning rate of 0.001 after a 25% test split. ...
Context 4
... we compared the results of our best performing standard CNN with the customized CNN in three classes of ACL tears by MRI in terms of confusion matrices, training and test model accuracy plots, and ROC AUC curves. Firstly, Figure 6 shows the value of healthy, partial, and fully ruptured tears in the confusion matrix. Then, the confusion matrix plots are taken from the best technique of both of our models. ...
Context 5
... the confusion matrix plots are taken from the best technique of both of our models. Figure 6A shows the confusion matrix of the standard CNN model with Adam optimizer and a learning rate of 0.0001. Similarly, Figure 6B shows the confusion matrix of the customized CNN model with RMS optimizer and a learning rate of 0.001 after a 25% test split. ...
Context 6
... 6A shows the confusion matrix of the standard CNN model with Adam optimizer and a learning rate of 0.0001. Similarly, Figure 6B shows the confusion matrix of the customized CNN model with RMS optimizer and a learning rate of 0.001 after a 25% test split. Secondly, we plotted the accuracy of the training and test results of our models. ...
Similar publications
Anterior cruciate ligament (ACL) tears within the skeletally immature population give rise to controversy regarding the timing of treatment decisions due to the concern of iatrogenic damage to the open physis. Physis disruption from the required intraoperative graft tunnel drilling can lead to growth disturbance, thus ligament reconstruction is not...
Citations
... After proofing the effectiveness of using the preprocessing in our work, we need to validate it more through the outcomes with some other modern approaches. In this study, the outcomes are compared with Convolutional Neural Network (CNN) 34 , Inception-v3 35 www.nature.com/scientificreports/ In this study, simulations were performed utilizing the MRNet dataset to evaluate the classification accuracy of the analyzed procedures. ...
In the current research study, a new method is presented to diagnose Anterior Cruciate Ligament (ACL) tears by introducing an optimized version of the InceptionV4 model. Our proposed methodology utilizes a custom-made variant of the Snow Leopard Optimization Algorithm, known as the Fractional-order Snow Leopard Optimization Algorithm (FO-LOA), to extract essential features from knee magnetic resonance imaging (MRI) images. This results in a substantial improvement in the accuracy of ACL tear detection. By effectively extracting critical features from knee MRI images, our proposed methodology significantly enhances diagnostic accuracy, potentially reducing false negatives and false positives. The enhanced model based on FO-LOA underwent thorough testing using the MRNet dataset, demonstrating exceptional performance metrics including an accuracy rate of 98.00%, sensitivity of 98.00%, precision of 97.00%, specificity of 98.00%, F1-score of 98.00%, and Matthews Correlation Coefficient (MCC) of 88.00%. These findings surpass current methodologies like Convolutional Neural Network (CNN), Inception-v3, Deep Belief Networks and Improved Honey Badger Algorithm (DBN/IHBA), integration of the CNN with an Amended Cooking Training-based Optimizer version (CNN/ACTO), Self-Supervised Representation Learning (SSRL), signifying a significant breakthrough in ACL injury diagnosis. Using FO-SLO to optimize the InceptionV4 framework shows promise in improving the accuracy of ACL tear identification, enabling prompt and efficient treatment interventions.
... Since 2021, there has been an exponential increase in studies on custom architecture CNNs for the diagnosis of ACL injuries applied to MRI, and currently there are various DL models developed, such as VGG16, VGG19, U-Net, AdaBoost, XGBoost, Xception, MRPyrNet, Inception ResNet-v2, RadImageNet, and Inception-v3 DTL [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51]. Awan et al. introduced a method that utilizes a tailored 14-layer ResNet-14 configuration of a CNN, which processes data in six distinct directions. ...
