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Breast Mammogram Analysis and Classification Using Deep Convolution Neural Network

Computer systems Science & Engineering 2022年第10期43卷 275-289页

作者： v.Ulagamuthalvi G.Kulanthaivel A.Balasundaram Arun Kumar Sivaraman Department of Computer Science and Engineering Sathyabama UniversityChennai600119India Department of Electrical Electronics and Communication EngineeringNITTTRChennai600113India Centre for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of Technology(VIT)Chennai600127India School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India

One of the fast-growing disease affecting women’s health seriously is breast *** is highly essential to identify and detect breast cancer in the earlier *** paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer *** learning algorithms are fully automatic in learning,extracting,and classifying the features and are highly suitable for any image,from natural to medical *** methods focused on using various conventional and machine learning methods for processing natural and medical *** is inadequate for the image where the coarse structure matters *** of the input images are downscaled,where it is impossible to fetch all the hidden details to reach accuracy in *** deep learning algorithms are high efficiency,fully automatic,have more learning capability using more hidden layers,fetch as much as possible hidden information from the input images,and provide an accurate *** this paper uses AlexNet from a deep convolution neural network for classifying breast cancer in mammogram *** performance of the proposed convolution network structure is evaluated by comparing it with the existing algorithms.

关键词： Medical image processing deep learning convolution neural network breast cancer feature extraction classification

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Optimized double transformer residual super-resolution network-based X-ray images for classification of pneumonia identification

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KNOWLEDGE-BASED systems 2025年 311卷

作者： Prasath, G. Jerald Prabu, S. Mayil, v. valli Saini, Sumit T John Inst Technol Dept Comp Sci & Engn Bangalore India SRM Inst Sci & Technol Sch Comp Tiruchirapalli Campus Tiruchirapalli Tamil Nadu India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Guntur 522302 Andhra Pradesh India Cent Univ Haryana Dept Elect Engn Mahendergarh India

Pneumonia is an infectious disease characterized by inflammation of the lungs' air sacs, which results in the accumulation of fluid or pus. Medical images is important for the timely identification and precise diagnosis of illnesses;chest X-rays are a commonly utilized modality for respiratory disorders including pneumonia. In this research, optimized double transformer residual super-resolution network-related chest x-ray imageries for the classification of pneumonia identification (DTRSN-XRI-CPI). The procedure involves pre-processing the input image using region-aware neural graph collaborative filtering (RNGCF) to reduce noise, enhance contrast, and eliminate high and low frequencies from the collected dataset. Next, the Synchro-squeezed fractional wavelet transform (SFWT) is utilized for the feature extraction to extract color features such as color, shape, spatial, texture, and relation from the image. Hence, the weight parameters for DTRSN are optimized using the Hunter Prey Optimization algorithms (HPOA). Then the DTRSN-XRI-CPI is implemented in Python and the performance metrics like precision, accuracy, recall, specificity, F1-score, and ROC are analysed. The performance of the DTRSN-XRI-CPI approach attains 20.7 %, 22.6 % and 30.5 % higher accuracy;21.8 %, 29.3 % and 30.5 %higher precision and 21.8 %, 29.5 % and 32.6 % higher recall when analysed through existing an intelligent computational framework based on deep learning for the identification and classification of pneumonia illness (ICPDDL-ICF), an adaptive and altruistic deep feature selection approach based on PSO for pneumonia detection from chest X-rays (APSO-DFSM-PDCX) and a deep learning system that uses explainable AI (DLAIB-PI-EAI) techniques respectively.

关键词： Pneumonia Hunter-prey optimization algorithms Region-aware neural graph collaborative filtering Double transformer residual super-resolution network and synchrosqueezed fractional wavelet transform

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Face Mask Detection in Real Time using Machine Learning algorithms 3

Face Mask Detection in Real Time using Machine Learning Algo...

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Kavitha, R. Sultania, Tanishq Padhmashri, v. Shreya, A. Kumaraguru College of Technology Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798331509675

Coronavirus pandemic caused by a deadly virus that rapidly spread worldwide, necessitated the usage of face mask to minimize the airborne transmission of the virus. An automated face mask recognition system has made it easier to identify if an individual is wearing a face mask or not. HAAR-CASCADE and ResNet10 algorithms are applied for initial face recognition and then Mobilenet v2 and vGG16 algorithm are employed for recognizing face masks in gatherings. Amongst the stages involved in accomplishing the AI training are the pre-processing augmenting data, training, testing, and segmenting images. During each step, the CNN algorithms are utilized to generate an image segmentation of the input dataset highlighting those wearing masks and those without mask. The model is then trained with dataset, Mobilenet v2 yields an accuracy of 96.6% and vGG16 yield an accuracy of 99.42% in detecting individuals with and without wearing masks. vGG16's deep architecture, feature extraction and transfer learning abilities enable it to be highly precise and effective. However, vGG16 is highly resource intensive and needs extensive computation time. © 2025 IEEE.

