Image Based Estimation of Food Colories Based on Machine Learning

The evolution and improvements of deep learning are being used to tackle any research obstacles that could be converted into classification problems in all spheres of life. Each Deep convolutional neural network (DCNN) design's output is determined by the depth and value of the hyperparameters, which explains why so many of them have been proposed. These DCNN architectures must be created entirely from scratch, and they can only be used for the applications for which they were intended. Transfer learning may be used to modify these pre-trained networks so they are more appropriate for particular purposes. This article aims to evaluate the empirical performance of the applicability of pre-trained DCNN models to identify human face presentation threats (FPAD). Human FPAD is one of the most significant and crucial areas of research right now because of the introduction of ambient computing, which necessitates contact-free identification of persons with the help of their biometric traits. Six pre-trained DCNN models are taken into account for an experimental evaluation in human FPAD alias VGG19, VGG16, DensNet121, MobileNet, Xception, and InceptionV3. The investigation makes use of the NUAA and Replay-Attack benchmark FPAD datasets. Thepade's sorted block truncation coding (SBTC) 10-ary features are merged with deep learning features produced from the finest performing finetuned DCNNs to enhance the FPAD capabilities of analyzed machine learning (ML) classifiers. The integration of features of Thepade's SBTC 10-ary and DCNN has considerably increased the FPAD accuracy of ML classifiers with slightly more computations of feature extraction.

Data Analytic interpretation of plant species is not widely explored. The image variety of plant species can be investigated with the help of machine learning and deep learning techniques. Machine learning and deep learning make the plant species investigation automated and almost accurate. In this paper, we have proposed a novel native and invasive plant species classification technique. We use the pre-trained deep convolution neural networks (transfer learning) to extract features from plant species and classify them into Native and Invasive. Here, state-of-the-art deep convolution neural network models like InceptionV3, MobileNetV2, ResNetV2, VGG16, and Xception have been explored to extract the feature vectors from images. Furthermore, the features of the images have been used for image classification using the Random Forest algorithm. The performance of the proposed system is verified on the proposed dataset (Native-Invasive) with aerial view images of native and invasive species. Results were obtained using cross-validation techniques. Our experiments demonstrate the potential of our proposed method for achieving excellent performance with a 95% accuracy using Xception with data augmentation, hyper-parameter optimization, and the Random Forest classification technique. The aerial images were captured and labeled by us which is very novel to our experimental setup. After using deep learning models we got promising results. The dataset used to train the plant species classifier will be made available on request.KeywordsFeature extractionTransfer learningRandom ForestDeep learningImage classificationNative plant speciesInvasive plant species

Deep learning-based computed tomography applied to the diagnosis of rib fractures

Machine learning has been densely used in most computer-aided medical diagnosis systems. These systems not only supported the physician’s decision but also accelerate the necessitated procedures. Electroencephalography (EEG) is an essential device for measuring the brain’s electrical activities. EEG is used to detect a series of brain disorders such as epilepsy, dementia, Parkinson’s disease, and Schizophrenia (SZ). In this work, a novel method for detecting SZ using EEG recordings is suggested. Initially, the presented technique breaks down each channel of the input EEG recordings into EEG rhythms. The wavelet transform is employed to achieve this. The 1D local binary pattern (LBP) is then used to code the acquired rhythm signals. Each row of the input picture is formed by concatenating the uniform histograms of the 1D LBP coded beats. The rows of the images are formed from the channels of the input EEG signal, while the columns of the images are constructed from the rhythms. Extreme learning machines (ELM) based autoencoders (AE) are utilized at a data augmentation step. After data augmentation, the SZ and healthy cases are classified using well-known deep transfer learning. Deep transfer learning employs a variety of pre-trained deep Convolutional Neural Network (CNN) models. Various performance assessment indicators are used to evaluate the produced outcomes. An EEG dataset that Lomonosov Moscow State University released is used in experiments, and a 97.7% accuracy score is obtained. The obtained results are also compared with several recently published methods. The comparisons show that the proposed method outperforms the compared methods.

