The Cancer Imaging Archive (TCIA) is a service which de-identifies and hosts a large archive of medical images of cancer accessible for public download.
- Update on new data sets
- New features
- New publications
- Other news
Track 6. Technological innovations in biomedical training and practice
Authors: Jesús M Gonçalves, M J Sanchez-Ledesma, P Ruisoto, M Jaramillo, J J Jimenez and J A Juanes
Enhancement of clinical outcome using OBI and Cone Beam CT in Radiotherapydrsumandas
Improving the quality of radiation treatment by use of on board image Guidance (OBI) with KV Xray and CBCT. This decreases the variability in daily dose delivery and improves outcome.
CyberKnife: Radiosurgery System Introductionduttaradio
Radiation source is mounted on a precisely controlled industrial robot.
- Image guidance system(continuous tracking system)
- Eliminates the need of gating techniques and restrictive head frames
Performance analysis of automated brain tumor detection from MR imaging and CT scan using basic image processing techniques based on various hard and soft computing has been performed in our work. Moreover, we applied six traditional classifiers to detect brain tumor in the images. Then we applied CNN for brain tumor detection to include deep learning method in our work. We compared the result of the traditional one having the best accuracy (SVM) with the result of CNN. Furthermore, our work presents a generic method of tumor detection and extraction of its various features.
Diagnostic Efficacy of Ultra-High-Field MRS in Glioma PatientsUzay Emir
Synopsis: We have recently initiated a 7T MRS glioma consortium, intending to bring together experts in the field to discuss pitfalls, promises, and potential research avenues of MRS in gliomas. During the "GlioMaRS-NET Workshop" in 2020, we tested the efficacy of UHF MRS for predicting the molecular characteristics by visual inspection by conducting a survey with a set of previously acquired UHF spectra from glioma patients. In the post-genomics era, the World Health Organization (WHO) classification of gliomas has become even more tightly integrated with molecular parameters in addition to histology. Integrated diagnoses offer prospects for precision medicine strategies tailoring therapies for each individual. The International Society of Neuropathology-Haarlem Consensus has proposed the personalized layered diagnosis for gliomas that merge all of the distinct but related information. For the final diagnosis, the concept of integrated diagnosis (Layer 1) relies on all available data, which include the histological classification (Layer 2, e.g., astrocytoma vs. oligodendroglioma), the grading (Layer 3, e.g., WHO grade II vs. III), and molecular characteristics (Layer 4, e.g., isocitrate dehydrogenase (IDH)-mutant, 1p/19q-codeletion) (1). Thus, the personalized layered diagnosis approach has emphasized to tailor the diagnostic imaging to contribute to the integrated diagnosis by discovering reliable imaging biomarkers and tools capable of differential diagnosis according to genetic subtypes. It has recently been shown that definitional features of glioma, such as IDH mutation and 1p/19q codeletion (Layer 4), can be identified with non-invasive MRS at 3T, opening up exciting new opportunities for diagnostics, clinical trials, and assessment of treatments (2,3). Ultra-high-field (UHF, >=7T) MRI scanners offer enhanced detection relative to routine 3T MRI of 2-HG peaks in the MRS spectra of IDH mutated patients (4,5). This is due to increased SNR at the higher field strength and increased spectral dispersion, which can improve the delineation of 2HG from neighboring metabolites such as glutamate and glutamine. Furthermore, at 7T, the 2-HG peak is not only to contribute to layer 4 of the integrated diagnosis by identifying molecular features but also to detect subtle changes due to the reprogramming of cellular metabolism during the disease progression or treatment. With the recent successes of 2-HG and increased use of UHF MRI in clinical settings as a basis, we have recently initiated a 7T MRS glioma consortium, intending to bring together experts in the field to discuss pitfalls, promises, and potential research avenues of MRS in gliomas. During the "Multi-center 7T Glioma Consortium (GlioMaRS-NET) Workshop" held on 16-20 Novembe
Track 6. Technological innovations in biomedical training and practice
Authors: Jesús M Gonçalves, M J Sanchez-Ledesma, P Ruisoto, M Jaramillo, J J Jimenez and J A Juanes
Enhancement of clinical outcome using OBI and Cone Beam CT in Radiotherapydrsumandas
Improving the quality of radiation treatment by use of on board image Guidance (OBI) with KV Xray and CBCT. This decreases the variability in daily dose delivery and improves outcome.
