The document compares the performance of different machine learning models for detecting COVID-19 from CT scans, including single models like SVM, NB, MLP, CNN and ensemble models like AdaBoost and GBDT. Based on accuracy, precision, recall, F1-score and MCC metrics, the SVM model achieved the best performance with an accuracy of 99.2%, followed by CNN and AdaBoost. While MLP, NB and GBDT showed lower performance, CNN had the advantage of automatically detecting important image features.
COVID-19 detection from scarce chest X-Ray image data using few-shot deep lea...Shruti Jadon
In the current COVID-19 pandemic situation, there is an urgent need to screen infected patients quickly and accurately. Using deep learning models trained on chest X-ray images can become an efficient method for screening COVID-19 patients in these situations. Deep learning approaches are already widely used in the medical community. However, they require a large amount of data to be accurate.
An Analysis of The Methods Employed for Breast Cancer Diagnosis IJORCS
Breast cancer research over the last decade has been tremendous. The ground breaking innovations and novel methods help in the early detection, in setting the stages of the therapy and in assessing the response of the patient to the treatment. The prediction of the recurrent cancer is also crucial for the survival of the patient. This paper studies various techniques used for the diagnosis of breast cancer. Different methods are explored for their merits and de-merits for the diagnosis of breast lesion. Some of the methods are yet unproven but the studies look very encouraging. It was found that the recent use of the combination of Artificial Neural Networks in most of the instances gives accurate results for the diagnosis of breast cancer and their use can also be extended to other diseases.
Developed Project with 3 more colleagues for Pneumonia Detection from Chest X-ray images using Convolutional Neural Network. Used confusion matrix, Recall, Precision for check the model performance on testing Data
Early detection of breast cancer using mammography images and software engine...TELKOMNIKA JOURNAL
The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.
COVID-19 detection from scarce chest X-Ray image data using few-shot deep lea...Shruti Jadon
In the current COVID-19 pandemic situation, there is an urgent need to screen infected patients quickly and accurately. Using deep learning models trained on chest X-ray images can become an efficient method for screening COVID-19 patients in these situations. Deep learning approaches are already widely used in the medical community. However, they require a large amount of data to be accurate.
An Analysis of The Methods Employed for Breast Cancer Diagnosis IJORCS
Breast cancer research over the last decade has been tremendous. The ground breaking innovations and novel methods help in the early detection, in setting the stages of the therapy and in assessing the response of the patient to the treatment. The prediction of the recurrent cancer is also crucial for the survival of the patient. This paper studies various techniques used for the diagnosis of breast cancer. Different methods are explored for their merits and de-merits for the diagnosis of breast lesion. Some of the methods are yet unproven but the studies look very encouraging. It was found that the recent use of the combination of Artificial Neural Networks in most of the instances gives accurate results for the diagnosis of breast cancer and their use can also be extended to other diseases.
Developed Project with 3 more colleagues for Pneumonia Detection from Chest X-ray images using Convolutional Neural Network. Used confusion matrix, Recall, Precision for check the model performance on testing Data
Early detection of breast cancer using mammography images and software engine...TELKOMNIKA JOURNAL
The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.
Agenda:
Introduction
Supercomputers for Scientific Research
Covid-19 Tracking and Prediction
Covid-19 Research and Diagnosis
Use Case 1 NLP and BERT to answer scientific questions
Use Case 2 Covid-19 Data Lake and Platform
Pneumonia Classification using Transfer LearningTushar Dalvi
Pneumonia can be life-threatening for people with weak immune systems, in which the alveoli filled with fluid that makes it hard to pass oxygen throughout the bloodstream. Detecting pneumonia is from a chest X-ray is not only expansive but also time-consuming for normal people. Throughout this research introduced a machine learning technique to classify pneumonia from Chest X-ray Images. Most of the medical datasets having class imbalance issues in the dataset. The Data augmentation technique used to reduce the class imbalance from the dataset, Horizontal Flip, width shift and height shift techniques used to complete the augmentation technique. Used VGG19 as a base architecture and ImageNet weights added for the transfer learning approach, also Removing initial layers and adding
some more dense layers helped to discover new possibilities. After testing the proposed model on testing data, we are able to achieve 98% recall and 82% of precision. As compare with state of the art technique, the proposed method able to achieve high
recall but that compromises with Precision.