Assess the efficacy of deep convolutional neural networks (DCNNs) in detection of critical enteric feeding tube malpositions on radiographs. 5475 de-identified HIPAA compliant frontal view chest and abdominal radiographs were obtained, consisting of 174 x-rays of bronchial insertions and 5301 non-critical radiographs, including normal course, normal chest, and normal abdominal x-rays. The ground-truth classification for enteric feeding tube placement was performed by two board-certified radiologists. Untrained and pretrained deep convolutional neural network models for Inception V3, ResNet50, and DenseNet 121 were each employed. The radiographs were fed into each deep convolutional neural network, which included untrained and pretrained models. The Tensorflow framework was used for Inception V3, ResNet50, and DenseNet. Images were split into training (4745), validation (630), and test (100). Both real-time and preprocessing image augmentation strategies were performed. Receiver operating characteristic (ROC) and area under the curve (AUC) on the test data were used to assess the models. Statistical differences among the AUCs were obtained. p < 0.05 was considered statistically significant. The pretrained Inception V3, which had an AUC of 0.87 (95 CI; 0.80–0.94), performed statistically significantly better (p < .001) than the untrained Inception V3, with an AUC of 0.60 (95 CI; 0.52–0.68). The pretrained Inception V3 also had the highest AUC overall, as compared with ResNet50 and DenseNet121, with AUC values ranging from 0.82 to 0.85. Each pretrained network outperformed its untrained counterpart. (p < 0.05). Deep learning demonstrates promise in differentiating critical vs. non-critical placement with an AUC of 0.87. Pretrained networks outperformed untrained ones in all cases. DCNNs may allow for more rapid identification and communication of critical feeding tube malpositions.

Female accounts for approximately 50% of the total population worldwide and many of them had breast cancer. Computer-aided diagnosis frameworks could reduce the number of needless biopsies and the workload of radiologists. This research aims to detect benign and malignant tumors automatically using breast ultrasound (BUS) images. Accordingly, two pretrained deep convolutional neural network (CNN) models were employed for transfer learning using BUS images like AlexNet and DenseNet201. A total of 697 BUS images containing benign and malignant tumors are preprocessed and performed classification tasks using the transfer learning-based CNN models. The classification accuracy of the benign and malignant tasks is completed and achieved 92.8% accuracy using the DensNet201 model. The results thus achieved compared in state of the art using benchmark data set and concluded proposed model outperforms in accuracy from first stage breast tumor diagnosis. Finally, the proposed model could help radiologists diagnose benign and malignant tumors swiftly by screening suspected patients.

In recent years, numerous techniques have been proposed for human activity recognition (HAR) from images and videos. These techniques can be divided into two major categories: handcrafted and deep learning. Deep Learning-based models have produced remarkable results for HAR. However, these models have several shortcomings, such as the requirement for a massive amount of training data, lack of transparency, offline nature, and poor interpretability of their internal parameters. In this paper, a new approach for HAR is proposed, which consists of an interpretable, self-evolving, and self-organizing set of 0-order If...THEN rules. This approach is entirely data-driven, and non-parametric; thus, prototypes are identified automatically during the training process. To demonstrate the effectiveness of the proposed method, a set of high-level features is obtained using a pre-trained deep convolution neural network model, and a recently introduced deep rule-based classifier is applied for classification. Experiments are performed on a challenging benchmark dataset UCF50; results confirmed that the proposed approach outperforms state-of-the-art methods. In addition to this, an ablation study is conducted to demonstrate the efficacy of the proposed approach by comparing the performance of our DRB classifier with four state-of-the-art classifiers. This analysis revealed that the DRB classifier could perform better than state-of-the-art classifiers, even with limited training samples.