CyberKnife: Radiosurgery System Introductionduttaradio
Radiation source is mounted on a precisely controlled industrial robot.
- Image guidance system(continuous tracking system)
- Eliminates the need of gating techniques and restrictive head frames
Performance analysis of automated brain tumor detection from MR imaging and CT scan using basic image processing techniques based on various hard and soft computing has been performed in our work. Moreover, we applied six traditional classifiers to detect brain tumor in the images. Then we applied CNN for brain tumor detection to include deep learning method in our work. We compared the result of the traditional one having the best accuracy (SVM) with the result of CNN. Furthermore, our work presents a generic method of tumor detection and extraction of its various features.
Diagnostic Efficacy of Ultra-High-Field MRS in Glioma PatientsUzay Emir
Synopsis: We have recently initiated a 7T MRS glioma consortium, intending to bring together experts in the field to discuss pitfalls, promises, and potential research avenues of MRS in gliomas. During the "GlioMaRS-NET Workshop" in 2020, we tested the efficacy of UHF MRS for predicting the molecular characteristics by visual inspection by conducting a survey with a set of previously acquired UHF spectra from glioma patients. In the post-genomics era, the World Health Organization (WHO) classification of gliomas has become even more tightly integrated with molecular parameters in addition to histology. Integrated diagnoses offer prospects for precision medicine strategies tailoring therapies for each individual. The International Society of Neuropathology-Haarlem Consensus has proposed the personalized layered diagnosis for gliomas that merge all of the distinct but related information. For the final diagnosis, the concept of integrated diagnosis (Layer 1) relies on all available data, which include the histological classification (Layer 2, e.g., astrocytoma vs. oligodendroglioma), the grading (Layer 3, e.g., WHO grade II vs. III), and molecular characteristics (Layer 4, e.g., isocitrate dehydrogenase (IDH)-mutant, 1p/19q-codeletion) (1). Thus, the personalized layered diagnosis approach has emphasized to tailor the diagnostic imaging to contribute to the integrated diagnosis by discovering reliable imaging biomarkers and tools capable of differential diagnosis according to genetic subtypes. It has recently been shown that definitional features of glioma, such as IDH mutation and 1p/19q codeletion (Layer 4), can be identified with non-invasive MRS at 3T, opening up exciting new opportunities for diagnostics, clinical trials, and assessment of treatments (2,3). Ultra-high-field (UHF, >=7T) MRI scanners offer enhanced detection relative to routine 3T MRI of 2-HG peaks in the MRS spectra of IDH mutated patients (4,5). This is due to increased SNR at the higher field strength and increased spectral dispersion, which can improve the delineation of 2HG from neighboring metabolites such as glutamate and glutamine. Furthermore, at 7T, the 2-HG peak is not only to contribute to layer 4 of the integrated diagnosis by identifying molecular features but also to detect subtle changes due to the reprogramming of cellular metabolism during the disease progression or treatment. With the recent successes of 2-HG and increased use of UHF MRI in clinical settings as a basis, we have recently initiated a 7T MRS glioma consortium, intending to bring together experts in the field to discuss pitfalls, promises, and potential research avenues of MRS in gliomas. During the "Multi-center 7T Glioma Consortium (GlioMaRS-NET) Workshop" held on 16-20 Novembe
Project report 3D visualization of medical imaging dataShashank
Report of my engineering research on 3D visualisation of medical images obtained from slices of human male and female cadevars. Courtesy NIH (USA), IIIT (Allahabad)
Application of 3D Modeling for Preoperative Planning and Intra Operative Navigation during Procedures on the Organs of Retroperitoneal Space by Alexey A Rozhentsov in Experimental Techniques in Urology & Nephrology
Learn more: https://www.brainlab.com/surgery-products
Abstract
Introduction “Navigation in surgery” spans a broad area, which, depending on the clinical challenge, can have different meanings. Over the past decade, navigation in surgery has evolved beyond imaging modalities and bulky systems into the rich networking of the cloud or devices that are pocket-sized.
