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Bone Fracture Classification in X-ray Images: A Deep Learning Approach Leveraging Transfer Learning

Bone Fracture Classification in X-ray Images: A Deep Learnin...

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International Conference on electrical, computer and Communication engineering (ECCE)

作者： Md. Sabbir Ahammed Md. Musfiqur Rahman Mridha Md. Taufiq Khan Md. Jamil Chaudhary Md. Fatin Nibbrash Nakib Sakib Imtiaz Md. Ashikuzzaman Ashik Dept. of Computer Science & Engineering Varendra University Rajshahi Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Bone fracture is a very common medical issue faced by medical practitioners, and fracture detection is quite crucial for treatment. Till now, the most popular diagnosing method for bone fracture is X-ray imaging. However, in many cases, an X-ray image of a fractured bone is prone to human error. Advancements in artificial intelligence (AI) have demonstrated the potential to achieve high accuracy in tasks like X-ray image classification of fractured bone. In this work, we have incorporated a deep learning-based approach to facilitate the diagnosis of bone fracture. We have utilized different pre-trained state-of-the-art (SOTA) convolutional neural networks (CNN), namely, DenseNet, VGG, ResNet, Xception, and EfficientNet as feature extractors, to extract higher-level feature space representations of X-ray images. We have incorporated multiple classifiers, namely Logistic Regression, Random Forest, Linear Regression, XGBoost, and a custom feed-forward network (FFN) to discriminate among the higher-level representations extracted by the aforementioned pre-trained feature extractors. Among the several feature extractor and classifier combinations we have experimented with, DenseNet169 combined with the custom FFN produced the best results, reporting an overall accuracy of 99.48%, Receiver Operating Characteristic Area Under the Curve (ROC-AUC) score of 99.99%, precision of 99.48%, recall of 99.48%, and F1 score of 99.48%. The results demonstrate that combining an appropriate pretrained SOTA CNN model and a classifier can achieve high classification accuracy in the bone-fracture X-ray image classification task. Such a method previews a promising avenue in helping medical practitioners to achieve better diagnostic performance with patient benefit.

关键词： Deep learning Accuracy Transfer learning Feature extraction Bones Convolutional neural networks X-ray imaging Medical diagnostic imaging Random forests Image classification

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A Deep Learning Approach for Classifying Retinal Diseases and Diabetic Retinopathy Stages to Enhance Early Detection in Diagnosis

A Deep Learning Approach for Classifying Retinal Diseases an...

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International Conference on electrical, computer and Communication engineering (ECCE)

作者： Shifa Tasmiah Tisha Sharmistha Chanda Tista Md Nazmul Hoq Dept. of Computer Science and Engineering Chittagong University of Engineering & Technology Chattagram Bangladesh Dept. of Computer Science and Engineering Concordia University Montreal Canada

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Diabetic Retinopathy (DR) is a common consequence of diabetes mellitus, resulting in retinal lesions that can adversely affect vision. If undetected and mistreated, diabetic retinopathy can ultimately result in blindness. This study concentrates on creating an enhanced model for the precise classification of retinal illnesses, particularly Diabetic Retinopathy. The suggested model increases the precision and effectiveness of disease classification by utilising deep learning methods and attention mechanisms. The study analyses the performance of many pre-trained models, such as ResNet152, EfficientNetB7, and MobileNetV3Large, using a varied dataset of retinal pictures. The ResNet152 model, enhanced with the Spatial Attention Module, achieved the best results in retinal disease classification, with an accuracy of 87.65(%), precision of 89.88(%), recall of 90.69(%), and F1-score of 88.58(%). This performance surpasses other models and demonstrating the effectiveness of attention modules in improving classification accuracy and robustness. The study also highlights the competitive performance of the suggested model by contrasting it with earlier studies in the field. All things considered, this research advances the classification of retinal diseases and may improve patient treatment and ophthalmology diagnostic precision.

关键词： Deep learning Diabetic retinopathy Accuracy Transfer learning Retina Software Robustness Medical diagnostic imaging Diseases Residual neural networks

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Alcoholic EEG Signal Feature Extraction Based on Relative Wavelet Bispectrum and Bispectrum-Gaussian Using CNN and ANN Classifier

Alcoholic EEG Signal Feature Extraction Based on Relative Wa...

