Pre-trained CNN Model Research Articles

Image Caption using VGG model and LSTM

Deep convolutional networks and recurrent neural networks have gained significant popularity in the field of image captioning tasks in recent times. As we all know the performance and the architecture of models are still eternal topic. We constructed the model using a new method to enhance its performance and accuracy. In our model, we make use of pretrained CNN model VGG (Visual Geometry Group) to extract image features, and learn caption sentence features using bidirectional LSTM(Long-Short-Term-Memory) which can better understand the meaning of sentences in the text. Then we combine the image features and caption features to predict captions for images. The dataset Flickr8K is used to train and test the model. Additionally, the model has the ability to produce captions that are shorter than a specified caption length. We evaluated our model with Bilingual Evaluation Understudy (BLEU) score which measures the similarity of predicted text and to the real text. After evaluation and comparison, our model is proved to be well-done on some conditions.

Cuffless blood pressure estimation from photoplethysmography using deep convolutional neural network and transfer learning

Blood pressure (BP) monitoring is an essential indicator for diseases of the cardiovascular system. Early detection of abnormalities in BP helps to significantly reduce the risk of diseases such as chronic heart failure, stroke and hypertension. In this study, we propose a new framework for systolic, diastolic and mean arterial blood pressure (SBP, DBP and MBP) estimation using PPG signals and its derivatives. The proposed framework includes the extraction of deep features by pre-trained CNN models based on transfer learning using images of signal waveforms. The most distinctive features are obtained by using the RFE feature selection algorithm on the extracted deep features. The selected deep features are exposed to a range of well-known machine and deep learning regression methods. The proposed BP estimation models have been evaluated with statistical metrics, visual analytical tools and international gold standards such as AAMI and BHS. Experimental results show that the first derivative of PPG, the VPG input image, the deep features obtained with DenseNet121 and the bi-directional gated recurrent unit (Bi-GRU) algorithm provide the best BP estimation performance. The results reveal that the SBP, DBP and MBP estimation models are grade-A according to the BHS protocol and meet the AAMI standard. The presented framework has been compared with well-established studies using the MIMIC II dataset, and the comparative analysis confirms that the proposed method outperforms existing techniques. The deep learning model for BP estimation offers a highly accurate, fast and practical system, especially in the monitoring of patients at risk of hypertension.

Hydrophobicity classification of polymeric insulators using a masked autoencoder model in vision transformer

The loss of hydrophobicity on the polymeric insulator’s surface leads to a continuous water channel formation. This water channel formation can initiate dry band arcing and subsequent flashover. Therefore, accurately identifying the hydrophobicity class is crucial as it provides the surface ageing information of the polymeric insulators. This study proposed a novel approach utilizing a masked autoencoder-based vision transformer image classifier to classify the hydrophobic condition of polymeric insulators accurately. A comprehensive series of tests were conducted on deep learning image classifier models to assess robustness. The experimental findings demonstrated that the vision transformer model exhibited a significant recognition accuracy of 99.69%, surpassing the performance of the CNN, pre-trained CNN, and ViT models.

CNN-based hot-rolled steel strip surface defects classification: a comparative study between different pre-trained CNN models

CNN-based hot-rolled steel strip surface defects classification: a comparative study between different pre-trained CNN models

Source printer identification using convolutional neural network and transfer learning approach

<span>In recent years, Source printer identification has become increasingly important for detecting forged documents. A printer's distinguishing feature is its fingerprints. Each printer has a unique collection of fingerprints on every printed page. A model for identifying the source printer and classifying the questioned document into one of the printer classes is provided by source printer identification. A paper proposes a new approach that trains three different approaches on the dataset to choose the more accurate model for determining the printer's source. In the first, some pre-trained models are used as feature extractors, and support vector machine (SVM) is used to classify the generated features. In the second, we construct a two-dimensional convolutional neural network (2D-CNN) to address the source printer identification (SPI) problem. Instead of SoftMax, 2D-CNN is employed for feature extractors and SVM as a classifier. This approach obtains 93.75% 98.5% accuracy for 2D-CNN-SVM in the experiments. The SVM classifier enhanced the 2D-CNN accuracy by roughly 5% over the initial configuration. Finally, we adjusted 13 already-pre-trained CNN architectures using the dataset. Among the 13 pre-trained CNN models, DarkNet-19 has the greatest accuracy of 99.2 %. On the same dataset, the suggested approaches achieve well in terms of classification accuracy than the other recently released algorithms. </span>

An ensemble approach for classification of tympanic membrane conditions using soft voting classifier

