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A Survey on Different Multimodal Medical image Fusion Techniques and Methods 1

A Survey on Different Multimodal Medical Image Fusion Techni...

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international conference on Innovation in Information Communication and Technology (ICIICT)

作者： Dolly, Jipsha Mariam Nisa, A. K. TKM Coll Engn Dept Comp Sci & Engn Kollam Kerala India

ISBN: (纸本)9781728116044

Multimodal fusion of medical image, as a powerful tool for the application of clinical images has grown with the emergence of various image modalities in medical imaging. The main objective of the image fusion is to merge features from several different input images into one image which becomes more reliable and easy to understand by patients. Fusion of medical image can apply in different areas, like image processing, computer vision, pattern recognition, machine learning and artificial intelligence etc. The fusion of multimodal medical images also helps the doctors for their easy diagnosis and treatments. In this review paper a survey is taken into account on different earlier methods used in fusion of multimodal medical images.

关键词： fusion tetrolet sparse modalities wavelet transform

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Detection of Diseases in Potato Leaves Using Transfer learning 1st

Detection of Diseases in Potato Leaves Using Transfer Learni...

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1st international conference on Computational Intelligence in pattern recognition, CIPR 2019

作者： Dasgupta, Soumik Ranjan Rakshit, Somnath Mondal, Dhiman Kole, Dipak Kumar Jalpaiguri Government Engineering College Jalpaiguri735102 India Cyware Labs Bangalore560102 India

ISBN: (纸本)9789811390418

The problem of food shortage has grown rampant in the recent times in developing countries. In a tropical country like India, potato is one of the major staple food that is eaten throughout the year. Recently the production of potato is falling short due to various diseases like Early Blight and Late Blight which cause a huge loss of the cropped plants. This also leads to a major loss in the national economy as well. The emergence of deep learning has affected many fields of machine learning research. Since it is not required in deep learning to develop hand-crafted features, it has found widespread adoption in the scientific community. To tackle the need for a huge amount of data for deep learning, another heavily implemented technique is used, namely, transfer learning, to make the training process faster and more accurate with a relatively small dataset at hand. The performance of the model is demonstrated both quantitatively by computing the accuracy metric as well as visually. The model is lightweight and robust and thus can be added to an application in a handheld device like smartphone so that crop growers could spot the disease affected crops on the go and save them from getting ruined. © 2020, Springer Nature Singapore Pte Ltd.

关键词： image classification

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Pneumonia Detection Using Deep learning Approaches 1

Pneumonia Detection Using Deep Learning Approaches

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1st international conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2020

作者： Tilve, Ashitosh Nayak, Shrameet Vernekar, Saurabh Turi, Dhanashri Shetgaonkar, Pratiksha R. Aswale, Shailendra S.R.I.E.I.T Computer Engineering Department Shiroda Goa India

ISBN: (纸本)9781728141428

Pneumonia is among the most prevalent diseases, and due to lack of experts it is difficult to detect. This paper focuses on surveying and comparing the detection of lung disease using different computer-aided techniques and suggests a revised model for detecting pneumonia, which will then be implemented as part of our future research. In this survey, we also tried to familiarize ourselves with the different image pre-processing techniques used to convert raw X-ray images into standard formats for analysis and detection, machine learning techniques such as CNN, RESNET, CheXNet, DENSENET, ANN and KNN, which is an important phase in accurate pneumonia detection. © 2020 IEEE.

关键词： Surveys

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Random Subspace Combined LDA Based machine learning Model for OSCC Classifier 1st

Random Subspace Combined LDA Based Machine Learning Model fo...

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1st international conference on Modeling, machine learning and Astronomy, MMLA 2019

作者： Nawandhar, Archana Kumar, Navin Yamujala, Lakshmi CMRIT ECE Bangalore590018 India Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham Bangalore560035 India Bangalore560100 India

ISBN: (纸本)9789813364622

Oral squamous cell carcinoma (OSCC) remains a major death causing oral cancer in developing countries. In recent years, tremendous development in medical imaging devices made microscopic colour images of biopsy samples available to the researchers. image processing and machine learning techniques can be used to develop automatic cancer grading mechanism. In this work, automatic OSCC classifier using Linear Discriminant Analysis combined with Random Subspace is developed and analyzed. The proposed classifiers automatically classifies the input image in one of the four categories, namely: Normal, Grade-I, II or III. Total 83 colour and texture features are computed from the 100 Haemotoxylin and Eosin (H&E) stained images of oral mucosa. The overall accuracy of the proposed classifier is 93.5% with sensitivity and specificity of 0.89 and 0.95 respectively. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Grading

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CNN Transfer learning of Shrimp Detection for Underwater Vision System 1

CNN Transfer Learning of Shrimp Detection for Underwater Vis...