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IEEE EMBS Conference on Biomedical engineering and Sciences (IECBES)

作者： Prima Dewi Purnamasari Fulky Hariz Zulkarnaen Melinda Melinda Emerson Pancawira Sinulingga Fahmi Fahmi Anak Agung Putri Ratna Dept. of Electrical Engineering Universitas Indonesia Depok Indonesia Dept. of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Dept. of Electrical Engineering Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798350383409

ISBN: (纸本)9798350383416

A person who is addicted to alcohol is most likely to have problems related to health and brain function, such as in doing cognitive tasks. Thus, detecting the alcoholic condition is necessary. One of the methods to check whether someone is still addicted to alcohol or not is by looking into their brain signal using an electroencephalograph (EEG). This research compares the performance of two feature extraction methods for EEG signal classification, relative wavelet bispectrum (RWB) and bispectrum-Gaussian; the classifications were done using two different kinds of models, ANN and CNN. According to the experiment's results, the 2D RWB feature and CNN classifier have the highest training accuracy of 99%, while the 1D RWB feature and ANN classifier have the highest testing accuracy of 90%. This resulted in RWB becoming the better EEG feature extraction method than bispectrum-Gaussian. The experiments also suggest that the variation of lag value during the autocorrelation calculation has an impact on the classification accuracy, where for every multiple of two of the lag values, resulting in increasing accuracy by 7.5% on average.

关键词： Training Deep learning Accuracy Pattern classification Alcoholic beverages Feature extraction Brain modeling Electroencephalography Testing Convergence

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Movie Recommendation System Using Euclidean Distance

Movie Recommendation System Using Euclidean Distance

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Khan, Muneeb Sami Hussain, Zunnurain Amaad, Muhammad Irtaza Hasan, Muhammad Zulkifl Khalid, Aqsa Chuhan, Saad Hussain Awan, Rimsha Yaqub, Muhammad Atif Ashraf, Farhan Information Technology University Department of Computer Engineering Lahore Pakistan Bahria University Lahore Campus Dept. of Computer Science Pakistan University of Central Punjab Faculty of Information Technology India National College of Business Administration and Economics Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350373783

In this study, we pivot from the commonly used cosine similarity to the Euclidean distance as the foundation for our film recommendation algorithm. Initially, data pertaining to users, films, and ratings are loaded using the panda's library. The algorithm then computes normalized ratings, tabulates the number of films in each genre, and visualizes this distribution. The frequency of ratings for each genre is similarly plotted. A dictionary is maintained to store movie IDs for each genre. These values are then integrated into the normalized rating matrix. For each genre, the normalized ratings of movies are cataloged in a lexicon. Using the normalized ratings matrix and movie IDs, a Data Frame is generated. Subsequently, the movie and ratings datasets are merged. A pivot table captures the number of films each user has watched per genre. The study also be happy with their purchase if these recommendations are tailored to their needs. As a result, the client would utilize this application once more. Because of the significant money gained determines the average number of films a user watches in each genre. This pivot table is refined to display movies in each genre that a user has watched, but only those exceeding the genre's average viewership. Instead of cosine similarity, the Euclidean distance is computed to gauge the similarity between movies, offering a fresh perspective on recommendation systems. © 2024 IEEE.

关键词： ContentBased Filtering Data Normalization Euclidean Distance Genre Analysis Machine Learning Algorithms Movie Recommendation System Personalized Recommendations Similarity Measurement User Preferences User Rating Analysis

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Evaluation of Single Current Regulator Based Field Weakening Strategy for PMSM Drives and Experimental Validation using HIL 2

Evaluation of Single Current Regulator Based Field Weakening...