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
Prospects of Deep Learning in Medical ImagingGodswll Egegwu
A SEMINAR Presentation on the Prospects of Deep Learning in Medical Imaging Presented to the Department of Computer Science, Nasarawa State Polytechnic, Lafia.
BY:
EGEGWU, GODSWILL
08166643792
http://facebook.com/godswill.egegwu
http://egegwugodswill.name.ng
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Breast cancer detection from histopathological images is done using deep learning and transfer learning techniques. Image processing is done for better accuracy. CNN and DenseNet-121 algorithms are used. 90.9 % accuracy is achieved using CNN and 88% accuracy is achieved using Transfer learning.
Medical image is an important parameter for diagnosis to many diseases. Now day’s
telemedicine is major treatment based on medical images. The World Health Organization
(WHO) established the Global Observatory for eHealth (GOe) to review the benefits that
Information and communication technologies (ICTs) can bring to health care and patients’
wellbeing. Securing medical images is important to protect the privacy of patients and assure
data integrity. In this paper a new self-adaptive medical image encryption algorithm is proposed
to improve its robustness. A corresponding size of matrix in the top right corner was created by
the pixel gray-scale value of the top left corner under Chebyshev mapping. The gray-scale value
of the top right corner block was then replaced by the matrix created before. The remaining
blocks were encrypted in the same manner in clockwise until the top left corner block was finally
encrypted. This algorithm is not restricted to the size of image and it is suitable to gray images
and color images, which leads to better robustness. Meanwhile, the introduction of gray-scale value diffusion system equips this algorithm with powerful function of diffusion and disturbance.
Meta analysis of convolutional neural networks for radiological images - PubricaPubrica
Deep Learning is an inevitable branch of Artificial Intelligence technology. In which, Convolutional Neural Network is a modern approach to visualize the images with high performance. These networks help for high performance in the recognition and categorization of images. It has found applications in the modern science sectors such as Healthcare, Bioinformatics, Pharmaceuticals, etc. for Meta-analysis Writing Services.
Full Information: https://bit.ly/3lrEt1C
Reference: https://pubrica.com/services/research-services/meta-analysis/
Why Pubrica?
When you order our services, we promise you the following – Plagiarism free, always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts.
Contact us :
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44- 74248 10299
What is Deep Learning and how it helps to Healthcare Sector?Cogito Tech LLC
To know what is Deep Learning and how it helps to Healthcare Sector check this presentation that shows the top use cases of deep learning process of this technology backed systems, applications or machines in the healthcare industry. The entire presentation shows the deep learning definition and how it is changing the healthcare industry. This PPT is represented by Cogito to get to know the role of deep learning in healthcare as Cogito is providing the training data sets for deep learning and machine learning with best accuracy.
Visit: http://bit.ly/2QRrSc2
Detecting malaria using a deep convolutional neural networkYusuf Brima
Experiment with Deep Residual Convolutional Neural Network to classify microscopic blood cell images (Uninfected, Parasitized)
Utiling ResNet,Deep Residual Learning for Image Recognition (He et al, 2015) architecture.
Uses Keras with a Tensorflow backend.
Agenda:
Introduction
Supercomputers for Scientific Research
Covid-19 Tracking and Prediction
Covid-19 Research and Diagnosis
Use Case 1 NLP and BERT to answer scientific questions
Use Case 2 Covid-19 Data Lake and Platform
Pneumonia Classification using Transfer LearningTushar Dalvi
Pneumonia can be life-threatening for people with weak immune systems, in which the alveoli filled with fluid that makes it hard to pass oxygen throughout the bloodstream. Detecting pneumonia is from a chest X-ray is not only expansive but also time-consuming for normal people. Throughout this research introduced a machine learning technique to classify pneumonia from Chest X-ray Images. Most of the medical datasets having class imbalance issues in the dataset. The Data augmentation technique used to reduce the class imbalance from the dataset, Horizontal Flip, width shift and height shift techniques used to complete the augmentation technique. Used VGG19 as a base architecture and ImageNet weights added for the transfer learning approach, also Removing initial layers and adding
some more dense layers helped to discover new possibilities. After testing the proposed model on testing data, we are able to achieve 98% recall and 82% of precision. As compare with state of the art technique, the proposed method able to achieve high
recall but that compromises with Precision.