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1st international conference on Information Technology, Advanced Mechanical and Electrical Engineering (ICITAMEE)

作者： Isa, Iza Sazanita Norzrin, Nor Nabilah Sulaiman, Siti Noraini Hamzaid, Nur Azah Maruzuki, Mohd Ikmal Fitri Univ Teknol MARA Fac Elect Engn Permatang Pauh 13500 Pulau Pinang Malaysia Univ Malaya Fac Engn Dept Biomed Engn Block A Kuala Lumpur 50603 Malaysia

ISBN: (纸本)9781728183091

In deep learning, convolutional neural network (CNN) mostly apply common overland images instead of underwater images classifiers. Even though there are few classifiers that have been introduced in marine and aquaculture application, there is still limited sources of the underwater images such as shrimp images. Generally, most conventional management systems in shrimp aquaculture implemented manual techniques that highly depend on human to observe shrimp conditions. One of the major problems of shrimp aquaculture is the challenge of recognizing underwater images, despite the characteristic atmosphere such as the murky and turbid water conditions. Many models of image classification have been introduced in order to solve the issue of early detection in shrimp and ponds problems. However, there are several limitations of the proposed methods such as semi-intelligence or fully wired systems. Therefore, an intelligence computational method and wireless system or internet of things (IoT)-based system is crucial in making sure a precision aquaculture farming. This study conducted a transfer learning model for CNN real time shrimp recognition. This study aims to accurately assess the performance of the developed CNN model by evaluating shrimp images based on intersection over union (IoU) between annotation and proposed models. The result shows the proposed model can successfully detect the shrimps with more than 95% accuracy. As a conclusion, the proposed model is able to detect the real time video recognition of underwater shrimp in ponds and is applicable in wireless farming.

关键词： Transfer learning CNN UVS images Real-time Video-processing

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1st international conference on Bioengineering and Biomedical Signal and image processing, BIOMESIP 2021

作者： Abou-Abbas, Lina van Noordt, stefon Elsabbagh, Mayada Montreal Neurological Institute-Hospital McGill University MontrealQCH3A 2B4 Canada Department of Psychology Mount Saint Vincent University HalifaxNSB3M 2J6 Canada

ISBN: (纸本)9783030881627

Identifying diagnostic biomarkers for autism spectrum disorder (ASD) is one of the challenges in autism research today. Recent studies using visual Event Related Potentials (ERPs) have identified abnormal patterns of brain activity in high-risk infants who go onto ASD diagnosis. In this study, we used well-established ERP components related to face processing. Features of these ERPs were used to explore the performance of machine learning algorithms in classifying ASD diagnostic outcomes. Data were used from the EEG Integrated Platform (EEG-IP). ERPs were recorded from six-months infants in response to static faces that dynamically changed between direct and averted eye gaze. Amplitude and latency measures of prominent ERP peaks including P100, N290 and P400 were derived across five scalp regions. Difference Scores between stimulus conditions (direct versus indirect eye gaze, toward versus away and face versus noise) were considered. Features were selected by weight correlation and used as inputs to three classifiers: k-Nearest Neighbor, Support Vector machines and Decision tree. Performance of these classifiers was compared. The results showed that the Decision tree classifier had the greatest average accuracy rate of 78.09% in classifying ASD diagnosis in high-risk infants. © 2021, Springer Nature Switzerland AG.

关键词： Support vector machines

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Face recognition with Kernel Principal Component Analysis and Support Vector machine 1

Face Recognition with Kernel Principal Component Analysis an...

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1st international conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Liliana, Dewi Yanti Setiawan, I. Made Agus State Polytech Jakarta Dept Informat & Comp Depok Indonesia Udayana Univ Denpasar Indonesia Univ Pittsburgh Pittsburgh PA 15260 USA

ISBN: (纸本)9781728129303

machine learning and pattern recognition recently become a hot topic in computing world. This is due to the fast-growing of resources as well as techniques that make it easier to solve machine learning and pattern recognition problems. Problems that require machine learning solutions may be very simple for humans but actually can be very complex for machines to solve them. Face recognition is amongst those problems. Almost all human can easily recognize others without require specific knowledge to do it, different from machines which require its. This paper discussed face recognition task using machine learning strategies which involved Kernel Principal Component Analysis (KPCA) and Support Vector machine (SVM) to identify person. KPCA extracted features from 2D image input and produced the important features of an image input. The extracted face features are recognized by SVM by classifying human face according to their stored identity in a database. SVM, which was basically a binary classifier worked by using one-against-one strategy to compare the face feature vector of a single test image to the stored face image in a face image database. Experiment results on grayscale images with size 92x112 pixels gave 96.25% of accuracy rate. Hence, KPCA and SVM for face recognition is a robust machine learning method.

关键词： face recognition kernel eigenface kernel PCA machine learning pattern recognition support vector machine

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A New Multi-filter Framework with statistical Dense SIFT Descriptor for Spoofing Detection in Fingerprint Authentication Systems 20th

A New Multi-filter Framework with Statistical Dense SIFT Des...

