Use Of Convolutional Neural Networks Research Articles

Robust prostate disease classification using transformers with discrete representations.

Automated prostate disease classification on multi-parametric MRI has recently shown promising results with the use of convolutional neural networks (CNNs). The vision transformer (ViT) is a convolutional free architecture which only exploits the self-attention mechanism and has surpassed CNNs in some natural imaging classification tasks. However, these models are not very robust to textural shifts in the input space. In MRI, we often have to deal with textural shift arising from varying acquisition protocols. Here, we focus on the ability of models to generalise well to new magnet strengths for MRI. We propose a new framework to improve the robustness of vision transformer-based models for disease classification by constructing discrete representations of the data using vector quantisation. We sample a subset of the discrete representations to form the input into a transformer-based model. We use cross-attention in our transformer model to combine the discrete representations of T2-weighted and apparent diffusion coefficient (ADC) images. We analyse the robustness of our model by training on a 1.5 T scanner and test on a 3 T scanner and vice versa. Our approach achieves SOTA performance for classification of lesions on prostate MRI and outperforms various other CNN and transformer-based models in terms of robustness to domain shift and perturbations in the input space. We develop a method to improve the robustness of transformer-based disease classification of prostate lesions on MRI using discrete representations of the T2-weighted and ADC images.

From Plants to Pixels: The Role of Artificial Intelligence in Identifying Sericea Lespedeza in Field-Based Studies

The increasing use of convolutional neural networks (CNNs) has brought about a significant transformation in numerous fields, such as image categorization and identification. In the development of a CNN model to classify images of sericea lespedeza [SL; Lespedeza cuneata (Dum-Cours) G. Don] from weed images, four architectures were explored: CNN model variant 1, CNN model variant 2, the Visual Geometry Group (VGG16) model, and ResNet50. CNN model variant 1 (batch normalization with adjusted dropout method) demonstrated 100% validation accuracy, while variant 2 (RMSprop optimization with adjusted learning rate) achieved 90.78% validation accuracy. Pre-trained models, like VGG16 and ResNet50, were also analyzed. In contrast, ResNet50’s steady learning pattern indicated the potential for better generalization. A detailed evaluation of these models revealed that variant 1 achieved a perfect score in precision, recall, and F1 score, indicating superior optimization and feature utilization. Variant 2 presented a balanced performance, with metrics between 86% and 93%. VGG16 mirrored the behavior of variant 2, both maintaining around 90% accuracy. In contrast, ResNet50’s results revealed a conservative approach for class 0 predictions. Overall, variant 1 stood out in performance, while both variant 2 and VGG16 showed balanced results. The reliability of CNN model variant 1 was highlighted by the significant accuracy percentages, suggesting potential for practical implementation in agriculture. In addition to the above, a smartphone application for the identification of SL in a field-based trial showed promising results with an accuracy of 98–99%. The conclusion from the above is that a CNN model with batch normalization has the potential to play a crucial role in the future in redefining and optimizing the management of undesirable vegetation.

Heritage Identification of monuments using Deep Learning Techniques

The Heritage Identification of Monuments web application employs different Machine learning technologies like Convolutional Neural Networks(CNN) and Deep Learning, to identify historical sites and monuments. The project focuses on training machine learning model to identify heritage sites and monuments with the use of CNN to extract features from images and help in identifying historical monuments. This project focuses on collection data from satellite images and user contributions, thus expanding the database and offering information on different identified monuments. This initiative aims to promote general awareness among the public and promote heritage preservation with labelled datasets and machine learning technologies. Machine learning techniques have evolved over the years, in this project we highlight the main techniques and algorithms by which we extract data from images and compare different mathematical models to test the accuracy. Additionally, the web-app helps to create a good reach among the masses and raise awareness. Keywords— Machine learning, Deep Learning, Convolution Neural Network (CNN), Pattern Recognition, Indian Monuments, Open source.