Discussion
This article will review various aspects of navigation in the operating room and beyond. This includes a short history of navigation, the evolution of surgical navigation, as well as technical aspects and clinical benefits with examples from neurosurgery, spinal surgery, and orthopedics.
Conclusion
With improved computer technology and a trend towards advanced information processing within hospitals, navigation is quickly becoming an integral part in the surgical routine of clinicians.
Excerpt:
Over the last three decades, technical advances have significantly changed the way we live. From computers to smartphones, from single purpose to multipurpose devices, technology has become an intrinsic part of our daily routine. Navigation in surgery is an important example of today’s technological capabilities being applied to medicine. It has emerged as one of the most reliable representatives of technology as it continues to transform surgical interventions into safer and less invasive procedures. In surgery, navigation has spurred technical progress, enabled more daring procedures, and unlocked new synergies. What was once a simple localization tool has evolved into a centerpiece of technology in the surgical theater.
“Navigation in surgery” spans a broad area, which, depending on the clinical challenge, may have various interpretations. The meaning of navigation in surgery is most accurately defined by the questions posed: “Where is my (anatomical) target?”, “How do I reach my target safely?”, “Where am I (anatomically)?”, or “Where and how shall I position my implant?”. Apart from these important anatomical orientation questions, surgical navigation is also used as a measurement tool and an information center for providing surgeons with the right information at the right time.
There are examples of technological advances in the medical field, whose benefit to the patient became immediately evident which were rapidly adopted and integrated into the clinical routine—without the need for proper randomized clinical trials. Examples range from the introduction of anesthesia to enable safer surgery and the introduction of microscopy enabling microsurgery. Surgical navigation and its wide range of benefits could be next.
Brain tumor detection and localization in magnetic resonance imagingijitcs
A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the
surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death and
responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate
in the world. Great knowledge and experience on radiology are required for accurate tumor detection in
medical imaging. Automation of tumor detection is required because there might be a shortage of skilled
radiologists at a time of great need. We propose an automatic brain tumor detectionand localization
framework that can detect and localize brain tumor in magnetic resonance imaging. The proposed brain
tumor detection and localization framework comprises five steps: image acquisition, pre-processing, edge
detection, modified histogram clustering and morphological operations. After morphological operations,
tumors appear as pure white color on pure black backgrounds. We used 50 neuroimages to optimize our
system and 100 out-of-sample neuroimages to test our system. The proposed tumor detection and localization
system was found to be able to accurately detect and localize brain tumor in magnetic resonance imaging.
The preliminary results demonstrate how a simple machine learning classifier with a set of simple
image-based features can result in high classification accuracy. The preliminary results also demonstrate the
efficacy and efficiency of our five-step brain tumor detection and localization approach and motivate us to
extend this framework to detect and localize a variety of other types of tumors in other types of medical
imagery.
Brain tumor classification using artificial neural network on mri imageseSAT Journals
Abstract
In this paper, an attempt has been made to summarize the multi-resolution transformation and the different classifiers useful to
analyze the brain tumor using MRI. X-ray, MRI, Ultrasound etc. are different techniques used to scan brain tumor images.
Radiologist prefers MRI to get detail information about tumor to help him diagnoses. In this paper we have used MRI of brain
tumor for analysis. We have used Digital image processing tool for detection of the tumor. The identification, detection and
classification of brain tumor have been done by extracting features from MRI with the help of wavelet transformation. The MRI of
brain tumor is classified into two categories normal and abnormal brain. In this work Digital image processing has been used as
a tool for getting clear and exact details about tumor in earlier stages. This helps the physicians and practitioners for diagnoses.
Key word – Brain tumor, Wavelet transform, segmentation.
Learn more: https://www.brainlab.com/spinal-navigation
Brainlab Spinal Navigation combines state-of-the-art touch screen based image control with best-in-class registration methods for image-guided surgery. As an open navigation platform, Brainlab Spinal Navigation enables accurate pedicle screw placement as well as drastic reduction of X-Ray exposure to both the surgical team and the patient. Navigation of implants and instruments is possible in 2D images, 3D fluoroscopy scans, MR or CT datasets in all stages of surgery—from incision planning to implant placement.
