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Brain Tumours Classification Using Support Vector machines Based on Feature Selection by Binary Cat Swarm Optimization 1st

Brain Tumours Classification Using Support Vector Machines B...

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1st international conference on Emerging Technology Trends in Internet of Things and Computing (TIOTC)

作者： Hassan, Wid Ali Ali, Yossra Hussain Ibrahim, Nuha Jameel Univ Technol Baghdad Dept Comp Sci Baghdad Iraq

ISBN: (纸本)9783030972554;9783030972547

Magnetic resonance imaging (MRI) is one of main imaging modalities which has become extremely important in the diagnosis of brain tumors in real life also it has a major role in suggested computer systems for classification and segmentation of tumors due to the advancements in computer science and information technology. Researchers all over the world have worked hard to develop methods for diagnosing and classifying brain tumors. This paper presents an intelligent segmentation and classification approach worked on brain MR images to support doctors in diagnosis processes. However, the approach will consist of 4 phases. Pre-processing will be performed to enhance the MRI image, followed by Binary thresholding combined with Morphology operation to perform segmentation, followed by feature extraction fusion consisting of Local Binary pattern (LBP), Gray Level Co-Occurrence Matrix Occurrence Matrix (GLCM), and Connected Regions. Modified Binary Cat Swarm Optimization (BCSO) combined with Support Vector machine (SVM) is employed to perform feature selection and classification. The module provides the desired accuracy, which is better than SVM alone and artificial fish swarm. The achieved result shows the feasibility of employing a binary cat swarm to resolve the optimization problem of the extracted features.

关键词： Brain tumor Magnetic resonance imaging (MRI) Binary cat swarm optimization (BCSO) Segmentation Medical image classification and Support vector machine (SVM)

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Computer Vision to Animal Footprint Classification Based on Deep learning Model 1st

Computer Vision to Animal Footprint Classification Based on ...

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1st international conference on Advances in Artificial Intelligence and machine learning in Big Data processing, AAIMB 2023

作者： Rifana Fathima, A. Dhanalakshmi, K. Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9783031730641

An emerging area in machine vision is a real biometric system that can identify and describe animal life in images and videos these programs offer methods for classifying animals using computer vision Probabilistic Neural Network (PNN) features a well-liked deep learning technique are the foundation of the current system for classifying animal faces. Here, the suggested system analyses photos of animal footprints to categorise them using deep learning. Using a clever method, the footprint photos are pre-processed and turned into grayscale boundaries. Gabor filter are used to extract features of segmented image. The dimensionality reduction is carried out based on unsupervised model, (PCA). Convolutional Neural Network (CNN) is used for classification and identifying the animal class. Footprints 0 dataset of five different animal categories of 100 images is to be used for classification. The performance analysis of the system is evaluated using the measure accuracy, precision, recall and fl- measure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Automatic Forest Road Extraction from LiDAR Data of Mountainous Areas 1st

Automatic Forest Road Extraction from LiDAR Data of Mountain...

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1st IAPR international Joint conference on Discrete Geometry and Mathematical Morphology (DGMM)

作者： Even, Philippe Ngo, Phuc Univ Lorraine LORIA CNRS F-54000 Nancy France

ISBN: (纸本)9783030766566;9783030766573

In this paper, a framework is proposed to extract forest roads from LiDAR (Light Detection and Ranging) data in mountainous areas. For that purpose, an efficient and simple solution based on discrete geometry and mathematical morphology tools is proposed. The framework is composed of two steps: (i) detecting road candidates in DTM (Digital Terrain Model) views using a mathematical morphology filter and a fast blurred segment detector in order to select a set of road seeds;(ii) extracting road sections from the obtained seeds using only the raw LiDAR points to cope with DTM approximations. For the second step, a previous tool for fast extraction of linear structures directly from ground points was adapted to automatically process each seed. It first performs a recognition of the road structure under the seed. In case of success, the structure is tracked and extended as far as possible on each side of the segment before post-processing validation and cleaning. Experiments on real data over a wide mountain area (about 78 km(2)) have been conducted to validate the proposed method.

关键词： LiDAR data Road detection Point cloud processing DTM image analysis

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Digital image Forensic based on machine learning approach for Forgery Detection and Localization 1

Digital Image Forensic based on Machine Learning approach fo...