The remarkable progress in data aggregation and deep learning algorithms has positioned artificial intelligence (AI) and machine learning (ML) to revolutionize the field of medicine. AI is becoming more and more prevalent in the healthcare sector, and its impact on orthopedic surgery is already evident in several fields. This review aims to examine the literature that explores the comprehensive clinical relevance of AI-based tools utilized before, during, and after anterior cruciate ligament (ACL) reconstruction. The review focuses on current clinical applications and future prospects in preoperative management, encompassing risk prediction and diagnostics; intraoperative tools, specifically navigation, identifying complex anatomic landmarks during surgery; and postoperative applications in terms of postoperative care and rehabilitation. Additionally, AI tools in educational and training settings are presented. Orthopedic surgeons are showing a growing interest in AI, as evidenced by the applications discussed in this review, particularly those related to ACL injury. The exponential increase in studies on AI tools applicable to the management of ACL tears promises a significant future impact in its clinical application, with growing attention from orthopedic surgeons.
... (2) Batch size is set to 4; training epoch is set to 100; Adam optimizer with initial learning rate of 0.001 is selected for gradient backpropagation and the learning rate is reduced to the half every 20 epochs. Note that we keep the same learning rate when training the appearance network, the motion network and the later fusion network, since the Adam optimizer is proved to be insensitive to the setting of learning rate [39], [40]. (3) The optical flow learner is first trained under the supervision of L op and then fine-tuned under the supervision of both cross-entropy loss and triplet loss. ...
Crash detection is of great significance for traffic emergency management. Video-based approaches can effectively save the manpower monitoring cost and have achieved promising results in recent studies. However, they sometimes fail to correctly identify crashes in congested traffic. To fill the gap, this paper proposes a novel appearance-motion network for improving video-based crash detection performance in congested traffic. The appearance-motion network utilizes two paralleled convolutional networks (i.e., an appearance network and a motion network) to extract both appearance features and motion features of crashes. To learn discriminative appearance features for differentiating crashes in congested traffic scene (CCT) with non-crashes in congested traffic scene (NCCT), an auxiliary network combined with a triplet loss are introduced to train the appearance network. To better capture crash motion features in congested traffic, an optical flow learner is built in the motion network and trained to extract more fine-grained motion information. Moreover, a temporal attention module is applied to enable the motion network to focus on valuable frames. Experimental results show that the proposed network achieves a state-of-the-art result on crash detection and the introduction of the three components (i.e., the auxiliary network, the optical flow learner and the temporal attention module) effectively reduces false alarm rate by 28.07% and miss rate by 27.08% on crash detection in congested traffic. Our dataset will be available at https://github.com/vvgoder/Dataset_for_crashdetection.
... Recently, deep learning (DL) methods have been widely used in medical imaging analysis and have emerged as feasible approaches for ACL diagnosis, due to their excellent ability in grasping complex features to overcome the above-mentioned challenges [5][6][7]. Deep learning architectures such as LeNet-5 CNN [8], CNNs [9], multilayered convolutional sparse coding (ML-CSC) [10], and 3D CNN [11] have been used for auxiliary diagnosis of ACL. Chang et al. proposed three deep learning network architectures for the detection of complete anterior cruciate ligament tear [12]. ...
The anterior cruciate ligament (ACL) is one of the most commonly injured ligaments in the knee. Accurate tear quantification of ACL plays a crucial role in the treatment of patients. This study aims to propose an auxiliary diagnosis scheme based on deep learning (DL) to assist orthopedic surgeons in automatic ACL tear localization and quantification. The proposed scheme adopted a decoupled generative adversarial network (GAN) to generate the distal residual mask and the normal ACL mask, thereby achieving ACL tear classification. Since the edge information of ACL is important in tear classification, we built the decoupled GAN by decoupling the body and edge parts of masks with different supervision and improved its segmentation performance through a histogram equalization for enhancing image quality, an atrous spatial pyramid pooling (ASPP) module and a distribution module for improving feature representations as well as an effective channel attention mechanism. The experiments showed that the decoupled GAN model achieved promising results in the test set and demonstrated its feasibility in ACL segmentation. The proposed scheme also achieved good results in ACL tear quantification (accuracy: 0.929) and yielded a comparable performance with a senior orthopedic surgeon. This work can provide valuable diagnosis evidence of ACL tear for orthopedic surgeons and is expected to apply in clinical practice.