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
Prospects of Deep Learning in Medical ImagingGodswll Egegwu
A SEMINAR Presentation on the Prospects of Deep Learning in Medical Imaging Presented to the Department of Computer Science, Nasarawa State Polytechnic, Lafia.
BY:
EGEGWU, GODSWILL
08166643792
http://facebook.com/godswill.egegwu
http://egegwugodswill.name.ng
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Breast cancer detection from histopathological images is done using deep learning and transfer learning techniques. Image processing is done for better accuracy. CNN and DenseNet-121 algorithms are used. 90.9 % accuracy is achieved using CNN and 88% accuracy is achieved using Transfer learning.
Medical image is an important parameter for diagnosis to many diseases. Now day’s
telemedicine is major treatment based on medical images. The World Health Organization
(WHO) established the Global Observatory for eHealth (GOe) to review the benefits that
Information and communication technologies (ICTs) can bring to health care and patients’
wellbeing. Securing medical images is important to protect the privacy of patients and assure
data integrity. In this paper a new self-adaptive medical image encryption algorithm is proposed
to improve its robustness. A corresponding size of matrix in the top right corner was created by
the pixel gray-scale value of the top left corner under Chebyshev mapping. The gray-scale value
of the top right corner block was then replaced by the matrix created before. The remaining
blocks were encrypted in the same manner in clockwise until the top left corner block was finally
encrypted. This algorithm is not restricted to the size of image and it is suitable to gray images
and color images, which leads to better robustness. Meanwhile, the introduction of gray-scale value diffusion system equips this algorithm with powerful function of diffusion and disturbance.
Meta analysis of convolutional neural networks for radiological images - PubricaPubrica
Deep Learning is an inevitable branch of Artificial Intelligence technology. In which, Convolutional Neural Network is a modern approach to visualize the images with high performance. These networks help for high performance in the recognition and categorization of images. It has found applications in the modern science sectors such as Healthcare, Bioinformatics, Pharmaceuticals, etc. for Meta-analysis Writing Services.
Full Information: https://bit.ly/3lrEt1C
Reference: https://pubrica.com/services/research-services/meta-analysis/
Why Pubrica?
When you order our services, we promise you the following – Plagiarism free, always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts.
Contact us :
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44- 74248 10299
What is Deep Learning and how it helps to Healthcare Sector?Cogito Tech LLC
To know what is Deep Learning and how it helps to Healthcare Sector check this presentation that shows the top use cases of deep learning process of this technology backed systems, applications or machines in the healthcare industry. The entire presentation shows the deep learning definition and how it is changing the healthcare industry. This PPT is represented by Cogito to get to know the role of deep learning in healthcare as Cogito is providing the training data sets for deep learning and machine learning with best accuracy.
Visit: http://bit.ly/2QRrSc2
Detecting malaria using a deep convolutional neural networkYusuf Brima
Experiment with Deep Residual Convolutional Neural Network to classify microscopic blood cell images (Uninfected, Parasitized)
Utiling ResNet,Deep Residual Learning for Image Recognition (He et al, 2015) architecture.
Uses Keras with a Tensorflow backend.
Development of Computational Tool for Lung Cancer Prediction Using Data MiningEditor IJCATR
The requirement for computerization of detection of lung cancer disease arises ever since recent-techniques which involve
manual-examination of the blood smear as the first step toward diagnosis. This is quite time-consuming, and their accurateness depends
upon the ability of operator's. So, prevention of lung cancer is very essential. This paper has surveyed various techniques used by previous
authors like ANN (Artificial Neural Network), image processing, LDA (Linear Dependent Analysis), SOM (Self Organizing Map) etc.