关键词： image segmentation

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Inexpensive SAR testbed for 3D mmWave imaging applications

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IET RADAR SONAR AND NAvIGATION 2025年第1期19卷

作者： Mahbub, S. M. Yahea Dawood, Muhammad Shareef, Ehtesham New Mexico State Univ Klipsch Sch Elect & Comp Engn Las Cruces NM 88003 USA

In this paper, the authors investigate and develop a low-cost synthetic aperture radar (SAR) testbed and utilise it to capture and reconstruct high-resolution 3D mmWave SAR images. Despite much attention from both industrial and academic scholars, cost restraints and complexity have hindered the deployment of SAR systems, particularly in the mmWave domain. This paper outlines the major components and systems of the 3D SAR testbed. The authors build a testbed with low-cost, commercially available hardware components and include open-source software that is highly reconfigurable and simple to reproduce. The multiple-input multiple-output (MIMO) radar sensor uses a frequency-modulated continuous wave (FMCW) chirp at the v-band (40-75 GHz) and synthesises a rectilinear two-dimensional planar aperture. The authors presented several reconstructed 3D images imitating real-world scenarios using the testbed. A non-linear sampling technique that has significantly decreased the amount of time required for data acquisition was implemented. In addition, the authors employed multiple image quality metrics to quantify and compare the images produced by the testbed. The testbed can be used to test and validate new techniques and algorithms.

关键词： millimetre wave radar radar imaging radar signal processing synthetic aperture radar

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Revolutionizing Industries with Aerial vehicle using Mobile for Surveillance 2

Revolutionizing Industries with Aerial Vehicle using Mobile ...

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2nd International Conference on Sustainable Computing and Smart systems (ICSCSS)

作者： Surendar, v Raghavendran, P. S. Poojitha, P. T. Shivali, S. Sakthivel, G. Lokesh, M. Kongu Engn Coll Dept EEE Perundurai India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558;9798350379990

Revolutionizing industries with aerial vehicles using mobile for surveillance is the proposed title of this paper. The availability of drones has created several opportunities, particularly in the areas of agriculture, transportation, and security. This study is a follow-up to the creation of a drone controlled by Wi-Fi-based UAv and advanced image processing. To provide simple and seamless control, a mobile application has been released. Drone-taken photos and videos can also be processed and studied right away. Increasing the degree of autonomous vehicle operations, improving the accuracy of picture identification/analysis algorithms, and ensuring continuous communication between the drone and the control software are perhaps the key objectives of this work. The image processing component aims to take into account sophisticated improvements such as terrain analysis, object detection, and anomaly detection, which are crucial, particularly in the context of security surveillance. Ensuring steady and long-range connectivity while taking certain environmental elements into account can be critical. Enhancing the image processing technique to run smoothly on the off-board drone's hardware constraints is also a crucial step. However, integrating hardware and software continues to be a major challenge in the implementation of a dependable, high-accuracy, real-time analog-to-digital conversion system. The purpose of this research is to develop novel ways for improving UAv systems by integrating tough control systems with complicated image analysis capabilities. Keeping the aforementioned limits in mind, the purpose of this work is to improve understanding of how to create more self-sufficient and adaptive drones that can contribute to a broader range of professions or do more specific activities with minimal human interaction.

关键词： Wireless Fidelity Unmanned Aerial vehicle App Cutting-edge Drone image processing

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Deep learning based authentication schemes for smart devices in different modalities: progress, challenges, performance, datasets and future directions

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第28期83卷 71451-71493页

作者： Shende, Shailendra W. Tembhurne, Jitendra v. Ansari, Nishat Afshan Indian Inst Informat Technol Nagpur 441108 India Govt Coll Engn Chandrapur 442403 India