Diagnostic effectiveness of deep learning-based MRI in predicting multiple sclerosis: A meta-analysis.

The brain and spinal cord, constituting the central nervous system (CNS), could be impacted by an inflammatory disease known as multiple sclerosis (MS). The convolutional neural networks (CNN), a machine learning method, can detect lesions early by learning patterns on brain magnetic resonance image (MRI). We performed this study to investigate the diagnostic performance of CNN based MRI in the identification, classification, and segmentation of MS lesions. PubMed, Web of Science, Embase, the Cochrane Library, CINAHL, and Google Scholar were used to retrieve papers reporting the use of CNN based MRI in MS diagnosis. The accuracy, the specificity, the sensitivity, and the Dice Similarity Coefficient (DSC) were evaluated in this study. In total, 2174 studies were identified and only 15 articles met the inclusion criteria. The 2D-3D CNN presented a high accuracy (98.81, 95% CI: 98.50-99.13), sensitivity (98.76, 95% CI: 98.42-99.10), and specificity (98.67, 95% CI: 98.22-99.12) in the identification of MS lesions. Regarding classification, the overall accuracy rate was significantly high (91.38, 95% CI: 83.23-99.54). A DSC rate of 63.78 (95% CI: 58.29-69.27) showed that 2D-3D CNN-based MRI performed highly in the segmentation of MS lesions. Sensitivity analysis showed that the results are consistent, indicating that this study is robust. This metanalysis revealed that 2D-3D CNN based MRI is an automated system that has high diagnostic performance and can promptly and effectively predict the disease.

INVESTIGATING LOSS FUNCTIONS FOR SEGMENTING AND DETECTING SHIPS ON SAR IMAGERY

Abstract. In accordance with the United Nations (UN) Sustainable Development Goal (SDG) 16: Peace, Justice, and Strong Institutions, this study explores ship monitoring through the use of Synthetic Aperture Radar (SAR) for its potential applications to economic and security purposes. One method to extract ships through SAR-derived imagery is to employ the use of convolutional neural networks (CNN). However, the extraction of small features continues to be a challenging task for CNNs. To improve the performance in such cases, one way is to employ the use of an appropriate loss function, which helps guide the CNN model during training. In this paper, Focal Combo (FC) loss, a recent loss function designed for extreme class imbalance, will be investigated to analyze its effects when applied to ship extraction. In doing so, this paper also presents a thorough comparison of existing loss functions in their capability to segment and detect ships on SAR imagery. Making use of the U-Net model, our results demonstrate that by using FC loss we can observe an increase in segmentation of about 9% in terms of f3-score and a decrease in missed detections by about 17 ships (after post-processing) when compared to cross-entropy loss. Unfortunately, it has also shown a significant drop in precision of about 35% resulting in an additional 270 ships being incorrectly detected in the background. In future work, varying CNN models shall be tested to see if the pattern persists and several trials shall be conducted to assess consistency.

CONVOLUTIONAL NEURAL NETWORK (CNN) BASED ON ARTIFICIAL INTELLIGENCE IN PERIODONTAL DISEASES DIAGNOSIS

Introduction: The main problem by many clinicians is the correct diagnosis of periodontal disease. Usually, conventional clinical measurements such as measuring probing depth, attachment loss, presence of plaque and calculus are the way to diagnose and classify periodontal disease. However, clinical examination has limited reliability for periodontitis screening. Likewise, the ability of dentists to read radiographs using conventional methods increases the risk of misdiagnosis. Due to the diversity of existing clinical criteria and the increase in knowledge about human health, changes in the diagnostic criteria for periodontal disease that have occurred in recent decades have led to several updates. Recent research has focused on developing artificial intelligence tools to assist in diagnostic and therapeutic roles. This literature review aims to determine the use of artificial intelligence-based convolutional neural network (CNN) in diagnosing periodontal disease. Review: Artificial intelligence (AI) can make more accurate and efficient diagnoses, thereby reducing the workload of dentists. The use of the convolutional neural network (CNN) system in diagnosis and treatment planning allows dentists to reduce diagnostic errors that arise. Several studies have found that the CNN algorithm can assist in detecting alveolar bone loss, gingival abnormalities, and assisting early intervention in implantology. The CNN system can also capture details that dentists miss in diagnosis, especially radiographic diagnosis. Conclusion: Implementing an AI system is effective in helping to analyze periodontal disease. The CNN algorithm outperforms other AI techniques that can be used to facilitate diagnosis and treatment planning by dentists in the future.

