Lightweight CNN Model Research Articles

Analysis of Lung Disease Prediction using Machine Learning Algorithms

Lung diseases are a notable in global health concern, requiring early diagnosis for better recovery and survival rates. Deep learning strategies, especially CNNs, have shown great promise in self learning lung disease diagnosis from medical images like chest X-rays. Ensemble learning methods using pretrained networks such as VGG16, InceptionV3, and MobileNetV2 have achieved up to 94% accuracy in identifying conditions like COVID-19, pneumonia, and lung opacity. Lightweight CNN models also performed well, with accuracy up to 89.89%. Traditional machine learning algorithms, including Random Forest and Logistic Regression, yielded accuracy rates between 88% and 90%. A hybrid deep learning approach, combining CNN based feature extraction with classifiers like AdaBoost, SVM, and Random Forest, improved classification accuracy by 3.1% and reduced computational complexity by 16.91%. This hybrid method highlights the main feature for integrating deep learning with traditional classifiers to enhance lung disease detection efficiency

CSI-Based Human Activity Recognition via Lightweight CNN Model and Data Augmentation

CSI-Based Human Activity Recognition via Lightweight CNN Model and Data Augmentation

Real-Time Deepfake Video Detection Using Eye Movement Analysis with a Hybrid Deep Learning Approach

Deepfake technology uses artificial intelligence to create realistic but false audio, images, and videos. Deepfake technology poses a significant threat to the authenticity of visual content, particularly in live-stream scenarios where the immediacy of detection is crucial. Existing Deepfake detection approaches have limitations and challenges, prompting the need for more robust and accurate solutions. This research proposes an innovative approach: combining eye movement analysis with a hybrid deep learning model to address the need for real-time Deepfake detection. The proposed hybrid deep learning model integrates two deep neural network architectures, MesoNet4 and ResNet101, to leverage their respective architectures’ strengths for effective Deepfake classification. MesoNet4 is a lightweight CNN model designed explicitly to detect subtle manipulations in facial images. At the same time, ResNet101 handles complex visual data and robust feature extraction. Combining the localized feature learning of MesoNet4 with the deeper, more comprehensive feature representations of ResNet101, our robust hybrid model achieves enhanced performance in distinguishing between manipulated and authentic videos, which cannot be conducted with the naked eye or traditional methods. The model is evaluated on diverse datasets, including FaceForensics++, CelebV1, and CelebV2, demonstrating compelling accuracy results, with the hybrid model attaining an accuracy of 0.9873 on FaceForensics++, 0.9689 on CelebV1, and 0.9790 on CelebV2, showcasing its robustness and potential for real-world deployment in content integrity verification and video forensics applications.

A Multi-Step Image Pre-Enhancement Strategy for a Fish Feeding Behavior Analysis Using Efficientnet

To enhance the accuracy of lightweight CNN classification models in analyzing fish feeding behavior, this paper addresses the image quality issues caused by external environmental factors and lighting conditions, such as low contrast and uneven illumination, by proposing a Multi-step Image Pre-enhancement Strategy (MIPS). This strategy includes three critical steps: initially, images undergo a preliminary processing using the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm, effectively reducing the impact of water surface reflections and enhancing the visual effect of the images; secondly, the Multi-Metric-Driven Contrast Limited Adaptive Histogram Equalization (mdc) technique is applied to further improve image contrast, especially in areas of low contrast, by adjusting the local contrast levels to enhance the clarity of the image details; finally, Unsharp Masking (UM) technology is employed to sharpen the images, emphasizing their edges to increase the clarity of the image details, thereby significantly improving the overall image quality. Experimental results on a custom dataset have confirmed that this pre-enhancement strategy significantly boosts the accuracy of various CNN-based classification models, particularly for lightweight CNN models, and drastically reduces the time required for model training compared to the use of advanced ResNet models. This research provides an effective technical route for improving the accuracy and efficiency of an image-based analysis of fish feeding behavior in complex environments.