Computer Aided System for Detection and Classification of Breast CancerIJITCA Journal
Breast cancer is one of the most important causes of death among all type of cancers for grown-up and
older women, mainly in developed countries, and its rate is rising. Since the cause of this disease is not yet
known, early detection is the best way to decrease the breast cancer mortality. At present, early detection of
breast cancer is attained by means of mammography. An intelligent computer-aided diagnosis system can
be very helpful for radiologist in detecting and diagnosing cancerous cell patterns earlier and faster than
typical screening programs. This paper proposes a computer aided system for automatic detection and
classification of breast cancer in mammogram images. Intuitionistic Fuzzy C-Means clustering technique
has been used to identify the suspicious region or the Region of Interest automatically. Then, the feature
data base is designed using histogram features, Gray Level Concurrence wavelet features and wavelet
energy features. Finally, the feature database is submitted to self-adaptive resource allocation network
classifier for classification of mammogram image as normal, benign or malignant. The proposed system is
verified with 322 mammograms from the Mammographic Image Analysis Society Database. The results
show that the proposed system produces better results.
Breast cancer detection using ensemble of convolutional neural networksIJECEIAES
Early detection leading to timely treatment in the initial stages of cancer may decrease the breast cancer death rate. We propose deep learning techniques along with image processing for the detection of tumors. The availability of online datasets and advances in graphical processing units (GPU) have promoted the application of deep learning models for the detection of breast cancer. In this paper, deep learning models using convolutional neural network (CNN) have been built to automatically classify mammograms into benign and malignant. Issues like overfitting and dataset imbalance are overcome. Experimentation has been done on two publicly available datasets, namely mammographic image analysis society (MIAS) database and digital database for screening mammography (DDSM). Robustness of the models is accomplished by merging the datasets. In our experimentation, MatConvNet has achieved an accuracy of 94.2% on the merged dataset, performing the best amongst all the CNN models used individually. Hungarian optimization algorithm is employed for selection of individual CNN models to form an ensemble. Ensemble of CNN models led to an improved performance, resulting in an accuracy of 95.7%.
covid 19 detection using lung x-rays.pptx.pptxDiya940551
Chest CT is emerging as a valuable diagnostic tool for the clinical management of COVID-19-associated lung disease. Artificial intelligence (AI) has the potential to aid in the rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities.
COVID-19 (Coronavirus Disease) Outbreak Prediction Using a Susceptible-Exposed-Symptomatic Infected-Recovered-Super Spreaders-Asymptomatic Infected-Deceased-Critical (SEIR-PADC) Dynamic Model
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Rapid COVID-19 Diagnosis Using Deep Learning of the Computerized Tomography Scans
1. Presented by Amir Mosavi
Rapid COVID-19 Diagnosis Using Deep Learning of
the Computerized Tomography Scans
2. The advantages and disadvantages of mentioned machine leaning
Models Advantages Disadvantages
Single models
SVM
Effective performance in high
dimensional spaces
Doesn't perform well with large data set
(required high trainingtime)
NB
Easy and fast class prediction in test
datasets
Need to calculate the
prior
probability
MLP
Adaptive learning, coefficients can
easily beadapted
It requires much more training data than
traditional
machine learning algorithms
CNN
Automatically detects
the important features
(Feature
Extraction), uses convolution of
image and filters to generate
invariant features
Unexplained functioning
of the network (Black box),
require
processors with parallel processing power
Ensemble models
AdaBoost
Less susceptible
to the overfitting problem than most
learning algorithms
Using too weak classifiers can lead to low
margins and overfitting
GBDT
Lots of
flexibility (can optimize
on
different loss
functions)
Too much
improvement to minimize all errors can
overemphasize outliers and cause
overfitting
4. Rapid COVID-19 Diagnosis Using Deep Learning
of the Computerized Tomography Scans
Hamed Tabrizchi
Department of Computer Science,
Shahid Bahonar University of Kerman
Kerman, Iran
0000-0001-9250-2232
Imre Felde