Otitis media is a medical concept that represents a range of inflammatory middle ear disorders. The high costs of medical devices utilized by field experts to diagnose the disease relevant to otitis media prevent the widespread use of these devices. This makes it difficult for field experts to make an accurate diagnosis and increases subjectivity in diagnosing the disease. To solve these problems, there is a need to develop computer-aided middle ear disease diagnosis systems. In this study, a deep learning-based approach is proposed for the detection of OM disease to meet this emerging need. This approach is the first that addresses the performance of a voting ensemble framework that uses Inception V3, DenseNet 121, VGG16, MobileNet, and EfficientNet B0 pre-trained DL models. All pre-trained CNN models used in the proposed approach were trained using the Public Ear Imagery dataset, which has a total of 880 otoscopy images, including different eardrum cases such as normal, earwax plug, myringosclerosis, and chronic otitis media. The prediction results of these models were evaluated with voting approaches to increase the overall prediction accuracy. In this context, the performances of both soft and hard voting ensembles were examined. Soft voting ensemble framework achieved highest performance in experiments with 98.8% accuracy, 97.5% sensitivity, and 99.1% specificity. Our proposed model achieved the highest classification performance so far in the current dataset. The results reveal that our voting ensemble-based DL approach showed quite high performance for the diagnosis of middle ear disease. In clinical applications, this approach can provide a preliminary diagnosis of the patient's condition just before field experts make a diagnosis on otoscopic images. Thus, our proposed approach can help field experts to diagnose the disease quickly and accurately. In this way, clinicians can make the final diagnosis by integrating automatic diagnostic prediction with their experience.

A Lung Cancer Detection with Pre-Trained CNN Models

Lung cancer is a common cancer in Malaysia, affecting the majority of male citizens. The early detection of lung cancer will decrease its death rate. The only way to detect lung cancer is with a CT scan, and it also requires the doctor to check the scan to confirm the disease. In another way, the computer's support for the detection and diagnosis tool will assist doctors in determining lung cancer more accurately and efficiently. There are three main objectives for this research work. The first target is to study state-of-the-art research work to detect and recognize lung cancer from CT scan images. Then, the article will aim to adopt pre-trained convolutional neural network models in lung cancer detection. It also evaluates the performance of convolutional models on lung cancer imagery data. Then, the pre-trained models with a few added layers and modifications to parameters such as epochs, batch size, optimizer, etc. to conduct model training in this article. After that, Python Pylidc is used in image pre-processing to filter the dataset. Overall, pre-trained models such as ResNet-50, VGG-16, Xception, and MobileNet achieve above-state-of-the-art performance in classifying lung cancer from CT scan images in the range of 78% to 86% accuracy. The best detection accuracy result is the pre-trained VGG-16 model with the addition of some fully connected layers, 16 batch sizes, and the Adam optimizer, which achieved 86.71%.

Automated Cephalometric Landmark Detection: A Novel Software Model Compared with Manual Annotation Method

Introduction AI-based automated cephalometric landmark detection streamlines orthodontic diagnosis and treatment planning, providing accurate, efficient, and reliable results. Benefits include saving time, minimizing subjectivity, improving precision, and facilitating continuous improvement. However, they should complement clinician expertise, ensuring qualified orthodontists make the final diagnosis and treatment plan. Aim To propose a method that automatically detects cephalometric landmarks on the X-ray images and compare these values with the manual annotation method. Methodology A dataset of 600 X-ray images, each containing 19 landmarks, was collected. Two orthodontists manually marked the 19 landmarks in 300 cephalograms and their coordinates were automatically extracted. The dataset was cleaned for errors, and a pre-trained CNN model with an EfficientNetB7 backbone was used for landmark detection. The model was trained on 80% of the dataset and tested on the remaining 20%. The two-step method included ROI extraction and landmark detection. The RMSE score was used to evaluate inter-examiner reliability and the R2 score was used to compare manual and automated models. Result Model landmark locations were compared to the manual method. The mean deviation of the predicted landmarks from the actual landmarks was calculated using RMSE, and the model showed acceptable accuracy compared to manual annotation. EfficientNetB7 was found to have detection accuracies similar to the manual annotation method. For landmarks like Porion, articulare, and soft tissue pogonion, the model outperformed the human annotation method and provides a consistent better result, and for points like Point A, pogonion, gnathion, and menton, the manual methods show more accurate results. Conclusion The study introduced an automated approach using deep learning to predict landmark locations, and the results demonstrate its accuracy in comparison with the manual annotation method. This approach effectively detects cephalometric landmarks, suggesting its potential for clinical use with orthodontist’s supervision.

Melanoma skin cancer detection using mask-RCNN with modified GRU model.