Building on prior lightweight CNN model combined with LSTM-AM framework to guide fault detection in fixed-wing UAVs

Building on prior lightweight CNN model combined with LSTM-AM framework to guide fault detection in fixed-wing UAVs

Automated Digitization of Student’s Marks from the Answer-Book Images Using a Lightweight CNN Model

Automated Digitization of Student’s Marks from the Answer-Book Images Using a Lightweight CNN Model

Emotion Detection Using ECG Signals and a Lightweight CNN Model

Emotion Detection Using ECG Signals and a Lightweight CNN Model

DS-ECA-CNN：一种新型轻量化CNN的WIFI指纹室内定位模型

DS-ECA-CNN：一种新型轻量化CNN的WIFI指纹室内定位模型

Lung Sound Classification With Multi-Feature Integration Utilizing Lightweight CNN Model

Lung Sound Classification With Multi-Feature Integration Utilizing Lightweight CNN Model

Facial Emotion Recognition (FER) Through Custom Lightweight CNN Model: Performance Evaluation in Public Datasets

Facial Emotion Recognition (FER) Through Custom Lightweight CNN Model: Performance Evaluation in Public Datasets

Texture and motion aware perception in-loop filter for AV1

Lossy compression introduces artifacts, and many conventional in-loop filters have been adopted in the AV1 standard to reduce these artifacts. Researchers have explored deep learning-based filters to remove artifacts in the compression loop. However, the high computational complexity of CNN-based filters remains a challenge. In this paper, a Texture- and Motion-Aware Perception (TMAP) in-loop filter is proposed to addresses this issue by selectively applying CNNs to texture-rich and high-motion regions, while utilizing non-learning methods to detect these regions. The proposed method introduces a new CNN structure, the Dense-Dual-Field Network (DDFN), which leverages a larger receptive field to enhance the quality of reconstructed frames by incorporating more contextual information. Furthermore, to improve perceptual quality, a novel loss function integrating wavelet-based perceptual information is presented. Experimental results demonstrate the superiority of our proposed models over other lightweight CNN models, and the effectiveness of the perceptual loss function is validated using the VMAF metric.

Novel Diagnosis Method for GIS Mechanical Defects Based on an Improved Lightweight CNN Model With Load Adaptive Matching

Mechanical defects of GIS equipment seriously threaten power grid security, but on-site complex operating conditions create great challenges for defect diagnosis. Therefore, this paper proposes a novel diagnosis and state assessment method for GIS mechanical defects under varying currents. First, a time-frequency analysis method of GIS vibration signals based on neighboring mode noise suppression was proposed, and a defect type and severity diagnosis model with load adaptive matching was designed with the improved SqueezeNet (RF-SqzNet). Then, vibration datasets with different severities and currents of typical defects were established based on 110 kV GIS mechanical vibration platform. Finally, model validation and comparison analysis are carried out. Results show that the proposed method effectively mines feature and achieves accurate multiobjective diagnoses of the defect type and severity under varying currents, which is more accurate than traditional methods. The smaller model and faster computing speed are more suitable for edge deployment application.

Systematic assessment method for post-earthquake damage of regional buildings using adaptive-network-based fuzzy inference system

Rapid damage assessment of regional buildings is critical in post-earthquake rescue and victim resettlement. In this study, a novel rapid damage assessment method for post-earthquake buildings based on multi-types of damage features was proposed. First, the roof-related damage features are extracted from UAV tilt photography with the method of multiscale segmentation and object-oriented classification. Then, the normalized digital surface model was introduced to determine the height-related damage features. Meanwhile, a lightweight CNN model was employed to realize the preliminary evaluation of building facade conditions. Finally, all obtained damage features were taken as inputs to an adaptive-network-based fuzzy inference system for damage assessment. The proposed method was characterized by the capability to comprehensively cover the damage features of regional buildings involved in the evaluation, and thus be able to obtain more accurate judgments than current methods based on a single feature. At the same time, the effectiveness and robustness of the proposed method were evaluated and demonstrated by the field test on the world's largest earthquake site in Beichuan, China, and the prediction of damage level on the testing set achieved an accuracy of 87.1%.

M2CE: Multi‐convolutional neural network ensemble approach for improved multiclass classification of skin lesion

AbstractDue to inter‐class homogeneity and intra‐class variability, the classification of skin lesions in dermoscopy images has remained difficult. Although deep convolutional neural networks (DCNNs) have achieved satisfactory performance for binary skin cancer classification, multiclass skin lesion classification is still an open problem due to the limited training samples and class imbalance issues. To tackle these issues, in this article, we propose a multi‐CNN ensemble approach dubbed for multiclass skin lesion classification. The includes three individual CNN models, each helping in extracting different high‐level features from skin images and thereby yielding different prediction results. First, we design a lightweight CNN model to extract prominent features and train it from scratch, which primarily aims at avoiding the data scarcity problem. Then, we ensemble two different pre‐trained CNN models with the lightweight model to improve the performance and generalization capability. The proposed ensemble approach can effectively fuse the predictions of each individual CNN model using the averaging method. The approach is validated using a benchmark data set, HAM10000, which contains skin lesion images of seven different classes. The results demonstrate that the outperforms base CNN models and state‐of‐the‐art approaches without using any external data.