Lung cancer is one of the leading
causes of mortality in every country, affecting
both men and women. Lung cancer has a low
prognosis, resulting in a high death rate. The
computing sector is fully automating it, and the
medical industry is also automating itself with the
aid of image recognition and data analytics. Lung
cancer is one of the most common diseases for
human beings everywhere throughout the world.
Lung cancer is a disease which arises due to growth
of unwanted tissues in the lung and this growth
which spreads beyond the lung are named as
metastasis which spreads into other parts of the
body.
The objective of our project is to inspect accuracy
ratio of two classifiers which is Support Vector
Machine (SVM), and K Nearest Neighbour
(KNN) on common platform that classify lung
cancer in early stage so that many lives can be
saving. The experimental results show that KNN
gives the best result Than SVM. This report
discusses the Implementation details of our
project.
We have done data preprocessing, data cleaning
and implements machine leaning algorithm for
prediction of lung cancer at early stages through
their symptoms. We have used both classification
algorithms to find or predict the accuracy ratio.
Lung cancer is identified as the most common
cancer in the world that causes death. Early
detection has the ability to reduce deaths by 20%.
In the current clinical process, radiologists use
Computed Tomography (CT) scans to identify
lung cancer in early stages. Radiologists do so by
searching for regions called ‘nodules’, which
correspond to abnormal cell growths. But
identifying process is time consuming, laborious
and depends on the experience of the radiologist.
Hence an intelligent system to automatically
assess whether a patient is prone to have a lung
cancer is a need.
This paper presents a novel method which use
deep learning, namely convolutional neural
networks (CNNs) to identify whether a given CT scan shows evidence of lung cancer or not. The
implementation uses a combination of classical
feature-based candidate detection with modern
deep-learning architectures to generate excellent
results better than either of the methods. The
overall implementation consists of two stages.
Nodule Regions-of-Interest (ROI) extraction and
cancer classification. In nodule ROI extraction
stage, we select top most candidate regions as
nodules. A combination of rule based image
processing method and a 2D CNN was used for
this stage. In the cancer classification stage, we
estimate the malignancy of each nodule regions
and hence label the whole CT scan as cancerous
or non-cancerous. A combination of feature based
eXtreme Gradient Boosting (XGBoost) classifier
and 3D CNN was used for this stage. The LUNA
dataset and LIDC dataset were used for both
training and testing. The results were clearly
demonstrated promising classification
performance. The sensitivity, accuracy and
specificity values obtained for
Brain tumor classification in magnetic resonance imaging images using convol...IJECEIAES
Deep learning (DL) is a subfield of artificial intelligence (AI) used in several sectors, such as cybersecurity, finance, marketing, automated vehicles, and medicine. Due to the advancement of computer performance, DL has become very successful. In recent years, it has processed large amounts of data, and achieved good results, especially in image analysis such as segmentation and classification. Manual evaluation of tumors, based on medical images, requires expensive human labor and can easily lead to misdiagnosis of tumors. Researchers are interested in using DL algorithms for automatic tumor diagnosis. convolutional neural network (CNN) is one such algorithm. It is suitable for medical image classification tasks. In this paper, we will focus on the development of four sequential CNN models to classify brain tumors in magnetic resonance imaging (MRI) images. We followed two steps, the first being data preprocessing and the second being automatic classification of preprocessed images using CNN. The experiments were conducted on a dataset of 3,000 MRI images, divided into two classes: tumor and normal. We obtained a good accuracy of 98,27%, which outperforms other existing models.
Project report 3D visualization of medical imaging dataShashank
Report of my engineering research on 3D visualisation of medical images obtained from slices of human male and female cadevars. Courtesy NIH (USA), IIIT (Allahabad)
Application of 3D Modeling for Preoperative Planning and Intra Operative Navigation during Procedures on the Organs of Retroperitoneal Space by Alexey A Rozhentsov in Experimental Techniques in Urology & Nephrology
Learn more: https://www.brainlab.com/surgery-products
Abstract
Introduction “Navigation in surgery” spans a broad area, which, depending on the clinical challenge, can have different meanings. Over the past decade, navigation in surgery has evolved beyond imaging modalities and bulky systems into the rich networking of the cloud or devices that are pocket-sized.