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20th international conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Contreras, Rodrigo Colnago Nonato, Luis Gustavo Boaventura, Maurilio Gasparotto Boaventura, Ines Aparecida Coelho, Bruno Gomes Viana, Monique Simplicio Univ Sao Paulo BR-13566590 Sao Carlos SP Brazil Sao Paulo State Univ BR-15054000 Sao Jose Do Rio Preto SP Brazil NYU 550 1St Ave New York NY 10012 USA Univ Fed Sao Carlos BR-13565905 Sao Carlos SP Brazil

ISBN: (纸本)9783030878979;9783030878962

Fingerprint-based authentication systems represent what is most common in biometric authentication systems. Today's simplest tasks, such as unlocking functions on a personal cell phone, may require its owner's fingerprint. However, along with the advancement of this category of systems, have emerged fraud strategies that aim to guarantee undue access to illegitimate individuals. In this case, one of the most common frauds is that in which the impostor presents manufactured biometry, or spoofing, to the system, simulating the biometry of another user. In this work, we propose a new framework that makes two filtered versions of the fingerprint image in order to increase the amount of information that can be useful in the process of detecting fraud in fingerprint images. Besides, we propose a new texture descriptor based on the well-known dense Scale-Invariant Feature Transform (SIFT): the statistical dense SIFT, in which their descriptors are summarized using a set of signal processing functions. The proposed methodology is evaluated in benchmarks of two editions of LivDet competitions, assuming competitive results in comparison to techniques that configure the state of the art of the problem.

关键词： Liveness detection Spoofing detection Fingerprint authentication system Dense SIFT pattern recognition

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Breast Cancer Diagnosis Using image processing and machine learning 1st

Breast Cancer Diagnosis Using Image Processing and Machine L...

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1st international conference on Emerging Technology in Modelling and Graphics, IEMGraph 2018

作者： Sadhukhan, Subham Upadhyay, Nityasree Chakraborty, Prerana Department of Computer Science & Engineering Institute of Engineering & Management Kolkata India

ISBN: (纸本)9789811374029

Breast cancer is one of the second leading causes of cancer death in women. Despite the fact that cancer is preventable and curable in primary stages, a huge number of patients are diagnosed with cancer very late. Conventional methods of detecting and diagnosing cancer mainly depend on skilled physicians, with the help of medical imaging, to detect certain symptoms that usually appear in the later stages of cancer. Therefore, we present here the computerized method for cancer detection in its early stage within a very short time. Here, we have used machine learning to train a model using the predicted features of the nuclei of cells. A comparative study of two different algorithms KNN and SVM is conducted where the accuracy of each classifier is measured. After this, we analyze a digital image of a fine needle aspirate (FNA) of breast tissue using image processing to find out the features of nuclei of the cells. We then apply the feature values to our trained model to find whether the tumor developed is benign or malignant. © Springer Nature Singapore Pte Ltd 2020.

关键词： Needles

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1st international conference on Applied Computing Research to Support Industry: Innovation and Technology, ACRIT 2019

1st International Conference on Applied Computing Research t...

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1st international conference on Applied Computing Research to Support Industry: Innovation and Technology, ACRIT 2019

ISBN: (纸本)9783030387518

The proceedings contain 39 papers. The special focus in this conference is on Applied Computing Research to Support Industry: Innovation and Technology. The topics include: Evaluation of Local Texture Descriptors for Eyebrow-Based Continuous Mobile User Authentication;deep learning Approach Based Dominant Age Group Based Classification for Social Network;a Novel Detection System for Human Retina Based on Fuzzification Neural Network;a Novel Car-Pooling Optimization Method Using Ant Colony Optimization Based on Network Analysis (Case study: Tehran);A Trusted MANET Routing Algorithm Based on Fuzzy Logic;Minimum Array Elements and Snapshots for Performance Analysis Based on Direction Of Arrival (DOA) Estimation Methods;experimenting Two machine learning Methods in Classifying River Water Quality;multi View Face Detection in Cattle Using Infrared Thermography;deep learning for Face Expressions Detection: Enhanced Recurrent Neural Network with Long Short Term Memory;Detection of Copy-Move Forgery in Digital image Based on SIFT Features and Automatic Matching Thresholds;the Application of Artificial Intelligence Technology in Healthcare: A Systematic Review;Gain Scheduling Fuzzy PID Controller for Distributed Control Systems;comparative Analysis for Arabic Sentiment Classification;political Articles Categorization Based on Different Naïve Bayes Models;single Runway Aircraft Landing Scheduling Using Simulated Annealing and Exact Timing Method;towards Smart Meter Energy Analysis and Profiling to Support Low Carbon Emissions;prediction of Compressive strength of High-Performance Concrete: Hybrid Artificial Intelligence Technique;cancellable Face Biometrics Template Using AlexNet;pattern of Diffusion recognition in a Molecular Communication Model;querying Encrypted Data in Graph Databases;an Effective Protein Multiple structure Alignment Using Parallel Computing.

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