Introduction: Melanoma Skin Cancer (MSC) is a type of cancer in the human body; therefore, early disease diagnosis is essential for reducing the mortality rate. However, dermoscopic image analysis poses challenges due to factors such as color illumination, light reflections, and the varying sizes and shapes of lesions. To overcome these challenges, an automated framework is proposed in this manuscript. Methods: Initially, dermoscopic images are acquired from two online benchmark datasets: International Skin Imaging Collaboration (ISIC) 2020 and Human against Machine (HAM) 10000. Subsequently, a normalization technique is employed on the dermoscopic images to decrease noise impact, outliers, and variations in the pixels. Furthermore, cancerous regions in the pre-processed images are segmented utilizing the mask-faster Region based Convolutional Neural Network (RCNN) model. The mask-RCNN model offers precise pixellevel segmentation by accurately delineating object boundaries. From the partitioned cancerous regions, discriminative feature vectors are extracted by applying three pre-trained CNN models, namely ResNeXt101, Xception, and InceptionV3. These feature vectors are passed into the modified Gated Recurrent Unit (GRU) model for MSC classification. In the modified GRU model, a swish-Rectified Linear Unit (ReLU) activation function is incorporated that efficiently stabilizes the learning process with better convergence rate during training. Results and discussion: The empirical investigation demonstrate that the modified GRU model attained an accuracy of 99.95% and 99.98% on the ISIC 2020 and HAM 10000 datasets, where the obtained results surpass the conventional detection models.

Gradient aggregation based fine-grained image retrieval: A unified viewpoint for CNN and Transformer

The gradients of CNN are traditionally utilized for optimization and visualization. In this paper, we find that a discriminative representation hides in the gradients of convolution filters. Based on this, we propose a corresponding feature extraction and aggregation method for fine-grained image retrieval (FGIR). Firstly, we propose a metric to evaluate manually-designed loss functions and design a loss function originating from Grad-CAM in the testing phase based on it to extract the gradients of the convolution filters. Secondly, we take the gradients as the new features and design a succinct approach to aggregate them into a compact vector, which is named as Convolution Filters Gradient Aggregation (CFGA) feature. CFGA features can be extracted from pre-trained and fine-tuned CNN models. Extensive experiments are conducted on FGIR to verify the effectiveness of our proposed CFGA approach, compared with five supervised state-of-the-art methods and two unsupervised methods on two standard fine-grained retrieval datasets. Moreover, we generalize the CFGA method designed for CNN to Swin Transformer, and propose the Transformer parameter gradients aggregation (TPGA) method, which proves the applicability of the core idea of CFGA/TPGA to mainstream feature extraction models. We achieve state-of-the-art FGIR performance on CUB-200-2011 dataset and CARS196 dataset.

A novel automated system to detect breast cancer from ultrasound images using deep fused features with super resolution

Cancer patients can benefit from early detection and diagnosis. This study proposes a machine vision strategy for detecting breast cancer in ultrasound images and correcting several ultrasound difficulties such artifacts, speckle noise, and blurring. In quantitative evolution, edge preservation criteria were discovered to be superior to standard ones for hybrid anisotropic diffusion. A learnable super-resolution (SR) is inserted in the deep CNN to dig for extra possible information. The feature is fused with a pre-trained deep CNN model utilizing Gabor Wavelet Transform (GWT) and Local Binary Pattern (LBP). Machine learning (ML) techniques that are used to create these recommendation systems require well-balanced data in terms of class distribution, however most datasets in the real world are imbalanced. Imbalanced data forces a classifier to concentrate on the majority class while ignoring other classes of interest, lowering the predicted performance of any classification model. We propose a generative adversarial networks (GAN) strategy to overcome the data imbalance problem and improve the performance of recommendation systems in this research. Standard data is used to train this model, which assures a high level of resolution. In the testing phase, generalized data of varied resolution is used to evaluate the model's performance. It is discovered through cross-validation that a 5-fold method can successfully eliminate the overfitting problem. With an accuracy of 99.48 %, this suggested feature fusion technique indicates satisfactory performance when compared to current related works. Finally finding cancer region, researcher used U-Net architecture and extract cancer region from BC ultrasound images.