Diagnosis of Lungs X-ray Using Image Processing

The abstract of the document discusses the use of convolutional neural networks (CNN) and support vector machines (SVM) to detect Covid-19 patients based on X-ray chest images. The study focuses on using the ResNet-50 model to extract features from the images and classify them using SVM. The results show high sensitivity and overall performance values for Covid-19 detection. The study suggests that this method can be beneficial for radiology specialists and help reduce false detection rates. These methods offer potential solutions for the early and accurate diagnosis of COVID-19 patients using X-ray imaging. Keywords- covid-19, convolutional neural network, SVM, prediction, feature extraction, deep learning.

Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers.

Breast cancer is the most prevalent cancer among women, and its diagnosis requires the accurate identification and classification of histological features for effective patient management. Artificial intelligence, particularly through deep learning, represents the next frontier in cancer diagnosis and management. Notably, the use of convolutional neural networks and emerging Vision Transformers (ViT) has been reported to automate pathologists' tasks, including tumor detection and classification, in addition to improving the efficiency of pathology services. Deep learning applications have also been extended to the prediction of protein expression, molecular subtype, mutation status, therapeutic efficacy, and outcome prediction directly from hematoxylin and eosin-stained slides, bypassing the need for immunohistochemistry or genetic testing. This review explores the current status and prospects of deep learning in breast cancer diagnosis with a focus on whole-slide image analysis. Artificial intelligence applications are increasingly applied to many tasks in breast pathology ranging from disease diagnosis to outcome prediction, thus serving as valuable tools for assisting pathologists and supporting breast cancer management.

A Machine Learning Application for Medical Image Analysis Using Deep Convolutional Neural Networks (CNNs) and Transfer Learning Models for Pneumonia Detection

Pneumonia is still a major problem in world health, causing a lot of illness and death, particularly among the most susceptible people. For care and therapy to be successful, a prompt and precise diagnosis is essential. Recent years have seen encouraging outcomes in medical image processing tasks when using deep learning methods, including Convolutional Neural Networks (CNNs). Using deep convolutional neural networks (CNNs) and transfer learning models, we provide a machine learning application for detecting pneumonia in medical photographs.Our suggested system makes use of convolutional neural network (CNN) designs that have already been trained using massive natural picture datasets, including VGG, ResNet, and DenseNet. We hope that by training these algorithms on a collection of chest X-ray pictures, we may modify their characteristics to better diagnose pneumonia. Furthermore, we investigate data augmentation methods to improve the models' generalizability, especially in cases when there is a lack of labelled data.Standard measures including precision, sensitivity, specificity, and area under the curve of the receiver operating characteristic (AUC-ROC) are used to assess the performance of the created models on a heterogeneous dataset of chest X-ray images.  

Enhancing energy‐efficient sleep scheduling for Narrowband Internet of Things devices in coordinated 5G networks within smart environments