The aim of the present study was to explore the diagnostic value of a deep convolutional neural network (DCNN) model for the diagnosis of pulmonary nodules in adolescent and young adult patients with osteosarcoma. For the present study, 675 chest CT images were retrospectively collected from 109 patients with clinically confirmed osteosarcoma who underwent chest CT examination at Hangzhou Third People's Hospital (Hangzhou, China) from March 2011 to February 2022. CT images were then evaluated using the DCNN and manual models. Subsequently, pulmonary nodules of osteosarcoma were divided into calcified nodules, solid nodules, partially solid nodules and ground glass nodules using the DCNN model. Those patients with osteosarcoma who were diagnosed and treated were followed up to observe dynamic changes in the pulmonary nodules. A total of 3,087 nodules were detected, while 278 nodules were missed compared with those determined using the reference standard given by the consensus of three Experienced radiologists., which was analyzed by two diagnostic radiologists. In the manual model group, 2,442 nodules were detected, while 657 nodules were missed. The DCNN model showed significantly higher sensitivity and specificity compared with the manual model (sensitivity, 0.923 vs. 0.908; specificity, 0.552 vs. 0.351; P<0.05). In addition, the DCNN model yielded an area under the curve (AUC) value of 0.795 [95% confidence interval (CI), 0.743-0.846], outperforming that of the manual model (AUC, 0.687; 95% CI, 0.629-0.732; P<0.05). The film reading time of the DCNN model was also significantly shorter compared with that of the manual model [mean ± standard deviation (SD); 173.25±24.10 vs. 328.32±22.72 sec; P<0.05)]. The AUC of calcified nodules, solid nodules, partially solid nodules and ground glass nodules was calculated to be 0.766, 0.771, 0.761 and 0.796, respectively, using the DCNN model. Using this model, the majority of the pulmonary nodules were detected in patients with osteosarcoma at the initial diagnosis (69/109, 62.3%), and the majority of these were found with multiple pulmonary nodules instead of a single nodule (71/109, 65.1% vs. 38/109, 34.9%). These data suggest that, compared with the manual model, the DCNN model proved to be beneficial for the detection of pulmonary nodules in adolescent and young adult patients with osteosarcoma, which may reduce the time of artificial radiograph reading. In conclusion, the proposed DCNN model, developed using data from 675 chest CT images retrospectively collected from 109 patients with clinically confirmed osteosarcoma, may be used as an effective tool to evaluate pulmonary nodules in patients with osteosarcoma.

This study focuses on the detection of COVID-19 using chest X-ray images. With the rapid global spread of the highly contagious disease, early and accurate detection is crucial to prevent further transmission. We propose a multi-view approach that leverages different deep-learning methods to detect COVID-19 based on chest X-ray images. The framework presented in this study aims to capture both complementary and correlative information from multiple views. By using pre-trained deep convolutional neural network (CNN) models such as ResNet50 and VGG16 to extract deep features from the X-ray images. Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) algorithms are then employed to classify the X-ray images based on the learned feature representations. The single-view models using VGG16 and ResNet50 with SVM achieved accuracy scores of 97% and 98% respectively. Similarly, using KNN, the single-view models achieved accuracy scores of 89% and 95%. Recognizing the potential of multi-view learning to improve generalization performance. We utilized early fusion to integrate the outputs of the pre-trained models and fed them into SVM and KNN classifiers. The multi-view model achieved accuracy scores of 98% and 92% respectively. The experimental results demonstrate that the multi-view deep learning methods outperformed the single-view deep models in detecting COVID-19 from chest X-ray images. The findings of this study have practical implications as they can assist experts in early diagnosis of COVID-19 cases, highlighting the effectiveness of the proposed methods.