Assessing the carbon footprint of soccer events through a lightweight CNN model utilizing transfer learning in the pursuit of carbon neutrality

IntroductionSoccer events require a lot of energy, resulting in significant carbon emissions. To achieve carbon neutrality, it is crucial to reduce the cost and energy consumption of soccer events. However, current methods for cost minimization often have high equipment requirements, time-consuming training, and many parameters, making them unsuitable for real-world industrial scenarios. To address this issue, we propose a lightweight CNN model based on transfer learning to study cost minimization strategies for soccer events in a carbon-neutral context.MethodsOur proposed lightweight CNN model uses a downsampling module based on the human brain for efficient information processing and a transfer learning-based module to speed up the training progress. We conducted experiments to evaluate the performance of our model and compared it with existing models in terms of the number of parameters and computation and recognition accuracy.ResultsThe experimental results show that our proposed network model has significant advantages over existing models in terms of the number of parameters and computation while achieving higher recognition accuracy than conventional models. Our model effectively predicts soccer event data and proposes more reasonable strategies to optimize event costs and accelerate the realization of carbon neutral goals.DiscussionOur proposed lightweight CNN model based on transfer learning is a promising method for studying cost minimization strategies for soccer events in a carbon-neutral context. The use of a downsampling module based on the human brain and a transfer learning-based module allows for more efficient information processing and faster training progress. The results of our experiments indicate that our model outperforms existing models and can effectively predict soccer event data and propose cost optimization strategies. Our model can contribute to the realization of carbon-neutral goals in the sports industry.

An efficient lightweight CNN model for real-time fire smoke detection

An efficient lightweight CNN model for real-time fire smoke detection

Diagnosis of tomato pests and diseases based on lightweight CNN model

Diagnosis of tomato pests and diseases based on lightweight CNN model

An augmented attention-based lightweight CNN model for plant water stress detection

An augmented attention-based lightweight CNN model for plant water stress detection

Development of an optimally designed real-time automatic citrus fruit grading–sorting​ machine leveraging computer vision-based adaptive deep learning model

Conventional automation approaches for postharvest operations are plagued by time and data inefficiency seldom leading to suboptimal solutions. Automatic machines often require highly skilled software professionals for calibration and reconfiguration thus making the technology prone to high costs. Contemporary sensors and smart devices capable of handling deep learning image analytics have been employed in the present study for the development of an automatic machine that performs postharvest operations, like—washing, vision-based sorting and weight-based grading of citrus fruits with much reduced human effort while achieving excellent performance for the designated tasks. Accuracy of performance was ensured by the optimal design of mechanical components carried out by kinematic synthesis and dimensional analysis. The machine was equipped with an effective custom lightweight CNN model “SortNet” that was designed and tuned to carry out vision-based classification of citrus fruits into “accept” and “reject” based on surface characteristics. SortNet was less complex and took less computational time while exhibiting comparable accuracy with respect to existing state-of-the-art pre-trained deep learning models. An embedded system operated by a single-board computer was used in the weight grading section for segregating fruits based on three weight categories. Evaluation, realization and transferability of the above said strategy was demonstrated by the real hardware with physical actuators working in real-time to serve as proof-of-concept for a sustainable solution to postharvest automation of citrus fruits.

Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP

Kidney abnormality is one of the major concerns in modern society, and it affects millions of people around the world. To diagnose different abnormalities in human kidneys, a narrow-beam x-ray imaging procedure, computed tomography, is used, which creates cross-sectional slices of the kidneys. Several deep-learning models have been successfully applied to computer tomography images for classification and segmentation purposes. However, it has been difficult for clinicians to interpret the model’s specific decisions and, thus, creating a “black box” system. Additionally, it has been difficult to integrate complex deep-learning models for internet-of-medical-things devices due to demanding training parameters and memory-resource cost. To overcome these issues, this study proposed (1) a lightweight customized convolutional neural network to detect kidney cysts, stones, and tumors and (2) understandable AI Shapely values based on the Shapley additive explanation and predictive results based on the local interpretable model-agnostic explanations to illustrate the deep-learning model. The proposed CNN model performed better than other state-of-the-art methods and obtained an accuracy of 99.52 ± 0.84% for K = 10-fold of stratified sampling. With improved results and better interpretive power, the proposed work provides clinicians with conclusive and understandable results.