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1st international conference on Mechatronics and Artificial Intelligence, ICMAI 2021

作者： Monika Passi, Abhiruchi Manav Rachna International Institute of Research and Studies India

machine learning for multimedia forensic is a new way of image forgery detection due to its amazing features of fast forgery detection. Compared with existing techniques of Deep learning and Convolution Neural Network ("CNN"), machine learning improves security in the specific forged region under various test conditions. Some researchers use Support Vector machine ("SVM") and k-nearest neighbors (k-NN) algorithms to detect forgeries and another category uses unsupervised classification, including self-organization feature map (SOFM) and fuzzy c-means. But there occurs a need to address the detection speed improvement under the present scenario. The proposed algorithm has been developed using a machine learning approach to improve detection speed by pre-processing of feature extraction and feature reduction using "DWT"and "PCA"where data is trained by support vector machine ("SVM") to provide quick results under various test conditions. This work specifies different image attacks like all types of geometric transformation, post-processing operations, etc., and presents efficiency in forgery detection and localization in case of multiple forgeries. © Published under licence by IOP Publishing Ltd.

关键词： Support vector machines

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An Intelligent machine learning Framework for Melanoma Classification System: A Critique 1st

An Intelligent Machine Learning Framework for Melanoma Class...

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1st international conference on Advances in Artificial Intelligence and machine learning in Big Data processing, AAIMB 2023

作者： Sridevi, S. Gowthami, S. Hemalatha, K. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Sri Krishna College of Engineering and Technology Coimbatore India Vellore Institute of technology Vellore India

ISBN: (纸本)9783031730672

One of the most common cancers, skin cancer accounts for roughly half of the cancer prevalence in worldwide. Melanocytes, which are cells that produce pigment, are the source of melanoma, a kind of skin cancer. Both the incidence and mortality rates of melanoma have sharply increased in recent years. The ailment that attracted a variety of studies can be treated more effectively by medical professionals with early identification. This article focuses on several segmentation techniques and a classification strategy based on machine learning for the identification of melanoma. The melanoma areas are easily recognized by the segmentation methodologies, and the classification procedure is made simpler. Convolutional neural networks (CNN) with U-Net are used for segmentation. A support vector machine (SVM) with a variety of kernels and a k-nearest neighbor (KNN) with a variety of k values are used to classify the segmented image in the classification phase. Performance demonstrates that the segmentation process significantly increases the SVM's classification effectiveness when acquired values from the machine learning classifier are compared. Further the results shall be improved by enhancing the analysis with large dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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ACRM: Attention Cascade R-CNN with Mix-NMS for Metallic Surface Defect Detection 25

ACRM: Attention Cascade R-CNN with Mix-NMS for Metallic Surf...

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25th international conference on pattern recognition (ICPR)

作者： Fang, Junting Tan, Xiaoyang Wang, Yuhui Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Collaborat Innovat Ctr Novel Software Technol & I MIIT Key Lab Pattern Anal & Machine Intelligence Nanjing Peoples R China

ISBN: (纸本)9781728188089

Metallic surface defect detection is of great significance in quality control for production. However, this task is very challenging due to the noise disturbance, large appearance variation, and the ambiguous definition of the defect individual. Traditional image processing methods are unable to detect the damaged region effectively and efficiently. In this paper, we propose a new defect detection method, Attention Cascade R-CNN with Mix-NMS (ACRM), to classify and locate defects robustly. Three submodules are developed to achieve this goal: 1) a lightweight attention block is introduced, which can improve the ability in capture global and local feature both in the spatial and channel dimension;2) we firstly apply the cascade R-CNN to our task, which exploits multiple detectors to sequentially refine the detection result robustly;3) we introduce a new method named Mix Non-Maximum Suppression (Mix-NMS), which can significantly improve its ability in filtering the redundant detection result in our task. Extensive experiments on a real industrial dataset show that ACRM achieves state-of-the-art results compared to the existing methods, demonstrating the effectiveness and robustness of our detection method.

关键词： Surface defects

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DeepSIT: Deeply Supervised Framework for image Translation on Breast Cancer Analysis

DeepSIT: Deeply Supervised Framework for Image Translation o...

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pattern recognition Systems (ICPRS), international conference on

作者： Maria Baldeon-Calisto Francisco Rivera-Velastegui Daniel Riofrío Ricardo Flores-Moyano Noel Peréz Diego Benítez Colegio de Ciencias e Ingenierías “El Politécnico” Ingeniería Industrial CATENA-USFQ Universidad San Francisco de Quito USFQ Quito Ecuador Departamento de Investigación y Postgrados Universidad Internacional del Ecuador UIDE Quito Ecuador

image translation networks are deep learning models that can convert an image from one domain to another while preserving the semantic content. These networks are helpful in the medical field for noise reduction, reconstruction, and modality conversion. In this work, we propose DeepSIT, a deeply supervised framework for image translation. DeepSIT is a conditional generative adversarial network composed of a deeply supervised U-Net generator network and four PatchGAN discriminator networks. The generator performs the translation task while the discriminators judge the quality of the generated images. Unlike other works, the generator has four output layers located in the final and intermediate layers of the network. Each output layer generates a synthetic image, which is evaluated using a pixel-wise L1 loss function. Furthermore, the four discriminator networks receive a predicted image from an output layer to judge the quality of the translation at different scales. A promising application of image translation is the generation of immunohistochemical (IHC) images from Hematoxylin and Eosin (HE) images for breast cancer diagnosis. The proposed framework is evaluated in the latter tasks using the BCI image Generation Grand Challenge dataset. DeepSIT achieves first place in the post-challenge leaderboard with an average of 0.545 SSIM and 18.037 PSNR in the test set.