Discussion
This article will review various aspects of navigation in the operating room and beyond. This includes a short history of navigation, the evolution of surgical navigation, as well as technical aspects and clinical benefits with examples from neurosurgery, spinal surgery, and orthopedics.
Conclusion
With improved computer technology and a trend towards advanced information processing within hospitals, navigation is quickly becoming an integral part in the surgical routine of clinicians.
Excerpt:
Over the last three decades, technical advances have significantly changed the way we live. From computers to smartphones, from single purpose to multipurpose devices, technology has become an intrinsic part of our daily routine. Navigation in surgery is an important example of today’s technological capabilities being applied to medicine. It has emerged as one of the most reliable representatives of technology as it continues to transform surgical interventions into safer and less invasive procedures. In surgery, navigation has spurred technical progress, enabled more daring procedures, and unlocked new synergies. What was once a simple localization tool has evolved into a centerpiece of technology in the surgical theater.
“Navigation in surgery” spans a broad area, which, depending on the clinical challenge, may have various interpretations. The meaning of navigation in surgery is most accurately defined by the questions posed: “Where is my (anatomical) target?”, “How do I reach my target safely?”, “Where am I (anatomically)?”, or “Where and how shall I position my implant?”. Apart from these important anatomical orientation questions, surgical navigation is also used as a measurement tool and an information center for providing surgeons with the right information at the right time.
There are examples of technological advances in the medical field, whose benefit to the patient became immediately evident which were rapidly adopted and integrated into the clinical routine—without the need for proper randomized clinical trials. Examples range from the introduction of anesthesia to enable safer surgery and the introduction of microscopy enabling microsurgery. Surgical navigation and its wide range of benefits could be next.
Brain tumor detection and localization in magnetic resonance imagingijitcs
A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the
surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death and
responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate
in the world. Great knowledge and experience on radiology are required for accurate tumor detection in
medical imaging. Automation of tumor detection is required because there might be a shortage of skilled
radiologists at a time of great need. We propose an automatic brain tumor detectionand localization
framework that can detect and localize brain tumor in magnetic resonance imaging. The proposed brain
tumor detection and localization framework comprises five steps: image acquisition, pre-processing, edge
detection, modified histogram clustering and morphological operations. After morphological operations,
tumors appear as pure white color on pure black backgrounds. We used 50 neuroimages to optimize our
system and 100 out-of-sample neuroimages to test our system. The proposed tumor detection and localization
system was found to be able to accurately detect and localize brain tumor in magnetic resonance imaging.
The preliminary results demonstrate how a simple machine learning classifier with a set of simple
image-based features can result in high classification accuracy. The preliminary results also demonstrate the
efficacy and efficiency of our five-step brain tumor detection and localization approach and motivate us to
extend this framework to detect and localize a variety of other types of tumors in other types of medical
imagery.
Brain tumor classification using artificial neural network on mri imageseSAT Journals
Abstract
In this paper, an attempt has been made to summarize the multi-resolution transformation and the different classifiers useful to
analyze the brain tumor using MRI. X-ray, MRI, Ultrasound etc. are different techniques used to scan brain tumor images.
Radiologist prefers MRI to get detail information about tumor to help him diagnoses. In this paper we have used MRI of brain
tumor for analysis. We have used Digital image processing tool for detection of the tumor. The identification, detection and
classification of brain tumor have been done by extracting features from MRI with the help of wavelet transformation. The MRI of
brain tumor is classified into two categories normal and abnormal brain. In this work Digital image processing has been used as
a tool for getting clear and exact details about tumor in earlier stages. This helps the physicians and practitioners for diagnoses.
Key word – Brain tumor, Wavelet transform, segmentation.
