A Novel Multi-Feature Extraction Approach for Plant Disease Detection Using Transfer Learning and Handcrafted Methods

Memory forensics is an essential cybersecurity tool that comprehensively examines volatile memory to detect the malicious activity of fileless malware that can bypass disk analysis. Image-based detection techniques provide a promising solution by visualizing memory data into images to be used and analyzed by image processing tools and machine learning methods. However, the effectiveness of image-based data for detection and classification requires high computational efforts. This paper investigates the efficacy of texture-based methods in detecting and classifying memory-resident or fileless malware using different image resolutions, identifying the best feature descriptors, classifiers, and resolutions that accurately classify malware into specific families and differentiate them from benign software. Moreover, this paper uses both local and global descriptors, where local descriptors include Oriented FAST and Rotated BRIEF (ORB), Scale-Invariant Feature Transform (SIFT), and Histogram of Oriented Gradients (HOG) and global descriptors include Discrete Wavelet Transform (DWT), GIST, and Gray Level Co-occurrence Matrix (GLCM). The results indicate that as image resolution increases, most feature descriptors yield more discriminative features but require higher computational efforts in terms of time and processing resources. To address this challenge, this paper proposes a novel approach that integrates Local Interpretable Model-agnostic Explanations (LIME) with deep learning models to automatically identify and crop the most important regions of memory images. The LIME’s ROI was extracted based on ResNet50 and MobileNet models’ predictions separately, the images were resized to 128 × 128, and the sampling process was performed dynamically to speed up LIME computation. The ROIs of the images are cropped to new images with sizes of (100 × 100) in two stages: the coarse stage and the fine stage. The two generated LIME-based cropped images using ResNet50 and MobileNet are fed to the lightweight neural network to evaluate the effectiveness of the LIME-based identified regions. The results demonstrate that the LIME-based MobileNet model’s prediction improves the efficiency of the model by preserving important features with a classification accuracy of 85% on multi-class classification.

Bone deterioration from osteoporosis creates fractures that primarily affect females who have reached menopause and older adults. Early detection of osteoporosis requires affordable methods because current diagnostic systems are both expensive and challenging to use. The current application of deep learning models for bone radiography analysis faces three major limitations: shallow architecture, insufficient dataset adaptation, and inadequate feature extraction methods. Talk-based machine learning techniques rely heavily on human-generated feature construction; however, they fail to recognize the delicate image patterns that are present in medical contexts. VGG-16 provides strong power, but businesses must invest in advanced and expensive hardware platforms while maintaining challenges with data quantity. The detection of osteoporosis in knee X-rays through deep learning and the ResNet-50 model using transfer learning remains our primary proposal for solving existing issues. An overview of 372 X-ray images with verified medical information was constructed for T-score ratings determined using a quantitative ultrasound system. Following fine-tuning of our model based on ResNet-50, we achieved 90% accuracy, which exceeded all alternative models tested, including VGG-16 with 88% accuracy using fine-tuning, 80% without fine-tuning, a 3-layer CNN at 66%, ResNet-18 at 79%, and non-fine-tuned ResNet-50 at 83%. The clinical value of the ResNet-50 model stems from its high accuracy, strong sensitivity, and specificity rates. The detection reliability of osteoporosis by the deep transfer learning CNN ResNet-50 has been proven through these findings, which provide healthcare practitioners with an effective diagnostic tool for early detection and prevention of fractures. Our research indicates that deep learning using transfer learning strategies produces a crucial enhancement in medical imaging systems, leading to better patient results.

Real-time quality monitoring using molten pool images is a critical focus in researching high-quality, intelligent automated welding. To address interference problems in molten pool images under complex welding scenarios (e.g., reflected laser spots from spatter misclassified as porosity defects) and the limited interpretability of deep learning models, this paper proposes a multi-granularity spatiotemporal representation learning algorithm based on the hybrid enhancement of handcrafted and deep learning features. A MobileNetV2 backbone network integrated with a Temporal Shift Module (TSM) is designed to progressively capture the short-term dynamic features of the molten pool and integrate temporal information across both low-level and high-level features. A multi-granularity attention-based feature aggregation module is developed to select key interference-free frames using cross-frame attention, generate multi-granularity features via grouped pooling, and apply the Convolutional Block Attention Module (CBAM) at each granularity level. Finally, these multi-granularity spatiotemporal features are adaptively fused. Meanwhile, an independent branch utilizes the Histogram of Oriented Gradient (HOG) and Scale-Invariant Feature Transform (SIFT) features to extract long-term spatial structural information from historical edge images, enhancing the model's interpretability. The proposed method achieves an accuracy of 99.187% on a self-constructed dataset. Additionally, it attains a real-time inference speed of 20.983 ms per sample on a hardware platform equipped with an Intel i9-12900H CPU and an RTX 3060 GPU, thus effectively balancing accuracy, speed, and interpretability.