John von Neumann Faculty of
Infromatics, Obuda University
Budapest, Hungary
felde@uni-obuda.hu
Amir Mosavi *
School of Economics and Business
Norwegian University of Life Sciences
1430 Ås, Norway
amir.mosavi@kvk.uni-obuda.hu
Laszlo Nadai
Kalman Kando Faculty of Electrical
Engineering, Obuda University
Budapest, Hungary
nadia@uni-obuda.hu
Akos Szabo-Gali
John von Neumann Faculty of
Infromatics, Obuda University
Budapest, Hungary
szabogaliakos@stud.uni-obuda.hu
Abstract— Several studies suggest that COVID-19 may be
accompanied by symptoms such as a dry cough, muscle aches,
sore throat, and mild to moderate respiratory illness. The
symptoms of this disease indicate the fact that COVID-19 causes
noticeable negative effects on the lungs. Therefore, considering
the health status of the lungs using X-rays and CT scans of the
chest can significantly help diagnose COVID-19 infection. Due
to the fact that most of the methods that have been proposed to
COVID-19 diagnose deal with the lengthy testing time and also
might give more false positive and false negative results, this
paper aims to review and implement artificial intelligence (AI)
image-based diagnosis methods in order to detect coronavirus
infection with zero or near to zero false positives and false
negatives rates. Besides the already existing AI image-based
medical diagnosis method for the other well-known disease, this
study aims on finding the most accurate COVID-19 detection
method among AI methods such as machine learning (ML) and
artificial neural network (ANN), ensemble learning (EL)
methods.
Keywords— COVID-19, image-based diagnosis, artificial
intelligence, machine learning, deep learning, computerized
tomography, coronavirus disease,
I. INTRODUCTION
COVID-19 is a global pandemic that collapsed the
healthcare systems in most countries. In the year 2020, people
all over the world witnessed the news of the death of their
fellow human beings from many world news agencies.
Furthermore, this pandemic event has affected the operations
of healthcare facilities. The medical centres witnessed
increases in patients who are needing care for a respiratory
illness that could be COVID-19 (+) or COVID-19 (-). The
World Health Organization (WHO) advises that all countries
to consider the importance of the test because the isolation of
all confirmed cases and also mild cases in health centers is
able to prevent transmission and provide acceptable care. One
of the pivotal reasons for the need to use intelligent systems in
the process of diagnosing this disease (taste) is the easy
transmission of this disease among people in a community or
even health facilities [1,2]. Since most of the excited test
needs a lot of time to generate the result compared to the time
for spreading virus among people, chest X-Ray or Computer
Tomography (CT) scan images of COVID19 is used to
provide a rapid and efficient way to test the COVID-19
suspected individuals. It is an undeniable fact that artificial
intelligence plays a central role in making human daily life
more convenient than the past. The advantage of AI methods
is their ability to interpret and understand the digital images in
order to identify and classify objects. For this reason, many
researchers in the world of artificial intelligence have drawn
attention to research on the data obtained from patients who
infected with COVID-19. Sachin Sharma [3] presents a study
that aims to discuss the importance of machine learning
methods to distinguish COVID-19 infected regarding their
lung CT scan images. Nripendra Narayan Das et al. [4] use
chest X-rays in order to find some radiological signatures of
COVID-19 by using deep learning of the chest CT scans.
Aayush Jaiswal et al. [5] use the pre-trained deep learning
architectures (DenseNet201) along with deep transfer learning
in order to provide an automated tool that aims to detect
COVID-19 positive and negative infected patients based on
chest CT images. Xueyan Mei et al. [6] combine chest CT
records including the patients’ essential symptoms. In this
pioneer research the interaction between the chest CT and the
clinical symptoms is conducted through basic machine
learning methods, i.e., SVM, random forest, MLP, and deep
learning to accurately predict COVID-19. In an alternative
approach, Pinter et al. [7] present the hybrid machine learning
method of ANFIS and MLP to predict mortality rate of
COVID-19 patients. Sina F. Ardabili et al. [8] review a wide
range of machine learning models to forecast the COVID-19
outbreak. Their study presents a number of suggestions to
demonstrate the potential of machine learning for future
research.