... Belton et al. [15] utilized spatial attention [16] integrated onto ResNet-18 to optimize knee injury detection, achieving a high AUC of 97.7% for ACL tear detection. Awan et al. [17] designed a custom CNN model to classify ACL tears, achieving excellent performance with accuracy, precision, sensitivity, specificity, and F1-score metrics all above 98%. Chang et al. [18] conducted a study on the influence of input field of view and dimensionality on ACL tear detection. ...
... However, there are areas for improvement in the studies mentioned above. First, some of the studies [13,17,19] were trained and evaluated using slices, which is inconsistent with the actual clinical application in medicine. The exact circumstance should be categorized as a person using all their slices. ...
... However, only a few analyses have classified all three tear grades in the human unit. For example, Awan et al. [17] achieved a good AUC and accuracy for ACL tear classification, but their method only used single slices as the unit of analysis. This limitation is not ideal for clinical applications. ...
The anterior cruciate ligament (ACL) is critical for controlling the motion of the knee joint, but it is prone to injury during sports activities and physical work. If left untreated, ACL injuries can lead to various pathologies such as meniscal damage and osteoarthritis. While previous studies have used deep learning to diagnose ACL tears, there has been a lack of standardization in human unit classification, leading to mismatches between their findings and actual clinical diagnoses. To address this, we perform a triple classification task based on various tear classes using an ordinal loss on the KneeMRI dataset. We utilize a channel correction module to address image distribution issues across multiple patients, along with a spatial attention module, and test its effectiveness with various backbone networks. Our results show that the modules are effective on various backbone networks, achieving an accuracy of 83.3% on ResNet-18, a 6.65% improvement compared to the baseline. Additionally, we carry out an ablation experiment to verify the effectiveness of the three modules and present our findings with figures and tables. Overall, our study demonstrates the potential of deep learning in diagnosing ACL tear and provides insights into improving the accuracy and standardization of such diagnoses.
... Sensor-based [16] and vision-based approaches [17] are the most common domains for developing automated sign language systems [18]. In the last decade, CNNs have made an extraordinary contribution to the vision-based sign language recognition approaches to recording one-handed and two-handed statistical or dynamical movement [4,5,[19][20][21][22]]. In the meantime, self-attention-based architectures have shown an excellent capability of capturing long-distance relationships and have become the de facto most popular approach in natural language processing (NLP) [23]. ...
Sign language recognition (SLR) is one of the crucial applications of the hand gesture recognition and computer vision research domain. There are many researchers who have been working to develop a hand gesture-based SLR application for English, Turkey, Arabic, and other sign languages. However, few studies have been conducted on Korean sign language classification because few KSL datasets are publicly available. In addition, the existing Korean sign language recognition work still faces challenges in being conducted efficiently because light illumination and background complexity are the major problems in this field. In the last decade, researchers successfully applied a vision-based transformer for recognizing sign language by extracting long-range dependency within the image. Moreover, there is a significant gap between the CNN and transformer in terms of the performance and efficiency of the model. In addition, we have not found a combination of CNN and transformer-based Korean sign language recognition models yet. To overcome the challenges, we proposed a convolution and transformer-based multi-branch network aiming to take advantage of the long-range dependencies computation of the transformer and local feature calculation of the CNN for sign language recognition. We extracted initial features with the grained model and then parallelly extracted features from the transformer and CNN. After concatenating the local and long-range dependencies features, a new classification module was applied for the classification. We evaluated the proposed model with a KSL benchmark dataset and our lab dataset, where our model achieved 89.00% accuracy for 77 label KSL dataset and 98.30% accuracy for the lab dataset. The higher performance proves that the proposed model can achieve a generalized property with considerably less computational cost.
... Mathematics 2023,11, 1027 ...