引用

2nd International Conference on Next Generation Intelligent Systems, ICNGIS 2022

作者： Shahana Beegum, O.S. Khalid, Meera Binojkumar, A.C. Rajiv Gandhi Institute of Technology Dept. of Electrical Engineering Kerala Kottayam India

ISBN: (纸本)9781665467926

Permanent Magnet Synchronous Motors (PMSM) are widely used in traction purposes. It has the advantage of high efficiency, robustness, and good control characteristics. For the traction purpose, field weakening control is required to obtain wide speed operation. When the voltage reaches its maximum value, speed extension is possible by weakening the air gap flux. Direct weakening of air gap flux is not possible while it is possible by applying a demagnetising stator current. The conventional methods of flux weakening strategies are feed-forward flux weakening strategy and feed-back fluxweakening strategy. Feed-forward method depends upon the motor parameters an this leads to the instability of the system. The utilization of two current PI regulators leads to the saturation problems also. A Single Current Regulator (SCR) based flux weakening strategy is used to remove the saturation problem. In this strategy, dc bus voltage is fully utilized and provides better dynamic response. The simulation of the presented control is done by using MATLAB-Simulink and experimental evaluation is done using Typhoon HIL-402 in RBS mode. © 2022 IEEE.

关键词： Permanent magnets

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Ultra Low Power-Low Noise GNRFET Wideband Amplifier for Cryogenic Applications

Ultra Low Power-Low Noise GNRFET Wideband Amplifier for Cryo...

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IEEE National Conference on Aerospace and Electronics (NAECON)

作者： Zachary Cochran Maher Rizkalla John J. Lee Trond Ytterdal Linknath Surya Balasubramanian dept. of Electrical and Computer Engineering) Indiana University Purdue University at Indianapolis Indianapolis USA dept. of Electrical and Computer Engineering Norwegian University of Science and Technology Trondheim Norway

ISBN: (数字)9798350338782

ISBN: (纸本)9798350338799

Cryogenics amplifiers feature very low noise injection and low power consumption, and therefore, they are of highly demand in RF imaging, space communications, medical applications, and quantum computing. Unconditional stability with high gain also feature these applications. The band gaps of these devices are independent from temperature, suggesting their suitability for cryogenics applications. Furthermore, the nanometer scale lengths of these devices lead to ballistic transport with no scattering or diffusion, resulting in high-speed, low-power, and low noise capabilities within Extremely High Frequency (EHF) operating range that may extend into the THz. This study addresses these parameter issues within the range of 90K to 300K, assuming liquid nitrogen coolant medium, and further shows its stability above the room temperature as well. An appropriate model for the device that incorporates complex parasitics was investigated within this temperature range. The device with its estimated parameters, has been integrated into a simulated three-stage amplifier system. A low-noise gain between 16.9-18.9 dB with estimated noise figure of 2.97 dB and a bandwidth of 269 GHz bandwidth were predicted, with a high Stern stability factor suggesting unconditional stability over the bandwidth.

关键词： Bandwidth Power system stability Stability analysis Nanoscale devices Mathematical models Thermal stability Gain

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Design and Development of a Smart Agriculture (SA) System with Machine learning-Based IoT Architecture

Design and Development of a Smart Agriculture (SA) System wi...

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Information and Communication technology for Sustainable Development (ICICT4SD), International Conference on

作者： Robi Paul Junayed Bin Nazir Arnob Paul Arif Ahammad Dept. of Electrical and Computer Engineering Technology Rochester Institute of Technology Rochester New York USA Dept. of Electrical & Electronic Engineering Shahjalal University of Science and Technology Sylhet Bangladesh

Agriculture has always been a crucial part of the economy, but urbanization has started to reduce our fertile land. This rapid reduction has increased the infestations and diseases, bottlenecking production rates and threatening food safety. However, recent advancements in the IoT have posed many potential solutions for this emerging problem. Incorporating IoT-based infrastructures like a smart-farming system can reduce production costs and increase productivity and overall quality while maintaining environmental balance. This study aims to create a smart agriculture system using IoT that can maintain suitable harvesting conditions and classify diseases through a transfer learning-based approach using Deep Convolutional Neural Network (DCNN). Thereon necessary suggestions are passed to the user through the Android app. The system can successfully detect and suggest diagnosis to six classes, including healthy, narrow brown spot, leaf scald, leaf blast, brown spot, and bacterial leaf blight, with an average accuracy of 96.08%. The precision, recall, specificity, and F1-score were 0.9620, 0.9617, 0.9921, and 0.9616 using the non-normalized augmented dataset. The work is expected to offer a roadmap for the future generation of Smart Agriculture Systems by summarizing the cultivation system's disease detection, prevention, and overall optimization process.