Background Incidence rate of thyroid cancer is steadily increasing due to overdiagnosis and overtreatment. Thyroid ultrasound is commonly used to diagnose thyroid cancer. The aim of this study is to examine the accuracy of using deep convolutional neural network (DCNN) models to improve diagnosis of thyroid cancer by analyzing sonographic imaging data from clinical thyroid ultrasound. Methods A total of 131,731 sonographic images from 17,627 thyroid cancer patients and 180,668 sonographic images from 25,325 controls used as training set were obtained from Tianjin Cancer Hospital. Images from anatomical sites that did not have cancer according to location sign on the image were not included. All thyroid cancer patients and 13·2% of controls (51,255 images) were confirmed by pathological reports. DCNN is a specific type of neural network optimized for image recognition. We trained two DCNN models on the training set and subsequently evaluated the performance on one independent internal (Tianjin, 1,118 individuals) and two external (Jilin,154 individuals; Weihai, 1,420 individuals) validation sets. Individuals in the validation sets all have pathological examinations. We compared the specificity/sensitivity of DCNN models with the performance of six thyroid ultrasound radiologists on these three validation sets. Findings DCNN model achieved high performance in identifying thyroid cancer patients versus six experience radiologists: for Tianjin validation set, sensitivity was 92·2% versus 96·9% (95% CI 89·7% - 94·3% vs. 93·9% - 98·6%; p = 0·003), and specificity was 85·6% versus 59·4% (95% CI 82·4% - 88·4% vs. 53% - 65·6%; p &amp;lt; 0·0001); for Jilin validation set, sensitivity was 84·3% versus 92·9% (95% CI 73·6% - 91·9% vs. 84·1% - 97·6%; p = 0·05), and specificity was 86·9% versus 57·1% (95% CI 77·8% - 93·3% vs. 45·9% - 67·9%; p &amp;lt; 0·0001); for Weihai validation set, sensitivity was 84·5% versus 89% (95% CI 81·2% - 87·4% vs. 81·9% - 94%; p = 0·2), and specificity was 87·5% versus 68·6% (95% CI 85·1% - 89·6% vs. 60·7% - 75·8%; p &amp;lt; 0·0001). Interpretation DCNN models exhibited high accuracy, sensitivity, and specificity in identifying thyroid cancer patients at levels comparable to or higher than six experienced radiologists. Conferred by the high specificity of DCNN models, the rate of overdiagnosis and overtreatment of patients with thyroid cancer is expected to decrease. This supports future application of the deep learning models to clinical practice for thyroid cancer diagnosis. However, further validation of these DCNN models in prospective clinical trials is warranted. Funding The Program for Changjiang Scholars and Innovative Research Team in University in China (IRT_14R40), National Natural Science Foundation of China (31801117). Citation Format: Xiangchun Li, Sheng Zhang, Qiang Zhang, Xi Wei, Yi Pan, Jing Zhao, Xiaojie Xin, Xiaoqing Wang, Fan Yang, Jianxin Li, Meng Yang, Qinghua Wang, Xiangqian Zheng, Yanhui Zhao, Lun Zhang, Xudong Wang, Zhimin Zheng, Christopher T. Whitlow, Metin N. Gurcan, Boris Pasche, Ming Gao, Wei Zhang, Kexin Chen. Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images from clinical ultrasound exams [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1394.

The human tongue has been long believed to be a window to provide important insights into a patient's health in medicine. The present study introduced a novel approach to predict patient age, gender, and weight inferences based on tongue images using pretrained deep convolutional neural networks (CNNs). Our results demonstrated that the deep CNN models (e.g., ResNeXt) trained on dorsal tongue images produced excellent results for age prediction with a Pearson correlation coefficient of 0.71 and a mean absolute error (MAE) of 8.5 years. We also obtained an excellent classification of gender, with a mean accuracy of 80% and an AUC (area under the receiver operating characteristic curve) of 88%. ResNeXt model also obtained a moderate level of accuracy for weight prediction, with a Pearson correlation coefficient of 0.39 and a MAE of 9.06 kg. These findings support our hypothesis that the human tongue contains crucial information about a patient. This study demonstrated the feasibility of using the pretrained deep CNNs along with a large tongue image dataset to develop computational models to predict patient medical conditions for noninvasive, convenient, and inexpensive patient health monitoring and diagnosis.

Advances in satellite imagery presents unprecedented opportunities for understanding natural and social phenomena at global and regional scales. Although the field of satellite remote sensing has evaluated imperative questions to human and environmental sustainability, scaling those techniques to very high spatial resolutions at regional scales remains a challenge. Satellite imagery is now more accessible with greater spatial, spectral and temporal resolution creating a data bottleneck in identifying the content of images. Because satellite images are unlabeled, unsupervised methods allow us to organize images into coherent groups or clusters. However, the performance of unsupervised methods, like all other machine learning methods, depends on features. Recent studies using features from pre-trained networks have shown promise for learning in new datasets. This suggests that features from pre-trained networks can be used for learning in temporally and spatially dynamic data sources such as satellite imagery. It is not clear, however, which features from which layer and network architecture should be used for learning new tasks. In this paper, we present an approach to evaluate the transferability of features from pre-trained Deep Convolutional Neural Networks for satellite imagery. We explore and evaluate different features and feature combinations extracted from various deep network architectures, and systematically evaluate over 2,000 network-layer combinations. In addition, we test the transferability of our engineered features and learned features from an unlabeled dataset to a different labeled dataset. Our feature engineering and learning are done on the unlabeled Draper Satellite Chronology dataset, and we test on the labeled UC Merced Land dataset to achieve near state-of-the-art classification results. These results suggest that even without any or minimal training, these networks can generalize well to other datasets. This method could be useful in the task of clustering unlabeled images and other unsupervised machine learning tasks.