Uncertainty quantification-based robust deep learning for building energy systems considering distribution shift problem

BackgroundThe subjective assessment of Breast Imaging Reporting and Data System (BI-RADS) 4A lesions leads to a high number of unnecessary biopsies, highlighting the need for more objective and accurate diagnostic tools. This study aimed to construct and validate a novel multimodal framework that integrates deep learning (DL), radiomics, and clinical and breast ultrasound (US) features to distinguish between benign and malignant breast lesions classified as BI-RADS 4A.MethodsA total of 935 patients with pathologically confirmed BI-RADS 4A lesions were retrospectively enrolled and randomly divided into a training cohort (n=654) and an internal validation cohort (n=281). An additional 488 patients were enrolled as the external validation cohort. DL and radiomics models were developed with the light gradient boosting machine (LightGBM) algorithm, a machine learning technique known for its efficiency in handling high-dimensional data. Univariate and multivariate logistic regression (LR) analyses identified significant clinical and US features. After handcrafted radiomics features, DL features, and clinical/breast US features were combined, dimensionality reduction and feature selection were performed. The integrated model was developed via LightGBM, and the SHapley Additive Explanations (SHAP) approach was applied to rank feature contributions.ResultsThe integrated model achieved robust discrimination between benign and malignant BI-RADS 4A lesions, with an area under the curve (AUC) of 0.938, 0.870, and 0.861 for the training cohort, internal validation cohort, and external validation cohort, respectively. Furthermore, in the external validation cohort, the integrated model significantly outperformed the handcrafted radiomics model (AUC =0.719; P<0.001) and the DL model (AUC =0.763; P=0.011). Decision curve analysis confirmed that the integrated model was the most clinically useful across the three cohorts.ConclusionsThe integrated model demonstrated high accuracy in differentiating between benign and malignant BI-RADS 4A lesions, exhibiting the ability to significantly reduce unnecessary biopsies while maintaining diagnostic accuracy.

Power transmission line icing (PTLI) poses significant threats to the reliability and safety of electrical power systems, particularly in cold regions. Accumulation of ice on power lines can lead to severe consequences, such as line breaks, tower collapses, and widespread power outages, resulting in economic losses and infrastructure damage. This study proposes an enhanced image processing pipeline to accurately detect and match key points in PTLI images for 3D monitoring of ice thickness using binocular vision. The pipeline integrates established techniques such as multiscale retinex (MSR), oriented FAST and rotated BRIEF (ORB) and scale-invariant feature transform (SIFT) algorithms, further refined with m-estimator sample consensus (MAGSAC)-based random sampling consensus (RANSAC) optimization. The image processing steps include automatic cropping, image enhancement, feature detection, and robust key point matching, all designed to operate in challenging environments with poor lighting and noise. Experiments demonstrate that the proposed method significantly improves key point matching accuracy and computational efficiency, reducing processing time to make it suitable for real-time applications. The effectiveness of the pipeline is validated through 3D ice thickness measurements, with results showing high precision and low error rates, making it a valuable tool for monitoring power transmission lines in harsh conditions.

COVID-19 is highly infectious and causes acute respiratory disease. Machine learning (ML) and deep learning (DL) models are vital in detecting disease from computerized chest tomography (CT) scans. The DL models outperformed the ML models. For COVID-19 detection from CT scan images, DL models are used as end-to-end models. Thus, the performance of the model is evaluated for the quality of the extracted feature and classification accuracy. There are four contributions included in this work. First, this research is motivated by studying the quality of the extracted feature from the DL by feeding these extracted to an ML model. In other words, we proposed comparing the end-to-end DL model performance against the approach of using DL for feature extraction and ML for the classification of COVID-19 CT scan images. Second, we proposed studying the effect of fusing extracted features from image descriptors, e.g., Scale-Invariant Feature Transform (SIFT), with extracted features from DL models. Third, we proposed a new Convolutional Neural Network (CNN) to be trained from scratch and then compared to the deep transfer learning on the same classification problem. Finally, we studied the performance gap between classic ML models against ensemble learning models. The proposed framework is evaluated using a CT dataset, where the obtained results are evaluated using five different metrics The obtained results revealed that using the proposed CNN model is better than using the well-known DL model for the purpose of feature extraction. Moreover, using a DL model for feature extraction and an ML model for the classification task achieved better results in comparison to using an end-to-end DL model for detecting COVID-19 CT scan images. Of note, the accuracy rate of the former method improved by using ensemble learning models instead of the classic ML models. The proposed method achieved the best accuracy rate of 99.39%.