Medical image segmentation plays an indispensable role in the identification of articular cartilage, tibial and femoral bones from magnetic resonance imaging (MRI). There are various image segmentation strategies that can be used to identify the knee structures of interest. Among the most popular are the methods based on non-hierarchical clustering, including the algorithms K-means and fuzzy C-means (FCM). Although these algorithms have been used in many studies for regional image segmentation, they have two essential drawbacks that limit their performance and accuracy of segmentation. Firstly, they rely on a precise selection of initial centroids, which is usually conducted randomly, and secondly, these algorithms are sensitive enough to image noise and artifacts, which may deteriorate the segmentation performance. Based on such limitations, we propose, in this study, two novel alternative metaheuristic hybrid schemes: non-hierarchical clustering, driven by a genetic algorithm, and Particle Swarm Optimization (PSO) with fitness function, which utilizes Kapur’s entropy and statistical variance. The goal of these optimization elements is to find the optimal distribution of centroids for the knee MR image segmentation model. As a part of this study, we provide comprehensive testing of the robustness of these novel segmentation algorithms upon the image noise generators. This includes Gaussian, Speckle, and impulsive Salt and Pepper noise with dynamic noise to objectively report the robustness of the proposed segmentation strategies in contrast with conventional K-means and FCM. This study reveals practical applications of the proposed algorithms for articular cartilage extraction and the consequent classification performance of early osteoarthritis based on segmentation models and convolutional neural networks (CNN). Here, we provide a comparative analysis of GoogLeNet and ResNet 18 with various hyperparameter settings, where we achieved 99.92% accuracy for the best classification configuration for early cartilage loss recognition.
... Numerous studies (24,28,30) have delved into ACL injury classification tasks using DL frameworks. Although most of these studies (32,33) focused on lesion detection using binary classifiers, a small number of studies (34,35) explored multi-classification. Namiri et al. (18) developed a model based on 3D CNN and 2D CNN, achieving accurate multi-classification of ACL with accuracies of 0.89 and 0.92. However, their study only included 18 injuries. ...
Background
Anterior cruciate ligament (ACL) injuries are closely associated with knee osteoarthritis (OA). However, diagnosing ACL injuries based on knee magnetic resonance imaging (MRI) has been subjective and time-consuming for clinical doctors. Therefore, we aimed to devise a deep learning (DL) model leveraging MRI to enable a comprehensive and automated approach for the detection of ACL injuries.
Methods
A retrospective study was performed extracting data from the Osteoarthritis Initiative (OAI). A total of 1,589 knees (comprising 1,443 intact, 90 with partial tears, and 56 with full tears) were enrolled to construct the classification model. This one-stop detection pipeline was developed using a tailored YOLOv5m architecture and a ResNet-18 convolutional neural network (CNN) to facilitate tasks based on sagittal 2-dimensional (2D) intermediate-weighted fast spin-echo sequence at 3.0T. To ensure the reliability and robustness of the classification system, it was subjected to external validation across 3 distinct datasets. The accuracy, sensitivity, specificity, and the mean average precision (mAP) were utilized as the evaluation metric for the model performance by employing a 5-fold cross-validation approach. The radiologist’s interpretations were employed as the reference for conducting the evaluation.
Results
The localization model demonstrated an accuracy of 0.89 and a sensitivity of 0.93, achieving a mAP score of 0.96. The classification model demonstrated strong performance in detecting intact, partial tears, and full tears at the optimal threshold on the internal dataset, with sensitivities of 0.941, 0.833, and 0.929, specificities of 0.925, 0.947, and 0.991, and accuracies of 0.940, 0.941, and 0.989, respectively. In comparison, on a subset consisting of 171 randomly selected knees from the OAI, the radiologists demonstrated a sensitivity ranging between 0.660 and 1.000, specificity ranging between 0.691 and 1.000, and accuracy ranging between 0.689 and 1.000. On a subset consisting of 170 randomly selected knees from the Chinese dataset, the radiologists exhibited a sensitivity ranging between 0.711 and 0.948, specificity ranging between 0.768 and 0.977, and accuracy ranging between 0.683 and 0.917. After retraining, the model achieved sensitivities ranging between 0.630 and 0.961, specificities ranging between 0.860 and 0.961, and accuracies ranging between 0.832 and 0.951, respectively, on the external validation dataset.
Conclusions
The proposed model utilizing knee MRI showcases robust performance in the domains of ACL localization and classification.