5. In a nutshell, the main motivation of this paper is to find
the most accurate intelligent approach for detecting COVID-
19. In other words, we use state-of-the-art learning models in
order to classify positive and negative COVID-19 suspected
individuals with regard to their captured chest X-Ray or CT
scan images.
The rest of this paper is outlined as follows. Section 2
reviews Machine leaning-based models. Section 3 compares
the performances of the described and implemented machine
learning models. Finally, Section 4 draws conclusions and
offers some suggestions for the end-users of medical
intelligent systems.
II. BRIEF REVIEW OF MACHINE LEANING-BASED MODELS
AI has the potential to improve medical imaging
capabilities and patient diagnosis. Using ML, ANN, and
ensemble learning methods for medical image recognition is
a core component of computer vision in this widespread study
area. ML methodology works based on the cognitive learning
methods to advance an intelligent code without use of
conventional programing techniques. The performance of
ML algorithms’ is associated with other mathematical
techniques and improved by experience [4]. Generally, ML
uses historical data to make decisions and uncover hidden
insights [5]. In image-based diagnosis problems, the ML
models are advanced to be able to learn from medical records.
This process is often done through developing insight into the
patterns within complex imaging [6]. The following
subsection describes the basic and ensemble AI-based image
classifier methods in a brief way.
A. Singlemodels
In the following, the basic classifiers employed for
diagnosing COVID-19 are introduced.
Support Vector Machine (SVM) is one of the commonly-
used algorithms in research and industry, taking its power
from machine learning algorithms. The main advantage
of this algorithm is its ability to deal with non-linear
problems. SVM can be used to solve nonlinear
classification problems by transforming the problem
using the kernel method which makes SVM calculation in
the higher dimension. Vapnik was first introduced SVM
in 1995[]. He used the Statistic Learning Theory (SLT)
and Structural Risk Minimization (SRM) to introduce this
concept. SVM can be effectively employed in
classification, regression, and nonlinear function
approximation problems [9,10].
Naive Bayes (NB) is a well-known probabilistic
classification algorithm that applies Bayes' Theorem with
an assumption of strong (naive) independence among
predictors (a set of supervised learning algorithms).
During the process of constructing classifiers (training),
the NB model needs a small amount of training data to
estimate the vital parameters [11]. In other words, the
previous probability of each class is estimated by
calculating the conditional probability density function
and the posterior probability. Eventually, the final
prediction is made for the class that has the largest
posterior probability.
Artificial neural networks (ANN) are computing systems
that widely used for image-based medical diagnosis
problems. In fact, ANN draws inspiration from biological
neural systems and creates an interconnected network of
‘neurons that process information. These models consist
of several processing elements that reproduce input data
in a hierarchical structure. During the training process, a
corresponding weight (for each input data) must be
iteratively estimated and adjusted. Due to the variation of
connections between layers in an ANN, the architecture
of networks is able to design variously. Deciding the
number of layers and nodes in each layer depends on the
problem and the amount of training data. For this reason,
ANN is a great (flexible) option to deal with different
classification, regression, and clustering problems
[12,13].
Multilayer perceptron (MLP) is a well-known ANN in
which neurons are distributed in thoroughly connected
layers. These layers are divided into three groups: input
layers, output layers, and hidden layers. The weighted
inputs are linearly combined by their corresponding
neuron; then, the results are transferred through a
nonlinear activation function. Usually, a gradient-descent
algorithm called back-propagation is used to train an
MLP. In this algorithm, a maximum error is defined to be
used as a criterion to stop the iterative weight update
process [14].
CNN is a deep neural network that is commonly applied to
process large scale images. As same as the other ANN,
CNN is a network that includes several layers. In CNN
represents a sequential connection between the layers. As
the output of the previous layer is interconnected with the
input of other layer. However, unlike the other fully
connected neural network, in this network, the neurons in
one layer do not connect to all the neurons in the next
layer. The main powerful part of CNN is the convolution
layer [15,16].