Comparison of deep learning models and a typical process-based model in glacio-hydrology simulation

Object recognition is a key research area in the field of image processing and computer vision, which recognizes the object in an image and provides a proper label. In the paper, three popular feature descriptor algorithms that are Scale Invariant Feature Transform (SIFT), Speeded Up Robust Feature (SURF) and Oriented Fast and Rotated BRIEF (ORB) are used for experimental work of an object recognition system. A comparison among these three descriptors is exhibited in the paper by determining them individually and with different combinations of these three methodologies. The amount of the features extracted using these feature extraction methods are further reduced using a feature selection (k-means clustering) and a dimensionality reduction method (Locality Preserving Projection). Various classifiers i.e. K-Nearest Neighbor, Naive Bayes, Decision Tree, and Random Forest are used to classify objects based on their similarity. The focus of this article is to present a study of the performance comparison among these three feature extraction methods, particularly when their combination derives in recognizing the object more efficiently. In this paper, the authors have presented a comparative analysis view among various feature descriptors algorithms and classification models for 2D object recognition. The Caltech-101 public dataset is considered in this article for experimental work. The experiment reveals that a hybridization of SIFT, SURF and ORB method with Random Forest classification model accomplishes the best results as compared to other state-of-the-art work. The comparative analysis has been presented in terms of recognition accuracy, True Positive Rate (TPR), False Positive Rate (FPR), and Area Under Curve (AUC) parameters.

Detecting programmed death ligand 1 (PD-L1) expression based on immunohistochemical (IHC) staining is an important guide for the treatment of lung cancer with immune checkpoint inhibitors. However, this method has problems such as high staining costs, tumor heterogeneity, and subjective differences among pathologists. Therefore, the application of deep learning models to segment and quantitatively predict PD-L1 expression in digital sections of Hematoxylin and eosin (H&E) stained lung squamous cell carcinoma is of great significance. We constructed a dataset comprising H&E-stained digital sections of lung squamous cell carcinoma and used a Transformer Unet (TransUnet) deep learning network with an encoder-decoder design to segment PD-L1 negative and positive regions and quantitatively predict the tumor cell positive score (TPS). The results showed that the dice similarity coefficient (DSC) and intersection overunion (IoU) of deep learning for PD-L1 expression segmentation of H&E-stained digital slides of lung squamous cell carcinoma were 80 and 72%, respectively, which were better than the other seven cutting-edge segmentation models. The root mean square error (RMSE) of quantitative prediction TPS was 26.8, and the intra-group correlation coefficients with the gold standard was 0.92 (95% CI: 0.90-0.93), which was better than the consistency between the results of five pathologists and the gold standard. The deep learning model is capable of segmenting and quantitatively predicting PD-L1 expression in H&E-stained digital sections of lung squamous cell carcinoma, which has significant implications for the application and guidance of immune checkpoint inhibitor treatments. And the link to the code is https://github.com/Baron-Huang/PD-L1-prediction-via-HE-image.

Chest X-ray (CXR) imaging is a popular, inexpensive, and non-invasive diagnostic method for examining lung diseases. However, even radiologists often have lower accuracy with CXR data than more expensive methods like CT or MRI, due to lower precision of CXR images. Here, we present a new hybrid deep learning model of image features obtained through a ResNet50 model and clinical data taken through a Random Forest (RF) classifier. The image processing pipeline involved scaling, background/foreground noise removal, feature augmentation, and data cleaning for the clinical data, to present robust input to both deep learning model and RF classifier. The deep learning image features obtained from the ResNet50 model were fused with clinical data obtained from the RF classifier to complete a final classification. Our results compared model levels of accuracy, precision, recall, and F1-score against modern CNN and RNN baselines. Our experiments on a curated and classified CXR data set demonstrated that our ResNet50+RF hybrid model achieved the best performance, with 92% accuracy, 95.9% precision, 96% recall, and a 96% F1-score; the CNN model achieved an accuracy of 91%. RNN performance was lower overall, with an accuracy of 89%. These conclusions suggest the potential of extracting deep features from an image, while also performing ensemble learning on clinical data to improve classification accuracy with a scalable model for real-world clinical decision support systems.