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Computation of Big Data Security through Cross-Layer Techniques 5

Computation of Big Data Security through Cross-Layer Techniq...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Sharma, Priya Dwivedi, Shweta Bhati, Nitesh Singh Kumar, Santosh Sharda University Dept. of Computer Science Greater Noida India Integral and Innovative of Sustainable Education College Dept. of Information Technology Lucknow India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

Because smart devices are so common and have generated enormous volumes of data, a new strategy is required to handle the kinds of data known as big data in the modern world. Many people are quite concerned about security because of the size of big data. Therefore, encryption is the best way to secure data;nevertheless, typical procedures are insufficient to fully protect huge data sets. Therefore, specific, and effective security algorithm solutions are required to protect large amounts of data. It becomes problematic when a sizable amount of the data is not encrypted and there is uncertainty about the data. Therefore, a cross-layer security architecture is offered in this research to secure the important component of the data. Numerous data security approaches are employed to safeguard data, ensure that crucial information is not lost, and encrypt it using a robust encryption algorithm. Based on encryption time and throughput, a comparison is conducted between the cross-A-coin approach and the suggested framework. Throughput is used to calculate performance based on encryption time, followed by full encryption. © 2024 IEEE.

关键词： Big Data Cross-Layer Techniques Security Algorithm and Big Database Reliability

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GOSPA-Driven Gaussian Bernoulli Sensor Management 26

GOSPA-Driven Gaussian Bernoulli Sensor Management

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26th International Conference on Information Fusion, FUSION 2023

作者： Jones, George García-Fernández, Ángel F. Wong, Prudence W.H. Dept. of Electrical Engineering and Electronics University of Liverpool United Kingdom Dept. of Computer Science University of Liverpool United Kingdom Aries Research Centre Universidad Antonio de Nebrija Madrid Spain

ISBN: (纸本)9798890344854

This paper presents a multi-target metric driven approach to sensor management for Bernoulli filtering, in which at most one target of interest is present. The metric used is the generalised optimal sub pattern assignment (GOSPA) metric. We consider the problem of having an agile sensor operating in a surveillance area, tracking objects as they appear from a target birth distribution. Only one target of interest can exist at any given time-step and its single-target density is Gaussian. In this scenario, we have a grid of sensors that we can select from, one at a time using myopic planning. We evaluate the proposed sensor management algorithm via simulations. © 2023 International Society of Information Fusion.

关键词： Target tracking

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Universal Partitioning of a Large Array for Communications in Environments with Limited Spatial Coherence

Universal Partitioning of a Large Array for Communications i...

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Asilomar Conference on Signals, Systems & computers

作者： Yongjie Zhuang John R. Buck Andrew C. Singer Dept. Electrical and Computer Engineering Stony Brook University Stony Brook NY USA Dept. Electrical and Computer Engineering University of Massachusetts Dartmouth Dartmouth MA USA

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Large aperture arrays improve communication performance with higher gain, but are susceptible to limited spatial coherence of signals, especially in shallow water acoustic environments. An acoustic communication receiver's ability to combine the signal wavefront coherently degrades when there are losses across the array in either phase coherence or the ability to track phase variation. To mitigate such coherence issues, the array can be separated into smaller segments of sensors known as subapertures. The phase variations can be tracked and mitigated on each subaperture. Subsequently, each subaperture can be coherently combined to realize the full array gain, through this two-stage process. However, subaperture size selection remains an open problem, especially in dynamic, uncertain environments like those experienced in underwater acoustic communications. In this paper, a universal algorithm is proposed for realizing the performance of the best possible partitioning among a collection of partitions of the receiver into subapertures, such that the overall receiver performance is as good as if the best partitioning were known a priori. This work builds on previous work in universal adaptive filtering and beamforming.

关键词： Phased arrays Array signal processing Simulation Spatial coherence Coherence Receivers Acoustic arrays Partitioning algorithms Underwater acoustics Sensor arrays

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