Review of Eye Diseases Detection and Classification Using Deep Learning Techniques

Automated diagnosis of eye diseases using machine and deep learning models has become increasingly popular. Glaucoma, cataracts, diabetic retinopathy, Myopia, and age-related macular degeneration are common eye diseases that can cause severe damage. It is crucial to detect eye diseases early to prevent any potentially serious consequences. Early detection of eye disease is vital for effective treatment. Doing in-depth reading to identify any potential signs of eye disease is highly recommended. This paper will review all machine learning models built to detect and classify eye diseases in addition to helping grasp all limitations and challenges in this field. Recognizing eye diseases is a difficult task that typically requires several years of medical experience. This research is to be conducted to serve as a starting point for finding the most versatile solution. This research aims to review eye disease classification using deep learning models, including VGG16, ResNet, and Inception. The general classification model consists of these steps: The first step is to collect the globally obtainable datasets for the eye disease and pre-process them to ensure the generalization of experiments. The goal is to train the model to recognize disease symptoms instead of tweaking the outcomes for a specific dataset section. With the successful deployment of deep learning techniques for image classification and object recognition, research is now directed towards deep learning techniques instead of traditional handcrafted methods. One possible solution for the eye diseases classification challenge is to use a pre-trained deep CNN model for representation and feature extraction. This solution can be followed by classifier methods, such as support vector machines (SVM), multilayer perceptron (MLP), etc. It has been detected that CNN-based methods learned on large-scale marked datasets can be used for eye disease classification tasks with limited training datasets.

Brain Tumor Detection Using a Deep CNN Model

The diagnosis of brain tumors through magnetic resonance imaging (MRI) has become highly significant in the field of medical science. Relying solely on MR imaging for the detection and categorization of brain tumors demands significant time, effort, and expertise from medical professionals. This underscores the need for an autonomous model for brain tumor diagnosis. Our study involves the application of a deep convolutional neural network (DCNN) to diagnose brain tumors from MR images. The application of these algorithms offers several benefits, including rapid brain tumor prediction, reduced errors, and enhanced precision. The proposed model is built upon the state‐of‐the‐art CNN architecture VGG16, employing a data augmentation approach. The dataset utilized in this paper consists of 3000 brain MR images sourced from Kaggle, with 1500 images reported to contain tumors. Through training and testing, the pretrained CNN model achieves a precision and classification accuracy rate of 96%, and the loss is 1%. Moreover, it achieves an average precision, recall, and F1‐score of 98.7%, 97.44%, and 98.06%, respectively. These evaluation metric values demonstrate the effectiveness of the proposed solution.

Stealing the Invisible: Unveiling Pre-Trained CNN Models through Adversarial Examples and Timing Side-Channels

Stealing the Invisible: Unveiling Pre-Trained CNN Models through Adversarial Examples and Timing Side-Channels

Dynamic and Static Handwriting Assessment in Parkinson's Disease: A Synergistic Approach with C-Bi-GRU and VGG19

Parkinson's disease (PD) is a neurodegenerative disorder causing a decline in dopamine levels, impacting the peripheral nervous system and motor functions. Current detection methods often identify PD at advanced stages. This study addresses early-stage detection using handwriting analysis, specifically exploring the PaHaW dataset for pen pressure and stroke movement data. Evaluating online and offline features, the research employs pre-trained CNN models (VGG 19 and AlexNet) for offline datasets, achieving an overall accuracy of 0.53. For online datasets, velocity, and acceleration features are extracted and classified using Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and recurrent neural networks (RNN), with GRU yielding the highest accuracy at 0.57. Notably, the convolution-based model C-Bi-GRU surpasses other architectures with a remarkable 0.75 accuracy, emphasizing its efficacy in early PD detection. These findings underscore the potential of handwriting analysis as a diagnostic tool for PD, contributing valuable insights for further research and development in medical diagnostics.

Human centric attention with deep multiscale feature fusion framework for activity recognition in Internet of Medical Things

Recent advancements in the Internet of Medical Things (IoMT) have revolutionized the healthcare sector, making it an active research area in the academic and industrial sectors. Following these advances, an automatic Human Activity Recognition (HAR) is now integrated into the IoMT, facilitating remote patient monitoring systems for smart healthcare. However, implementing HAR via computer vision is intricate due to complex spatiotemporal patterns, single stream fusion, and clutter backgrounds. Mainstream approaches practice pre-trained CNN model, which extract non-salient features due to their generalized weight optimization and limited discriminative feature fusion. In addition, their sequential models have inadequate performance in complex scenarios due to the vanishing gradients encountered during backpropagation across multiple layers. In response to these challenges, we propose a multiscale feature fusion framework for both indoor and outdoor environments to enhance HAR in healthcare monitoring systems, which is mainly composed of two stages: First, the proposed Human Centric Attentional Fusion (HCAF) network is fused with the intermediate convolutional feature of lightweight MobileNetV3 backbone to enriches spatial learning capabilities for accurate HAR. Next, a Deep Multiscale Features Fusion (DMFF) network is proposed that enhanced the long-range temporal dependencies by redesigning the traditional bidirectional LSTM network into a residual fashion followed by Sequential Multihead Attention (SMA) to eliminate non-relevant information and optimized spatiotemporal feature vectors. The performance of the proposed fusion model is evaluated on benchmark healthcare and general activity datasets. In the healthcare, we used Multiple Camera Fall and UR Fall Detection datasets that achieved 99.941% and 100% accuracy. Despite this, our fusion strategy is rigorously evaluated over three challenging general HAR datasets, including HMDB51,UCF101, and UCF50, demonstrating 74.942%, 97.337%, and 96.156% superior performance compared to State-of-The-Art (SOTA) methods. The run time analysis shows that the proposed method is 2x times faster than the existing methods.