BackgroundSurgical process modeling automatically identifies surgical phases, and further improvement in recognition accuracy is expected with deep learning. Surgical tool or time series information has been used to improve the recognition accuracy of a model. However, it is difficult to collect this information continuously intraoperatively. The present study aimed to develop a deep convolution neural network (CNN) model that correctly identifies the surgical phase during laparoscopic cholecystectomy (LC).MethodsWe divided LC into six surgical phases (P1–P6) and one redundant phase (P0). We prepared 115 LC videos and converted them to image frames at 3 fps. Three experienced doctors labeled the surgical phases in all image frames. Our deep CNN model was trained with 106 of the 115 annotation datasets and was evaluated with the remaining datasets. By depending on both the prediction probability and frequency for a certain period, we aimed for highly accurate surgical phase recognition in the operation room.ResultsNine full LC videos were converted into image frames and were fed to our deep CNN model. The average accuracy, precision, and recall were 0.970, 0.855, and 0.863, respectively.ConclusionThe deep learning CNN model in this study successfully identified both the six surgical phases and the redundant phase, P0, which may increase the versatility of the surgical process recognition model for clinical use. We believe that this model can be used in artificial intelligence for medical devices. The degree of recognition accuracy is expected to improve with developments in advanced deep learning algorithms.

Diagnosing burns in humans has become critical, as early identification can save lives. The manual process of burn diagnosis is time-consuming and complex, even for experienced doctors. Machine learning (ML) and deep convolutional neural network (CNN) models have emerged as the standard for medical image diagnosis. The ML-based approach typically requires handcrafted features for training, which may result in suboptimal performance. Conversely, DL-based methods automatically extract features, but designing a robust model is challenging. Additionally, shallow DL methods lack long-range feature dependency, decreasing efficiency in various applications. We implemented several deep CNN models, ResNeXt, VGG16, and AlexNet, for human burn diagnosis. The results obtained from these models were found to be less reliable since shallow deep CNN models need improved attention modules to preserve the feature dependencies. Therefore, in the proposed study, the feature map is divided into several categories, and the channel dependencies between any two channel mappings within a given class are highlighted. A spatial attention map is built by considering the links between features and their locations. Our attention-based model BuRnGANeXt50 kernel and convolutional layers are also optimized for human burn diagnosis. The earlier study classified the burn based on depth of graft and non-graft. We first classified the burn based on the degree. Subsequently, it is classified into graft and non-graft. Furthermore, the proposed model performance is evaluated on Burns_BIP_US_database. The sensitivity of the BuRnGANeXt50 is 97.22% and 99.14%, respectively, for classifying burns based on degree and depth. This model may be used for quick screening of burn patients and can be executed in the cloud or on a local machine. The code of the proposed method can be accessed at https://github.com/dhirujis02/Journal.git for the sake of reproducibility.

Because of the success of deep learning in multiple sectors, it is gaining unquestionable acceptance in the therapeutic field also. The key challenge is determining how to construct a deep CNN model with an inadequate amount of training data. One key question is whether transfer learning and fine-tuning can be employed in biomedical image analysis to lessen the load of manual data labelling while still producing a complete deep representation for the task. In this paper, we compare the performance of transfer learning and machine learning for nuclei categorization to answer this question statistically. The used machine learning approaches are Decision Tree, Support Vector Machine, Quadratic Discriminant Analysis, K Neighbors, Ada-Boost, Gaussian Naïve Bayes, Logistic Regression, Extra Trees, Random Forest, Histogram Gradient Boosting. This paper shows how to recognize nuclei from microscope pictures using a deep learning model and an image processing-based processing flow. Convolutional neural networks and Inception Resnet V2 deep networks have been used to generate better results. The data augmentation is also done to address the paucity of data.