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Performance comparison of various machine learning algorithms for ultrasonic fetal image classification problem 1st

Performance comparison of various machine learning algorithm...

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1st international conference on Frontiers in Smart Systems Technologies, ICFSst 2019

作者： Sathish Kumar, N. Kasiselvanathan, M. Vimal, S.P. Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College CoimbatoreTamilnadu641022 India

ISBN: (纸本)9789811550287

The machine learning uses statistical algorithms to provide the ability to learn with and without data programming. image classifier is used to categorize a subject or an object present in an image into predefined classes. A statistical algorithm used in numerous fields like pattern classification, regression, control systems, identification, and prediction. This research paper presents a novel approach performance comparison of machine learning classifiers used for the classification of ultrasonic fetal images. It presents Gabor feature extraction and various classification techniques to classify three different classes of ultrasound images. The proposed method is presented as follows. Initially, the Gabor features are obtained from raw images. To remove redundancy in features and dimensionality reduction principal component analysis (PCA) is applied. Finally, the features obtained from PCA are fed to the various machine learning classifiers and its performances are evaluated. The simulations results are carried out using MATLAB image processing toolbox. From the results, it is observed that decision tree (DT) algorithm and multi-layer perceptron (MLP) perform to be closer and this classifier outperforms all other classifiers. © Springer Nature Singapore Pte Ltd 2021.

关键词： Principal component analysis

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Transductive Support Vector machines (TSVM) With Hybrid Recurrent Neural Networks For Medical image Filtering

Transductive Support Vector Machines (TSVM) With Hybrid Recu...

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Innovations in High Speed Communication and Signal processing (IHCSP), international conference on

作者： Ravi Kumar Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Filtering an image is one of the most fundamental and consequential tasks in the field of image processing. As of now, scientists still face a very real and pressing dilemma. images, whether in print or digital form, are a crucial criterion in many different industries, including geosciences, aerospace, education, agriculture, entertainment, surveillance, and many more. Background noise degrades image quality, despite extensive research aimed at finding a solution to this problem and the anticipation of a plethora of possible countermeasures. Methods for picture filtering, including those based on machine learning and those based on optimization, are detailed here. It has been possible to categorize these methods by the methods they employ. Gaussian noise, white noise, salt and pepper noise, speckle noise, and poison noise were all removed from lung CT scans using a combination of Recurrent Neural Networks (RNNs) and Transductive Support Vector machines (TSVMs) in this study. Using TSVM, RNN can function better than ever before. The approach proposed here uses LstM to normalize batches of data. By simulating a large number of individual beehives, the ABC (Artificial Bee Colony) algorithm may determine the optimal size of each batch. MATLAB was used to realize the suggested method. The experimental consequences demonstrate that the suggested system overtakes the baseline in terms of PSNR, MSE, and precision.

关键词： Support vector machines Technological innovation Recurrent neural networks Toxicology Filtering X-rays Speckle

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Real-Time Object Detection in Microscopic image of Indian Herbal Plants using YOLOv5 on Jetson Nano 1

Real-Time Object Detection in Microscopic Image of Indian He...

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1st international conference on Connected Systems and Intelligence, CSI 2022

作者： Jha, Yash Prajapati, Harsh Fataniya, Bhupendra Nirma University Ahmedabad Electronics and Communication Dept. Ahmedabad India

ISBN: (纸本)9781665458153

Object detection has been evolving greatly in recent years and the advancements in hardware and software technologies have made it possible to perform object detection with ease. Due to the enhanced capabilities of the modern processors and Graphics processing Unit (GPU) of doing an exponentially complex and extensive number of iterations in very less time. Real-time object detection has become highly popular and the center of attention in recent years because most of the hardware owned by common users is powerful enough to compute that which unlocks whole new possibilities for implementing real-time object detection in numerous applications in various domains. Real-time herbal plant detection is one such topic that has many applications in the field of ayurvedic medicines and many other pharmaceutical applications that can be used to spike the efficiency in identifying these herbal plants that can be used as a precaution and even as a cure for numerous health problems. There are many existing algorithms for real-time detection, but the evolution of new Artificial Neural Network (ANN) and machine learning (ML) techniques unlocks new ways to implement recent and advanced algorithms to apply for real-time detection of such powdered microscopic images to achieve better performance in various aspects compared to already existing methods. Our model is trained for detecting three types of microscopic herbal plants. © 2022 IEEE.

关键词： Graphics processing unit

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