... Several previous studies have focused on the application of deep learning for disease diagnoses in medical imaging; the applications include lung adenocarcinoma (Yu et al., 2021), abnormal pulmonary nodules (Sim et al., 2020), and breast masses (Caballo et al., 2020). In the case of diagnosing ACL injuries, previous work has been limited to the use of deep learning methods to detect the presence or absence of ACL injuries (Bien et al., 2018;Liu et al., 2019) and grading the hierarchical severity staging of ACL injuries on knee MR images (Namiri et al., 2020;Awan et al., 2021;Javed Awan et al., 2021). However, deep learning methods have yet to be applied to localizing the ACL rupture. ...
Purpose: To develop and evaluate a deep learning-based method to localize and classify anterior cruciate ligament (ACL) ruptures on knee MR images by using arthroscopy as the reference standard.
Methods: We proposed a fully automated ACL rupture localization system to localize and classify ACL ruptures. The classification of ACL ruptures was based on the projection coordinates of the ACL rupture point on the line connecting the center coordinates of the femoral and tibial footprints. The line was divided into three equal parts and the position of the projection coordinates indicated the classification of the ACL ruptures (femoral side, middle and tibial side). In total, 85 patients (mean age: 27; male: 56) who underwent ACL reconstruction surgery under arthroscopy were included. Three clinical readers evaluated the datasets separately and their diagnostic performances were compared with those of the model. The performance metrics included the accuracy, error rate, sensitivity, specificity, precision, and F1-score. A one-way ANOVA was used to evaluate the performance of the convolutional neural networks (CNNs) and clinical readers. Intraclass correlation coefficients (ICC) were used to assess interobserver agreement between the clinical readers.
Results: The accuracy of ACL localization was 3.77 ± 2.74 and 4.68 ± 3.92 (mm) for three-dimensional (3D) and two-dimensional (2D) CNNs, respectively. There was no significant difference in the ACL rupture location performance between the 3D and 2D CNNs or among the clinical readers (Accuracy, p < 0.01). The 3D CNNs performed best among the five evaluators in classifying the femoral side (sensitivity of 0.86 and specificity of 0.79), middle side (sensitivity of 0.71 and specificity of 0.84) and tibial side ACL rupture (sensitivity of 0.71 and specificity of 0.99), and the overall accuracy for sides classifying of ACL rupture achieved 0.79.
Conclusion: The proposed deep learning-based model achieved high diagnostic performances in locating and classifying ACL fractures on knee MR images.
... The ROC curve is generated by varying this probability threshold and computing the corresponding true positive rate (TPR) and false positive rate (FPR). The x-axis in ROC represents the FPR, and the y-axis represents the TPR [53,54]. The area under the curve (AUC) is a critical calculation metric for assessing the performance of any classification model. ...
With recent advancements in the classification methods of various domains, deep learning has shown remarkable results over traditional neural networks. A compact convolutional neural network (CNN) model with reduced computational complexity that performs equally well compared to the pretrained ResNet-101 model was developed. This three-layer CNN model was developed for plant leaf classification in this work. The classification of disease in tomato plant leaf images of the healthy and disease classes from the PlantVillage (PV) database is discussed in this work. Further, it supports validating the models with the images taken at “Krishi Vigyan Kendra Narayangaon (KVKN),” Pune, India. The disease categories were chosen based on their prevalence in Indian states. The proposed approach presents a performance improvement concerning other state-of-the-art methods; it achieved classification accuracies of 99.13%, 99.51%, and 99.40% with N1, N2, and N3 models, respectively, on the PV dataset. Experimental results demonstrate the validity of the proposed approach under complex background conditions. For the images captured at KVKN for predicting tomato plant leaf disease, the validation accuracy was 100% for the N1 model, 98.44% for the N2 model, and 96% for the N3 model. The training time for the developed N2 model was reduced by 89% compared to the ResNet-101 model. The models developed are smaller, more efficient, and less time-complex. The performance of the developed model will help us to take a significant step towards managing the infected plants. This will help farmers and contribute to sustainable agriculture.