SummaryThis research project takes on a crucial role in the quickly changing field of integrated 5G networks inside smart environments by concentrating on the creation of an extremely effective sleep scheduling system designed especially for Narrowband Internet of Things (NB‐IoT) devices. This work introduces a unique method for precisely controlling the nodes' sleep schedules through the use of convolutional neural network (CNN) architecture, which optimizes both energy usage and operating patterns. The principal goal still stands to guarantee the extended lifetime of operation and dependability of NB‐IoT devices in the larger framework of intelligent ecosystems driven by synchronized 5G networks. To achieve its objectives, the research explores a number of complex domains and employs cutting‐edge technologies and techniques, such as CNN‐based pattern recognition. This method's real‐time component is essential since it allows for prompt modifications to sleep schedules in order to optimize energy savings. Further boosting the devices' effectiveness and flexibility is continuous contact with a central server, which guarantees that the devices are updated with the most recent data and instructions. Essentially, the main objective of this study is to greatly increase the energy economy and operational lifetime of NB‐IoT devices, which will allow for stable and long‐lasting IoT deployments in the context of 5G networks in intelligent settings. This advancement is not only a boon for businesses and industries leveraging IoT technology but also a substantial step toward building smarter, more energy‐efficient, and resilient smart ecosystems that benefit society as a whole.

Dynamic Light Scattering in Biomedical Applications: feature issue introduction

The feature Issue on “Dynamic Light Scattering in Biomedical Applications” presents a compilation of research breakthroughs and technological advancements that have shaped the field of biophotonics, particularly in the non-invasive exploration of biological tissues. Highlighting the significance of dynamic light scattering (DLS) alongside techniques like laser Doppler flowmetry (LDF), diffusing wave spectroscopy (DWS), and laser speckle contrast imaging (LSCI), this issue underscores the versatile applications of these methods in capturing the intricate dynamics of microcirculatory blood flow across various tissues. Contributions explore developments in fluorescence tomography, the integration of machine learning for data processing, enhancements in microscopy for cancer detection, and novel approaches in optical biophysics, among others. Innovations featured include a high-resolution speckle contrast tomography system for deep blood flow imaging, a rapid estimation technique for real-time tissue perfusion imaging, and the use of convolutional neural networks for efficient blood flow mapping. Additionally, studies delve into the impact of skin strain on spectral reflectance, the sensitivity of cerebral blood flow measurement techniques, and the potential of photobiomodulation for enhancing brain function. This issue not only showcases the latest theoretical and experimental strides in DLS-based imaging but also anticipates the continued evolution of these modalities for groundbreaking applications in disease detection, diagnosis, and monitoring, marking a pivotal contribution to the field of biomedical optics.

Diagnosis of Lungs X-ray Using Image Processing

The abstract of the document discusses the use of convolutional neural networks (CNN) and support vector machines (SVM) to detect Covid-19 patients based on X-ray chest images. The study focuses on using the ResNet-50 model to extract features from the images and classify them using SVM. The results show high sensitivity and overall performance values for Covid-19 detection. The study suggests that this method can be beneficial for radiology specialists and help reduce false detection rates. These methods offer potential solutions for the early and accurate diagnosis of COVID-19 patients using X-ray imaging. Key Words: covid-19, convolutional neural network, SVM, prediction, feature extraction, deep learning

Convolutional Neural Networks to Study Contrast-Enhanced Magnetic Resonance Imaging–Based Skeletal Calf Muscle Perfusion in Peripheral Artery Disease

Peripheral artery disease (PAD) is associated with impaired blood flow in the lower extremities and histopathological changes of the skeletal calf muscles resulting in abnormal microvascular perfusion. We studied the use of convolutional neural networks (CNNs) to differentiate PAD patients from matched controls by utilizing perfusion pattern features from contrast-enhanced magnetic resonance imaging (CE-MRI) of the skeletal calf muscles. We acquired CE-MRI based skeletal calf muscle perfusion in 56 individuals (36 PAD patients, 20 matched controls). Microvascular perfusion imaging was performed post reactive hyperemia at the mid-calf level with a temporal resolution of 409 ms. We analyzed perfusion scans up to 2 minutes indexed from the local pre-contrast arrival time frame. Skeletal calf muscles including the anterior muscle (AM), lateral muscle (LM), deep posterior muscle group (DM), and the soleus (SM) and gastrocnemius muscles (GM) were segmented semi-automatically. Segmented muscles were represented as 3D DICOM stacks of CE-MRI perfusion scans for deep learning analysis. We tested several CNN models for the 3D CE-MRI perfusion stacks to classify PAD patients from matched controls. Two of the best performing CNNs (resNet and divNet) were selected to develop the final classification model. A peak accuracy of 75% was obtained for both resNet and divNet. Specificity was 80% and 94% for resNet and divNet, respectively. In conclusion, deep learning utilizing CNNs, and CE-MRI skeletal calf muscle perfusion can discriminate PAD patients from matched controls. Deep learning methods may be of interest for the study of PAD.