In various applications, such as computer vision, medical imaging and robotics, three-dimensional (3D) image registration is a significant step. It enables the alignment of various datasets into a single coordinate system, consequently providing a consistent perspective that allows further analysis. By precisely aligning images we can compare, analyze, and combine data collected in different situations. This paper presents a novel approach for 3D or z-stack microscopy and medical image registration, utilizing a combination of conventional and deep learning techniques for feature extraction and adaptive likelihood-based methods for outlier detection. The proposed method uses the Scale-invariant Feature Transform (SIFT) and the Residual Network (ResNet50) deep neural learning network to extract effective features and obtain precise and exhaustive representations of image contents. The registration approach also employs the adaptive Maximum Likelihood Estimation SAmple Consensus (MLESAC) method that optimizes outlier detection and increases noise and distortion resistance to improve the efficacy of these combined extracted features. This integrated approach demonstrates robustness, flexibility, and adaptability across a variety of imaging modalities, enabling the registration of complex images with higher precision. Experimental results show that the proposed algorithm outperforms state-of-the-art image registration methods, including conventional SIFT, SIFT with Random Sample Consensus (RANSAC), and Oriented FAST and Rotated BRIEF (ORB) methods, as well as registration software packages such as bUnwrapJ and TurboReg, in terms of Mutual Information (MI), Phase Congruency-Based (PCB) metrics, and Gradiant-based metrics (GBM), using 3D MRI and 3D serial sections of multiplex microscopy images.

Alcoholism is a serious disorder that poses a problem for our society. Detection of alcoholism has no widely accepted standard tests or procedures. An electroencephalogram (EEG) is a method to measure the brain's electrical activity and can be used to detect alcoholism. These EEG signals are complex and multichannel and hence can be hard to interpret manually. Several previous works have tried to classify a subject as alcoholic or non-alcoholic based on these EEG signals. These works have mostly used machine learning or statistical techniques, along with handcrafted features. Not much work is done on the application of deep learning models for the detection of alcoholism using EEG signals. This paper proposes a novel deep learning architecture that uses a combination of Fast Fourier Transform (FFT), Convolution Neural Network (CNN), Long Short Term Memory (LSTM), and recently proposed Attention mechanism for extracting Spatio-temporal features from multichannel EEG signals. This proposed architecture can classify a subject as alcoholic or control with high accuracy by analyzing EEG signals based on Spatio-temporal features and can be used for automating the task of alcoholism detection.

Brain tumour detection is a critical medical procedure where early and accurate diagnosis significantly improves patient outcomes. This study explores the application of deep learning models, specifically VGG19 and InceptionV3, for detecting brain tumours in MRI images. We fine-tuned both models using transfer learning and evaluated them on a dataset of MRI scans. InceptionV3 achieved 100% validation accuracy, while VGG19 achieved 95%, demonstrating their high efficacy in medical image classification. In addition to deep learning, the study integrates insights from traditional image processing techniques, such as edge detection and probabilistic neural networks (PNNs), highlighting how combining deep learning with traditional methods can enhance image preprocessing and quality. The results underscore that while deep learning models are highly effective in brain tumour classification, traditional techniques still play a vital role in optimizing the overall detection process.

There are some problems in feature extraction and representation of Chinese mental verbs, such as low accuracy and low efficiency. In order to further improve the computational efficiency and accuracy of Chinese mental verb text, based on deep learning theory, activation function and damage function were used to optimize the original model. Considering the calculation method of model gradient, the optimization model describing the characteristics of Chinese mental verbs is finally obtained. The model can be used to analyze the variation of the characteristic parameters of Chinese verbs and the method of representation. Finally, the model error is analyzed by the method of comparative verification. Relevant studies show that the number of outputs and output results corresponding to softmax function will influence the test results of the model. By comparing the curves, it can be seen that the curve corresponding to the output number has an obvious increasing trend, while the corresponding output result curve has an opposite changing trend. The linear and nonlinear characteristics of the two curves are obvious. The real value of the mean square error function shows a change of linear increase, while the corresponding output value shows a change trend of gradual decline, which indicates that the two kinds of data have different influences on the model under related algorithms. It can be seen from the error data that the gradual increase of independent variables will improve the accuracy of the test results. Five different Chinese mental verb parameters have different manifestations in the deep learning model: among them, declarative verbs fluctuate in a small range and have little corresponding influence. However, the fluctuation of nondeclarative verbs and positive and negative declarative verbs is relatively small, and the curve is relatively stable. Negative verbs have a positive influence on the test output. Double negative verbs have negative effects. Finally, the accuracy of the model is verified by calculating the difference between experimental data and model data. This research can provide theoretical support and model verification method for the application of deep learning model in other fields of Chinese language.