Learn more: https://www.brainlab.com/spinal-navigation
Brainlab Spinal Navigation combines state-of-the-art touch screen based image control with best-in-class registration methods for image-guided surgery. As an open navigation platform, Brainlab Spinal Navigation enables accurate pedicle screw placement as well as drastic reduction of X-Ray exposure to both the surgical team and the patient. Navigation of implants and instruments is possible in 2D images, 3D fluoroscopy scans, MR or CT datasets in all stages of surgery—from incision planning to implant placement.
Lung cancer is one of the leading
causes of mortality in every country, affecting
both men and women. Lung cancer has a low
prognosis, resulting in a high death rate. The
computing sector is fully automating it, and the
medical industry is also automating itself with the
aid of image recognition and data analytics. Lung
cancer is one of the most common diseases for
human beings everywhere throughout the world.
Lung cancer is a disease which arises due to growth
of unwanted tissues in the lung and this growth
which spreads beyond the lung are named as
metastasis which spreads into other parts of the
body.
The objective of our project is to inspect accuracy
ratio of two classifiers which is Support Vector
Machine (SVM), and K Nearest Neighbour
(KNN) on common platform that classify lung
cancer in early stage so that many lives can be
saving. The experimental results show that KNN
gives the best result Than SVM. This report
discusses the Implementation details of our
project.
We have done data preprocessing, data cleaning
and implements machine leaning algorithm for
prediction of lung cancer at early stages through
their symptoms. We have used both classification
algorithms to find or predict the accuracy ratio.
Lung cancer is identified as the most common
cancer in the world that causes death. Early
detection has the ability to reduce deaths by 20%.
In the current clinical process, radiologists use
Computed Tomography (CT) scans to identify
lung cancer in early stages. Radiologists do so by
searching for regions called ‘nodules’, which
correspond to abnormal cell growths. But
identifying process is time consuming, laborious
and depends on the experience of the radiologist.
Hence an intelligent system to automatically
assess whether a patient is prone to have a lung
cancer is a need.
This paper presents a novel method which use
deep learning, namely convolutional neural
networks (CNNs) to identify whether a given CT scan shows evidence of lung cancer or not. The
implementation uses a combination of classical
feature-based candidate detection with modern
deep-learning architectures to generate excellent
results better than either of the methods. The
overall implementation consists of two stages.
Nodule Regions-of-Interest (ROI) extraction and
cancer classification. In nodule ROI extraction
stage, we select top most candidate regions as
nodules. A combination of rule based image
processing method and a 2D CNN was used for
this stage. In the cancer classification stage, we
estimate the malignancy of each nodule regions
and hence label the whole CT scan as cancerous
or non-cancerous. A combination of feature based
eXtreme Gradient Boosting (XGBoost) classifier
and 3D CNN was used for this stage. The LUNA
dataset and LIDC dataset were used for both
training and testing. The results were clearly
demonstrated promising classification
performance. The sensitivity, accuracy and
specificity values obtained for
Brain tumor classification in magnetic resonance imaging images using convol...IJECEIAES
Deep learning (DL) is a subfield of artificial intelligence (AI) used in several sectors, such as cybersecurity, finance, marketing, automated vehicles, and medicine. Due to the advancement of computer performance, DL has become very successful. In recent years, it has processed large amounts of data, and achieved good results, especially in image analysis such as segmentation and classification. Manual evaluation of tumors, based on medical images, requires expensive human labor and can easily lead to misdiagnosis of tumors. Researchers are interested in using DL algorithms for automatic tumor diagnosis. convolutional neural network (CNN) is one such algorithm. It is suitable for medical image classification tasks. In this paper, we will focus on the development of four sequential CNN models to classify brain tumors in magnetic resonance imaging (MRI) images. We followed two steps, the first being data preprocessing and the second being automatic classification of preprocessed images using CNN. The experiments were conducted on a dataset of 3,000 MRI images, divided into two classes: tumor and normal. We obtained a good accuracy of 98,27%, which outperforms other existing models.
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.
Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.
Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the computational intelligence.
The biomedical profession has gained importance due to the rapid and accurate diagnosis of clinical patients using computer-aided diagnosis (CAD) tools.