Deep learning algorithms have shown exceptional effectiveness in a wide range of supervised and unsupervised learning tasks in a variety of fields, including image processing, computer vision, natural language processing, and speech or voice processing. In this paper, a comprehensive analysis is conducted to assess the impact of deep learning on user authentication using both physiological and behavioural biometrics. This work encompasses the diverse deep learning approaches employed in authentication schemes tailored for smart devices. Meticulous scrutiny of commonly used datasets in these authentication studies is undertaken, accompanied by a comparative assessment of performance. The deep learning models under consideration span a spectrum of architectures, including deep neural networks, convolutional neural networks, deep auto-encoders, recurrent neural networks, and their variants. To enhance the clarity and categorization of authentication techniques for smart devices, a taxonomy is proposed based on the specific authentication metrics employed: (1) Knowledge-based Authentication (KBA), (2) Physiological Biometric-based Authentication (PBBA), (3) Behavioural Biometric-based Authentication (BBBA), (4) Physiological and Behavioural Continuous Authentication (PBBCA), and (5) Multi Modal Authentication (MMA). Furthermore, potential contributions of deep learning techniques to the realms of physiological and behavioural biometrics are discussed. Significance is placed on performance metrics, including accuracy, stability, and robustness, in evaluating these deep learning-based authentication systems. The challenges and limitations that deep learning approaches must surmount when dealing with real-world biometric data in the context of biometric identification systems are examined. This work not only underscores the transformative role of deep learning in user authentication but also offers valuable insights into the evolving landscape of biometric identification o

关键词： Deep learning CNN RNN Authentication Biometrics Smart devices

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Alternating Projection Method for Intersection of Convex Sets, Multi-Agent Consensus algorithms, and Averaging Inequalities

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COMPUTATIONAL MATHEMATICS AND MATHEMATICAL PHYSICS 2024年第4期64卷 848-871页

作者： Proskurnikov, A. v. Zabarianska, I. S. Politecn Torino I-10129 Turin Italy St Petersburg State Univ St Petersburg 199178 Russia

The history of the alternating projection method for finding a common point of several convex sets in Euclidean space goes back to the well-known Kaczmarz algorithm for solving systems of linear equations, which was devised in the 1930s and later found wide applications in image processing and computed tomography. An important role in the study of this method was played by I.I. Eremin's, L.M. Bregman's, and B.T. Polyak's works, which appeared nearly simultaneously and contained general results concerning the convergence of alternating projections to a point in the intersection of sets, assuming that this intersection is nonempty. In this paper, we consider a modification of the convex set intersection problem that is related to the theory of multi-agent systems and is called the constrained consensus problem. Each convex set in this problem is associated with a certain agent and, generally speaking, is inaccessible to the other agents. A group of agents is interested in finding a common point of these sets, that is, a point satisfying all the constraints. Distributed analogues of the alternating projection method proposed for solving this problem lead to a rather complicated nonlinear system of equations, the convergence of which is usually proved using special Lyapunov functions. A brief survey of these methods is given, and their relation to the theorem ensuring consensus in a system of averaging inequalities recently proved by the second author is shown (this theorem develops convergence results for the usual method of iterative averaging as applied to the consensus problem).

关键词： alternating projection method convex programming Fej & eacute r mappings distributed algorithms consensus multi-agent systems

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vision Based Runway Identification with marked or unmarked terrain for Automatic Landing Applications of UAv 8

Vision Based Runway Identification with marked or unmarked t...

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8th Conference on Advances in Control and Optimization of Dynamical systems (ACODS)

作者： Tripathi, Amit Kumar Patel, vijay v. Padhi, Radhakant Aeronaut Dev Agcy Bangalore Karnataka India Indian Inst Sci Fac Aerosp Engn Dept Bangalore Karnataka India

vision based runway identification using 'marked or unmarked terrain' image sequences captured from a fixed wing unmanned aerial vehicle through onboard stereovision sensor is presented in this paper. An innovative convolutional neural netwok (CNN) based YOLO-v8 object detection algorithm is used to detect the runway during approach segment of UAv. This deep learning algorithm detects the region of interest in real time and in a computationally efficient manner. The captured unknown road segment or runway image frames are processed and examined for width, length, level and smoothness aspects to qualify as a suitable runway for UAv landings. Also, it is ensured that there are no obstacles, patches or holes on the detected road or runway. Runway start and end threshold lines and regions, touchdown point and runway edge lines are considered as the region of interest. image processing algorithms are applied on the captured runway or road images to detect strong features in the region of interest. Feature detector based image processing algorithm with stereo vision constraint is used to establish the relation between unmanned aerial vehicle's center of gravity and detected runway feature points image processing algorithms like hough line detection, RANSAC, Oriented FAST and Rotated BRIEF (ORB), median filters, morphological methods are applied to extract terrain features. Based on the detected runway orientation and position with respect to UAv position. An automatic landing manoeuvre is performed by UAv autopilot to land the UAv on intended touchdown point on runway computed through detected feature points.