Enhanced Hyperspectral Image Classification Through Pretrained CNN Model for Robust Spatial Feature Extraction

Enhanced Hyperspectral Image Classification Through Pretrained CNN Model for Robust Spatial Feature Extraction

Leveraging three-tier deep learning model for environmental cleaner plants production

The world's population is expected to exceed 9 billion people by 2050, necessitating a 70% increase in agricultural output and food production to meet the demand. Due to resource shortages, climate change, the COVID-19 pandemic, and highly harsh socioeconomic predictions, such a demand is challenging to complete without using computation and forecasting methods. Machine learning has grown with big data and high-performance computers technologies to open up new data-intensive scientific opportunities in the multidisciplinary agri-technology area. Throughout the plant's developmental period, diseases and pests are natural disasters, from seed production to seedling growth. This paper introduces an early diagnosis framework for plant diseases based on fog computing and edge environment by IoT sensors measurements and communication technologies. The effectiveness of employing pre-trained CNN architectures as feature extractors in identifying plant illnesses has been studied. As feature extractors, standard pre-trained CNN models, AlexNet are employed. The obtained in-depth features are eliminated by proposing a revised version of the grey wolf optimization (GWO) algorithm that approved its efficiency through experiments. The features subset selected were used to train the SVM classifier. Ten datasets for different plants are utilized to assess the proposed model. According to the findings, the proposed model achieved better outcomes for all used datasets. As an average for all datasets, the accuracy of the proposed model is 93.84 compared to 85.49, 87.89, 87.04 for AlexNet, GoogleNet, and the SVM, respectively.

Content-based image retrieval for medical diagnosis using fuzzy clustering and deep learning

Brain tumors are one of the most threatening causes of death worldwide. Neuroradiologists widely use Magnetic Resonance Imaging as a golden standard non-invasive imaging method for diagnosing Glioma grades. Brain tumor diagnosis based on radiology images is tedious and highly subjective to intra and inter-observer variability; it adversely affects therapeutic planning. Better medical treatment can be provided relating to similar past cases if relevant images are retrieved from an extensive medical image database. Content-Based Image Retrieval systems are potent image tools for dealing with such massive datasets. In a CBIR system, accurate classification and retrieval of similar pathological images can be effectively automated by utilising Convolutional Neural Network-based feature extraction methods. This paper presents a Content-Based Medical Image Retrieval pipeline on a medical domain using the CNN model for feature extraction and the clustering method used to index the feature map database. The proposed system applies a Multi-level Gain-based feature selection to reduce the dimensionality of the feature vectors obtained from the pre-trained CNN models. The experiment follows five-fold cross-validation on BraTS 2018 and 2020 datasets. The proposed system achieves state-of-the-art MRI brain image retrieval performance with a mean Average Precision and Precision@10 of 98.15% and 97.62%, respectively. The results prove the proposed method’s effectiveness and validate its clinical application.

A Hybrid Stacked CNN Model with Weighted Average Ensembling for Effective Face Recognition

The discipline of computer vision has given a lot of attention to facial recognition. Automated face recognition is extensively employed in various practical scenarios, including systems for streamlining immigration checkpoints, intelligent monitoring of visual data, and authentication of personal identity. Depending on the situation, it may be divided into the two separate duties of facial verification and face identification. This study proposed a hybrid stacked CNN model for face recognition system. The models have mastered the art of making their own inferences. The models are then combined to predict a class value using a cutting-edge method called weighted average ensembling. A more accurate estimate should be produced by the new assembly process. Pre-trained CNN models are used in our proposed method: AlexNet, Resnet50V2, and VGG-19, Yolov5, VGG-16 and ResNet50. When applied to Tufts dataset images, our suggested model successfully achieved 98.05% accuracy. We have also used the Discrete Wavelet Transform (DWT) method for denoising, SegNet for Image segmentation for better performance of the model proposed.