The document contains summaries of multiple research papers related to spine and scoliosis diagnosis and classification using machine learning and deep learning techniques. The papers propose and evaluate different algorithms using metrics like accuracy, sensitivity and F1-score. Methodologies involved include convolutional neural networks, random forest, SVM, deep learning models like U-Net etc. applied to X-ray, MRI and CT image datasets. The papers demonstrate high performance of these techniques for tasks like vertebrae segmentation, curvature measurement, deformity detection, and intervertebral disc classification, with most achieving accuracy above 85%. Limitations and scope for future work are also discussed.
Ataxic person prediction using feature optimized based on machine learning modelIJECEIAES
Ataxic gait monitoring and assessment of neurological disorders belong to important areas that are supported by digital signal processing methods and artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) techniques. This paper uses spatio-temporal data from Kinect sensor to optimize machine learning model to distinguish between ataxic and normal gait. Existing ML-based methodologies fails to establish feature correlation between different gait parameters; thus, exhibit very poor performance. Further, when data is imbalanced in nature the existing ML-based methodologies induces higher false positive. In addressing the research issues this paper introduces an extreme gradient boost (XGBoost)based classifier and enhanced feature optimization (EFO) by modifying the standard cross validation (SCV) mechanism. Experiment outcome shows the proposed ataxic person identification model achieves very good result in comparison with existing ML-based and DL-based ataxic person identification methodologies.
APPLICATION OF CNN MODEL ON MEDICAL IMAGEIRJET Journal
The document discusses using convolutional neural network (CNN) models to detect diseases from medical images such as chest X-rays. It describes how CNN models can be trained on large labeled datasets of chest X-rays to learn patterns and features that indicate diseases. The document then evaluates several CNN architectures - including VGG-16, ResNet, DenseNet, and InceptionNet - for classifying chest X-rays as normal or infected. It finds these models achieve high accuracy, with metrics like accuracy over 89% and AUC over 0.94. In conclusion, deep learning models show promising results for automated disease detection from medical images.
Bone Age Estimation for Investigational AnalysisIRJET Journal
This document proposes a machine learning model to estimate bone age from pelvis X-ray images for individuals over 18 years of age. The model uses the Xception neural network architecture with transfer learning. The dataset contains 200 pelvis X-ray images of different age groups. The images are preprocessed using CLAHE and data augmentation. The model achieves a mean average error of 12.352 years for bone age estimation. This bone age estimation system could help determine the age of victims in forensic investigations when other identification documents are unavailable. The accuracy may be improved further by adding more X-ray images to the training dataset.
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.
This document reviews various automated techniques that have been developed for brain tumor detection. It summarizes research done by several researchers on methods like sequential floating forward selection, color coding schemes using brain atlases, neural networks, region growing segmentation combined with area calculation, symmetry analysis of tumor areas in MRI images, and combining clustering and classification algorithms. The paper concludes that image segmentation plays an important role in medical applications like tumor diagnosis and that more robust techniques are needed for high accuracy and reliability.
IRJET- Breast Cancer Detection from Histopathology Images: A ReviewIRJET Journal
This document provides a review of techniques for detecting breast cancer from histopathology images. It discusses how histopathology examines tissue samples under a microscope to study diseases at a microscopic level. Detecting cell nuclei is an important first step, as is identifying mitosis (cell division) and metastasis (cancer spreading). The document reviews several techniques that use convolutional neural networks to automatically analyze histopathology images and detect breast cancer, including techniques for nuclei detection and segmentation. These automatic methods aim to assist pathologists by improving efficiency and reducing human error compared to manual analysis.
An optimized approach for extensive segmentation and classification of brain ...IJECEIAES
With the significant contribution in medical image processing for an effective diagnosis of critical health condition in human, there has been evolution of various methods and techniques in abnormality detection and classification process. An insight to the existing approaches highlights that potential amount of work is being carried out in detection and segmentation process but less effective modelling towards classification problems. This manuscript discusses about a simple and robust modelling of a technique that offers comprehensive segmentation process as well as classification process using Artificial Neural Network. Different from any existing approach, the study offers more granularities towards foreground/ background indexing with its comprehensive segmentation process while introducing a unique morphological operation along with graph-believe network for ensuring approximately 99% of accuracy of proposed system in contrast to existing learning scheme.