B. Ensemblemodels
Ensemble models are able to scale up the performance of
classification and regression processes. Boosting and Bagging
are the most widely-used ensemble learning frameworks in
science literature. Bagging ensembles create subsets and
ensemble estimates using Bootstrap re-sampling and a mean
combiner respectively. Boosting ensemble models train a
number of individual models in a sequential way. A way that
provides an opportunity for each model to learns from
mistakes made by the previous model [17].
AdaBoost (Adaptive Boosting) is an ensemble learning
algorithm that can be used in conjunction with many other
types of learning algorithms to improve performance.
AdaBoost initially created to enhance the performance of
binary classifiers. The main idea of AdaBoost is about
using an iterative approach in order to learn from the
mistakes of weak classifiers, and turn them into strong
ones. In fact, AdaBoost learns from the mistakes by
increasing the weight of misclassified data points [18].
Gradient boosting decision tree (GBDT) is an ML
algorithm, which produces a prediction model in the form
of an ensemble of weak prediction models (decision
trees). Gradient Boosting learns from the residual error
(directly), rather than update the weights of data points
[19].
6. In a nutshell, Table I indicates the advantages and
disadvantages of all mentioned machine leaning models.
TABLE I. ADVANTAGES AND DISADVANTAGES OF ML MODELS
Models Advantages Disadvantages
Single models
SVM
Effective
performance in
high
dimensional
spaces
Doesn't perform
well with large data
set (required high
trainingtime)
NB
Easy and fast
class prediction
in test datasets
Need to calculate
the prior
probability
MLP
Adaptive
learning,
coefficients can
easily beadapted
It requires much
more training data
than traditional
machine learning
algorithms
CNN
Automatically
detects the
important
features (Feature
Extraction), uses
convolution of
image and filters
to generate
invariant
features
Unexplained
functioning of
the network (Black
box), require
processors with
parallel processing
power
Ensemble
models
AdaBoost
Less susceptible
to the overfitting
problem than
most learning
algorithms
Using too weak
classifiers can lead
to low margins and
overfitting
GBDT
Lots of
flexibility (can
optimize on
different loss
functions)
Too much
improvement to
minimize all errors
can overemphasize
outliers and cause
overfitting
III. EXPERIMENTALRESULTS
In this section, all described models in the previous section
were evaluated and examined based on the datasets that
include image data on 980 patients suspected with COVID-19
infection. The implementation is facilitated under Python
using Scikit-Learn and Keras libraries. The experimental
results are provided and analyzed in detail by using a standard
CPU with the information of Intel Core i5-2.20 GHz with 16
GB RAM.
A. Data description
This paper evaluates all described models mentioned in the
previous section based on two datasets. The first data set
includes image data on 430 patients infected with COVID-19.
Also, 550 healthy (normal) individuals were randomly
selected from the second data set [20]. The following Figure
illustrates sample images of both classes.
B. Performance evaluation
In the presented study, we split the data images into a
training and testing image set. We use 75% of the data as the
training data for training the model, the next 25% remaining
data were used as testing data. Moreover, all considered
models were evaluated by taking advantage of the well-known
performance criteria and Matthews correlation coefficient
(MCC) [21,22].
Fig. 1. X-ray images of normal and COVID-19 caused patient
According to the confusion matrix, the formulas for these
measurements are described briefly as follows.
Accuracy = TP+TN
TP+FN+FP+TN
(1)
Precision = TP
T P+FP
(2)
Recall = T P
T P+FN
(3)
F1 − score = 2×P×R
P+R
(4)
MCC = T P×T N–FP×FN
ƒ( T P+FP)(T P+FN)(T N+FP)(T N+FN)
(5)
where FN and Fp present the quantity of the incorrect
predictions respectively. TP and TN indicate the quantity of
correct predictions.
The results of the implemented machine learning
algorithms evaluated using the datasets described earlier.
Table. II presents the performance of trained models with
regard to the mentioned standard performance criteria.