Facial fingerprint analysis using artificial intelligence techniques and its ability to respond quickly during karate (kumite)

The document discusses the use of facial fingerprint analysis using artificial intelligence (AI) techniques to quickly respond during karate matches. The integration of AI with sports technical analysis has the potential to improve the technical and tactical level of athletes. Traditional methods for tactical intelligence analysis in competitive sports have limitations such as high cost, data loss, delay, and low accuracy, but the use of convolutional neural networks and graph convolution models has shown promising results in the automatic, intelligent analysis of karate athletes' technical action recognition, action frequency statistics, and trajectory tracking. Eye-tracking technology is also used to analyse various aspects of performance and help identify visual strategies employed by athletes. By analysing video footage of facial biometrics during karate competition performances, performance criteria can be measured based on relevant skills in karate, and an objective scoring rubric can be developed for each criterion. Then, the scores can be compared between performers to see individual strengths and weaknesses and to optimize training, technique, and performance. Ultimately, the study seeks to investigate how to improve performance and decision-making in kumite by using AI techniques to analyse the eye print during an exhibition performance.

STUDENT EXPRESSION DETECTION IN E-LEARNING PLATFORMS

Our research suggests a deep learning method for determining how engaged students are with their online courses. Our method makes use of convolutional neural networks (CNNs) to interpret facial expressions that are recorded via webcams during online conversations. To provide the best possible model input, we preprocess face photos and select a varied dataset. The CNN architecture captures both spatial and temporal dependencies, enhancing engagement level detection accuracy. Our model performs well in classifying expressions that indicate curious, distracted, enthusiastic, observant, uninterested through training and validation. In conclusion, that the online learning experience may be enhanced by using deep learning algorithms for engagement detection, making it more advantageous and successful for students. KEYWORDS – CNN, Webcams, Engagement level, Online learning, Deep Learning, Facial expressions

Crop Disease Identification Using Deep Learning Techniques By IJISRT

Detection and control of plant diseases is critical to maintaining global food security. Recent advances in deep learning and computer vision have revolutionized precision agriculture, especially in automatic detection of crop diseases. This research aims to further advance this new trend using deep learning techniques. It focuses specifically on the use of convolutional neural networks (CNN), specifically the VGG19 architecture, for the accurate and efficient detection of agricultural diseases. The study utilized a large database containing numerous photographs of healthy and diseased plants. Adding this information increases the power and capabilities of the model. The VGG19 architecture is based on algorithms that use transfer learning techniques to extract complex information from images.

Breast Cancer Diagnosis Using YOLO-Based Multiscale Parallel CNN and Flattened Threshold Swish

In the field of biomedical imaging, the use of Convolutional Neural Networks (CNNs) has achieved impressive success. Additionally, the detection and pathological classification of breast masses creates significant challenges. Traditional mammogram screening, conducted by healthcare professionals, is often exhausting, costly, and prone to errors. To address these issues, this research proposes an end-to-end Computer-Aided Diagnosis (CAD) system utilizing the ‘You Only Look Once’ (YOLO) architecture. The proposed framework begins by enhancing digital mammograms using the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique. Then, features are extracted using the proposed CNN, leveraging multiscale parallel feature extraction capabilities while incorporating DenseNet and InceptionNet architectures. To combat the ‘dead neuron’ problem, the CNN architecture utilizes the ‘Flatten Threshold Swish’ (FTS) activation function. Additionally, the YOLO loss function has been enhanced to effectively handle lesion scale variation in mammograms. The proposed framework was thoroughly tested on two publicly available benchmarks: INbreast and CBIS-DDSM. It achieved an accuracy of 98.72% for breast cancer classification on the INbreast dataset and a mean Average Precision (mAP) of 91.15% for breast cancer detection on the CBIS-DDSM. The proposed CNN architecture utilized only 11.33 million parameters for training. These results highlight the proposed framework’s ability to revolutionize vision-based breast cancer diagnosis.