The diagnosis and treatment of Alzheimer’s disease (AD) using complementary multimodalities can improve the quality of life and mental state of patients.
In this study, we integrated a lightweight custom convolutional neural network
(CNN) model and nature-inspired optimization techniques to enhance the performance, robustness, and stability of progress detection in AD. A multi-modal
fusion database approach was implemented, including positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, to create a fused
database. We compared the performance of custom and pre-trained deep learning models with and without optimization and found that employing natureinspired algorithms like the particle swarm optimization algorithm (PSO) algorithm significantly improved system performance. The proposed methodology,
which includes a fused multimodality database and optimization strategy, improved performance metrics such as training, validation, test accuracy, precision, and recall. Furthermore, PSO was found to improve the performance of
pre-trained models by 3-5% and custom models by up to 22%. Combining different medical imaging modalities improved the overall model performance by
2-5%. In conclusion, a customized lightweight CNN model and nature-inspired
optimization techniques can significantly enhance progress detection, leading to
better biomedical research and patient care.
Neural Network Based Classification and Diagnosis of Brain HemorrhagesWaqas Tariq
The classification and diagnosis of brain hemorrhages has work out into a great importance diligence in early detection of hemorrhages which reduce the death rates. The purpose of this research was to detect brain hemorrhages and classify them and provide the patient with correct diagnosis. A possible solution to this social problem is to utilize predictive techniques such as sparse component analysis, artificial neural networks to develop a method for detection and classification. In this study we considered a perceptron based feed forward neural network for early detection of hemorrhages. This paper attempts to spot on consider and talk about Computer Aided Diagnosis (CAD) that chiefly necessitated in clinical diagnosis without human act. This paper introduces a Region Severance Algorithm (RSA) for detection and location of hemorrhages and an algorithm for finding threshold band. In this paper different data sets (CT images) are taken from various machines and the results obtained by applying our algorithm and those results were compared with domain expert. Further researches were challenged to originate different models in study of hemorrhages caused by hyper tension or by existing tumor in the brain.
Detect COVID-19 with Deep Learning- A survey on Deep Learning for Pulmonary M...JumanaNadir
Who knew Deep Learning can come so handy to us during this period of global crisis?
There has yet been no vaccine or any effective treatment for the 2019 novel Coronavirus (COVID-19), but generative deep learning is helping in detecting and monitoring coronavirus patients by chest CT screening.
An automated system for classifying types of cerebral hemorrhage based on ima...IJECEIAES
The brain is one of the most important vital organs in the human body. It is responsible for most of the body’s basic activities, such as breathing, heartbeat, thinking, remembering, speaking, and others. It also controls the central nervous system. Cerebral hemorrhage is considered one of the most dangerous diseases that a person may be exposed to during his life. Therefore, the correct and rapid diagnosis of the hemorrhage type is an important medical issue. The innovation in this work lies in extracting a huge number of effective features from computed tomography (CT) images of the brain using the Orange3 data mining technique, as the number of features extracted from each CT image reached (1,000). The proposed system then uses the extracted features in the classification process through logistic regression (LR), support vector machine (SVM), k-nearest neighbor algorithm (KNN), and convolutional neural networks (CNN), which classify cerebral hemorrhage into four main types: epidural hemorrhage, subdural hemorrhage, intraventricular hemorrhage, and intraparenchymal hemorrhage. A total of (1,156) CT images were tested to verify the validity of the proposed model, and the results showed that the accuracy reached the required success level with an average of (97.1%).
Classification of pathologies on digital chest radiographs using machine lear...IJECEIAES
This article is devoted to the research and development of methods for classifying pathologies on digital chest radiographs using two different machine learning approaches: the eXtreme gradient boosting (XGBoost) algorithm and the deep convolutional neural network residual network (ResNet50). The goal of the study is to develop effective and accurate methods for automatically classifying various pathologies detected on chest X-rays. The study collected an extensive dataset of digital chest radiographs, including a variety of clinical cases and different classes of pathology. Developed and trained machine learning models based on the XGBoost algorithm and the ResNet50 convolutional neural network using preprocessed images. The performance and accuracy of both models were assessed on test data using quality metrics and a comparative analysis of the results was carried out. The expected results of the article are high accuracy and reliability of methods for classifying pathologies on chest radiographs, as well as an understanding of their effectiveness in the context of clinical practice. These results may have significant implications for improving the diagnosis and care of patients with chest diseases, as well as promoting the development of automated decision support systems in radiology.