关键词： image processing Runway Identification Feature detection Region of Interest threshold lines touch down point YOLOv8 ORB CNN runway width slant distance

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FPGA Based image processing: A Comparative Study of Software-Centric and Hardware-Accelerated Approaches

FPGA Based Image Processing: A Comparative Study of Software...

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2024 International Conference on Smart Electronics and Communication systems, ISENSE 2024

作者： Parameswaran, P.N. Janardan, S. Nair, Siddharth R Thejaswitha, K.T. Nikhil, M. Adersh, v.R. College of Engineering Trivandrum Dept of Electronics and Communication Thiruvananthapuram India

ISBN: (纸本)9798331528126

This work compares two different approaches to image processing algorithm implementation in Zynq Zybo and Zedboard Field Programmable Gate Array (FPGA) boards. There are three main phases for the study, namely, Hardware-Accelerated image processing (HAIP), Software-Centric image processing (SCIP), and an in-depth analysis of their corresponding performance metrics. image processing algorithms were implemented in the SCIP phase, exclusively using the Zynq processing System (PS). On the other hand, the HAIP phase comprised creating distinct Intellectual Properties (IPs) for image processing tasks and integrating them with the Zynq PS through Direct Memory Access (DMA) and the Advanced eXtensible Interface (AXI) *** all assessed techniques, HAIP considerably outperformed SCIP in terms of processing time, for different image sizes, across both Zynq Zybo and Zedboard. On the other hand, as SCIP is software-centric, it showed minimal resource utilization and lower power consumption than HAIP, which showed higher resource utilization and power consumption. However, both methods produced processed images that were similar, highlighting the stability and dependability of FPGA-based image processing implementations. A comparative analysis between Zynq Zybo and Zedboard reveals insights into how different FPGA architectures impact the performance and resource utilization of image processing implementations. This work offers insights into the trade-offs between hardware-accelerated and software-centric approaches to image processing on FPGA platforms, guiding future design decisions for practical uses. © 2024 IEEE.

关键词： image analysis

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Application of Soft Computing for Prediction of Peripheral Neuropathy in T2DM-A Real-Time Data Analysis

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IEEE ACCESS 2025年 13卷 29264-29278页

作者： Sheikh, Mehewish Musheer Balachandra, Mamatha Narendra, v. G. Maiya, Arun G. Manipal Acad Higher Educ Manipal Inst Technol Dept Comp Sci & Engn Manipal 576104 India Manipal Coll Hlth Profess Dept Physiotherapy Manipal 576104 India

Type 2 Diabetes mellitus (T2DM) patients are experiencing diabetic foot problems that heavily burden healthcare systems around the globe. Although there have been challenges with diagnosing and treating these problems using traditional approaches, the advent of machine learning technology signals the beginning of a new age in diabetic foot care, with the promise of improved precision and customized treatment plans. Machine learning is a beneficial tool for extracting essential insights from large, complicated datasets to improve the accuracy of diabetic foot diagnosis and therapeutic planning. This research aims to employ artificial intelligence to build a decision support system that will use clinical and demographic variables to predict the likelihood that individuals with mild, moderate, or severe peripheral neuropathy may develop diabetic foot syndrome in T2DM. Real-time processing of clinical information is made possible by a customized stacked ensemble model, which offers immediate peripheral neuropathy risk prediction with low computing latency. The system's capacity to transform raw patients' data into valuable insights in milliseconds supports quick clinical decision-making. Additionally, comparison and testing have been conducted on four deep learning algorithms: ResMLP, LSTM, DNN, and 1D-CNN. The predictions produced by the classifiers have been interpreted using three explainers: LIME, Eli5, and SHAP. Using the mutual information feature selection technique, the final stack reached a maximum accuracy of 99%. The three most significant markers that helped predict the onset of diabetic foot syndrome were area under pressure, vpt right, and vpt left;the encouraging findings point to the possibility of predicting diabetic foot condition with a decision system.

关键词： Foot Diabetes Pressure measurement Skin Real-time systems Medical diagnostic imaging Machine learning algorithms Feature extraction Amputation Temperature distribution Artificial intelligence deep learning feature extraction image analysis machine learning prediction methods risk analysis

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