7. TABLE II. PERFORMANCE COMPARISON BETWEEN COVID-
19 DETECTIONMODELS
Model
Artificial Neural
Network
Machine learning Ensemble learning
MLP CNN SVM NB AdaBoost GBDT
Accuracy 0.9400 0.9760 0.9920 0.9400 0.9600 0.9520
Precision 0.9895 0.9724 0.9819 0.9122 0.9459 0.9145
Recall 0.8715 0.9724 1.000 0.9541 0.9633 0.9816
F1-score 0.9268 0.9724 0.990 0.9327 0.9545 0.9469
MCC 0.8814 0.9512 0.9838 0.8793 0.9189 0.9050
In our experiment, each test set is executed with six
different machine learning algorithms. By using the values in
the confusion matrix, 5 different statistics (in Equations. (1–
5)) are calculated to measure the efficiency of the algorithms.
In this study, the architecture of MLP designed as five fully
connected layers with 350, 250,150,50 and 2 number of
neurons in each layer, respectively. Except for the last layer,
the rectified linear unit (Relu) used in considered MLP
network and the last layer use "softmax" activation, which
means it will return an array of 2 probability scores (Positive
or Negative).
In addition, the architecture of CNN designed as four
convolution layers with a convolution kernel size of 7x7 to
extract the features and each them uses 3x3 average pooling
layer or max pooling to prevent the features. The number of
convolution in each convolution layer is at least 64. After the
max pooling layer, two 512-dimensional fully connected
layer are added, along with dropout layer in order to prevent
overfitting problem. In Table II, the results show that SVM
outperforms the other models. The Table includes the
performance of CNN which has provided a 97% accuracy
rate. In addition, it is reported an 99% accuracy for the SVM
model. Sine image-based diagnosis SVM model has to deal
with nonlinear pattern of data, we considered RBF kernel for
SVM. Furthermore, the confusion matrix for the considered
learning models is constructed in Table III.
Table. III. CONFUSION MATRIX
Model Confusion matrix
Predicted
P N
MLP Actual
P 140 1
N 14 95
CNN Actual
P 138 3
N 3 106
SVM Actual
P 139 2
N 0 109
NB Actual
P 131 10
N 5 104
AdaBoost Actual
P 131 10
N 2 107
GBDT Actual
P 135 6
N 4 105
IV. CONCLUSIONS
Rapid diagnosis of COVID-19 symptoms is of utmost
importance. This paper implemented COVID-19 detection
models by using six different machine learning algorithms, as
SVM, NB, GBDT, AdaBoost, CNN and MLP based on the
datasets that include image data on 980 patients suspected
with COVID-19 infection. The main purpose of this study was
to introducing image-based ML methods for developers and
end-users of intelligent medical systems in a comprehensive
way. We compared the performance of the state-of-the-art
models to show that how important is the ML image-based
models reliability to diagnose diseases. The experimental
results and discussions proved that the SVM with RBF kernel
outperforms other existing methods.
ACRONYMS
AI
ANN
CT
CNN
EL
GBDT
ML
MCC
MLP
NB
SLT
SRM
SVM
WHO
Artificial intelligence
Artificial neural
networks
Computer Tomography
Convolutional neural
networks
Ensemble learning
Gradient boosting
decision tree
Machine learning
Matthews correlation
coefficient
Multilayer perceptron
Naive Bayes
Statistic Learning Theory
Structural Risk
Minimization
Support Vector Machine
World Health
Organization
ACKNOWLEDGMENT
We acknowledge the financial support of this work by the
Hungarian State and the European Union under the EFOP-
3.6.1-16-2016-00010 project and the 2017-1.3.1-VKE-2017-
00025 project. The research presented in this paper was
carried out as part of the EFOP-3.6.2-16-2017-00016 project
in the framework of the New Szechenyi Plan. The completion
of this project is funded by the European Union and co-
financed by the European Social Fund. We acknowledge the
financial support of this work by the Hungarian State and the
European Union under the EFOP-3.6.1-16-2016-00010
project. We acknowledge the financial support of this work
by the Hungarian-Mexican bilateral Scientific and
Technological (2019-2.1.11-TÉT-2019-00007) project. The
support of the Alexander von Humboldt Foundation is
acknowledged.
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