American College of Radiology, Data Science Institute, AI-Lab
The ACR Data Science Institute has developed the ACR AI-LAB™, a data science toolkit designed to democratize AI by empowering radiologists to develop algorithms at their own institutions, using their own patient data, to meet their own clinical needs.
The National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Acute scrotum is a general term referring to an emergency condition affecting the contents or the wall of the scrotum.
There are a number of conditions that present acutely, predominantly with pain and/or swelling
A careful and detailed history and examination, and in some cases, investigations allow differentiation between these diagnoses. A prompt diagnosis is essential as the patient may require urgent surgical intervention
Testicular torsion refers to twisting of the spermatic cord, causing ischaemia of the testicle.
Testicular torsion results from inadequate fixation of the testis to the tunica vaginalis producing ischemia from reduced arterial inflow and venous outflow obstruction.
The prevalence of testicular torsion in adult patients hospitalized with acute scrotal pain is approximately 25 to 50 percent
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
8. 8
9 new TCIA-related Publications
1. Golan R, Jacob C, Denzinger J, editors. Lung nodule detection in CT images using deep convolutional neural
networks. Neural Networks (IJCNN), 2016 International Joint Conference on; 2016.
2. Ahmed Z, Levesque IR. Increased robustness in reference region model analysis of DCE MRI using two‐step
constrained approaches. Magnetic Resonance in Medicine. 2016. doi: 10.1002/mrm.26530.
3. Anil R, Colen RR. Imaging Genomics in Glioblastoma Multiforme: A Predictive Tool for Patients Prognosis,
Survival, and Outcome. Magnetic Resonance Imaging Clinics of North America. 2016;24(4):731-40.
4. Roozgard A, Barzigar N, Verma P, Cheng S. 3D-SCoBeP: 3D medical image registration using sparse coding and
belief propagation. International Journal of Diagnostic Imaging. 2014;2(1):54-68.
5. Ypsilantis P-P, Montana G. Recurrent Convolutional Networks for Pulmonary Nodule Detection in CT Imaging.
arXiv preprint arXiv:160909143. 2016.
6. Wang Q, Chen X, Wei M, Miao Z. Simultaneous encryption and compression of medical images based on
optimized tensor compressed sensing with 3D Lorenz. BioMedical Engineering OnLine. 2016;15(1):118.
7. Hsieh KL-C, Lo C-M, Hsiao C-J. Computer-aided grading of gliomas based on local and global MRI features.
Computer Methods and Programs in Biomedicine. 2016;139:31-8.
8. Krishnamurthy S, Narasimhan G, Rengasamy U. An Level Set Evolution Morphology Based Segmentation of Lung
Nodules and False Nodule Elimination by 3D Centroid Shift and Frequency Domain DC Constant Analysis.
International Journal of u- and e- Service, Science and Technology. 2016;9(10):187-98. doi:
http://www.sersc.org/journals/IJUNESST/vol9_no10/18.pdf
9. Nida N, Sharif M, Khan MUG, Yasmin M, Fernandes SL. A FRAMEWORK FOR AUTOMATIC COLORIZATION OF
MEDICAL IMAGING. IIOABJ. 2016;7:202-9.
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Other News: TCIA to collect APOLLO Moonshot Data
https://www.cancer.gov/research/key-initiatives/moonshot-cancer-initiative/milestones/nci-activities
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Other News: SPIE-AAPM ProstateX Challenge
PROSTATEx Classification Challenge
• Release date of training set cases with truth: 21 Nov 2016
• Release date of test set cases without truth: 12 Dec 2016
• Submission date for participants’ test set classification output: 15 Jan 2017
• Challenge results released to participants: 20 Jan 2017
• SPIE Medical Imaging Symposium: 13-16 Feb 2017
218 participants registered!