Classification, registration and segmentation of ear canal impressions using convolutional neural networks

Today, fitting bespoke hearing aids involves injecting silicone into patients’ ears to produce ear canal molds. These are subsequently 3D scanned to create digital ear canal impressions. However, before digital impressions can be used they require a substantial amount of effort in manual 3D editing. In this article, we present computational methods to pre-process ear canal impressions. The aim is to create automation tools to assist the hearing aid design, manufacturing and fitting processes as well as normalizing anatomical data to assist the study of the outer ear canal’s morphology. The methods include classifying the handedness of the impression into left and right ear types, orienting the geometries onto the same coordinate system sense, and removing extraneous artifacts introduced by the silicone mold. We investigate the use of convolutional neural networks for performing these semantic tasks and evaluate their accuracy using a dataset of 3000 ear canal impressions. The neural networks proved highly effective at performing these tasks with 95.8% adjusted accuracy in classification, 92.3% within 20° angular error in registration and 93.4% intersection over union in segmentation.

Metody zwiększania ilości danych termowizyjnych w uczeniu maszynowym

Machine learning is increasingly being applied in the processing and analysis of thermal imaging for object recognition and identification. This article presents a study on the impact of data augmentation on the effectiveness of machine learning in the context of thermal image analysis. The publicly available FLIR ADAS dataset, which includes labeled thermal and visible light images, was used for this study. The research focuses on the use of Convolutional Neural Networks, specifically the YOLOv8 architecture, for object detection in thermal images. As part of the study, the FLIR ADAS dataset underwent preprocessing and augmentation, and was then used to train two different models: one based on grayscale images and another using a color palette. The results of the experiment indicate that data augmentation can significantly impact the effectiveness of the model, and the use of colors in thermal images may, in certain situations, further improve detection accuracy.

Intelligent extraction of road cracks based on vehicle laser point cloud and panoramic sequence images

In light of the limited efficacy of conventional methods for identifying pavement cracks and the absence of comprehensive depth and location data in two-dimensional photographs, this study presents an intelligent strategy for extracting road cracks. This methodology involves the integration of laser point cloud data obtained from a vehicle-mounted system and a panoramic sequence of images. The study employs a vehicle-mounted LiDAR measurement system to acquire laser point cloud and panoramic sequence image data simultaneously. A convolutional neural network is utilized to extract cracks from the panoramic sequence image. The extracted sequence image is then aligned with the laser point cloud, enabling the assignment of RGB information to the vehicle-mounted three dimensional (3D) point cloud and location information to the two dimensional (2D) panoramic image. Additionally, a threshold value is set based on the crack elevation change to extract the aligned roadway point cloud. The three-dimensional data pertaining to the cracks can be acquired. The experimental findings demonstrate that the use of convolutional neural networks has yielded noteworthy outcomes in the extraction of road cracks. The utilization of point cloud and image alignment techniques enables the extraction of precise location data pertaining to road cracks. This approach exhibits superior accuracy when compared to conventional methods. Moreover, it facilitates rapid and accurate identification and localization of road cracks, thereby playing a crucial role in ensuring road maintenance and traffic safety. Consequently, this technique finds extensive application in the domains of intelligent transportation and urbanization development. The technology exhibits significant promise for use in the domains of intelligent transportation and city development.