Statistical Feature-based Neural Network Approach for the Detection of Lung C...CSCJournals
Lung cancer, if successfully detected at early stages, enables many treatment options, reduced risk of invasive surgery and increased survival rate. This paper presents a novel approach to detect lung cancer from raw chest X-ray images. At the first stage, we use a pipeline of image processing routines to remove noise and segment the lung from other anatomical structures in the chest X-ray and extract regions that exhibit shape characteristics of lung nodules. Subsequently, first and second order statistical texture features are considered as the inputs to train a neural network to verify whether a region extracted in the first stage is a nodule or not . The proposed approach detected nodules in the diseased area of the lung with an accuracy of 96% using the pixel-based technique while the feature-based technique produced an accuracy of 88%.
Ataxic person prediction using feature optimized based on machine learning modelIJECEIAES
Ataxic gait monitoring and assessment of neurological disorders belong to important areas that are supported by digital signal processing methods and artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) techniques. This paper uses spatio-temporal data from Kinect sensor to optimize machine learning model to distinguish between ataxic and normal gait. Existing ML-based methodologies fails to establish feature correlation between different gait parameters; thus, exhibit very poor performance. Further, when data is imbalanced in nature the existing ML-based methodologies induces higher false positive. In addressing the research issues this paper introduces an extreme gradient boost (XGBoost)based classifier and enhanced feature optimization (EFO) by modifying the standard cross validation (SCV) mechanism. Experiment outcome shows the proposed ataxic person identification model achieves very good result in comparison with existing ML-based and DL-based ataxic person identification methodologies.
APPLICATION OF CNN MODEL ON MEDICAL IMAGEIRJET Journal
The document discusses using convolutional neural network (CNN) models to detect diseases from medical images such as chest X-rays. It describes how CNN models can be trained on large labeled datasets of chest X-rays to learn patterns and features that indicate diseases. The document then evaluates several CNN architectures - including VGG-16, ResNet, DenseNet, and InceptionNet - for classifying chest X-rays as normal or infected. It finds these models achieve high accuracy, with metrics like accuracy over 89% and AUC over 0.94. In conclusion, deep learning models show promising results for automated disease detection from medical images.
Bone Age Estimation for Investigational AnalysisIRJET Journal
This document proposes a machine learning model to estimate bone age from pelvis X-ray images for individuals over 18 years of age. The model uses the Xception neural network architecture with transfer learning. The dataset contains 200 pelvis X-ray images of different age groups. The images are preprocessed using CLAHE and data augmentation. The model achieves a mean average error of 12.352 years for bone age estimation. This bone age estimation system could help determine the age of victims in forensic investigations when other identification documents are unavailable. The accuracy may be improved further by adding more X-ray images to the training dataset.
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.
This document reviews various automated techniques that have been developed for brain tumor detection. It summarizes research done by several researchers on methods like sequential floating forward selection, color coding schemes using brain atlases, neural networks, region growing segmentation combined with area calculation, symmetry analysis of tumor areas in MRI images, and combining clustering and classification algorithms. The paper concludes that image segmentation plays an important role in medical applications like tumor diagnosis and that more robust techniques are needed for high accuracy and reliability.
IRJET- Breast Cancer Detection from Histopathology Images: A ReviewIRJET Journal
This document provides a review of techniques for detecting breast cancer from histopathology images. It discusses how histopathology examines tissue samples under a microscope to study diseases at a microscopic level. Detecting cell nuclei is an important first step, as is identifying mitosis (cell division) and metastasis (cancer spreading). The document reviews several techniques that use convolutional neural networks to automatically analyze histopathology images and detect breast cancer, including techniques for nuclei detection and segmentation. These automatic methods aim to assist pathologists by improving efficiency and reducing human error compared to manual analysis.
An optimized approach for extensive segmentation and classification of brain ...IJECEIAES
With the significant contribution in medical image processing for an effective diagnosis of critical health condition in human, there has been evolution of various methods and techniques in abnormality detection and classification process. An insight to the existing approaches highlights that potential amount of work is being carried out in detection and segmentation process but less effective modelling towards classification problems. This manuscript discusses about a simple and robust modelling of a technique that offers comprehensive segmentation process as well as classification process using Artificial Neural Network. Different from any existing approach, the study offers more granularities towards foreground/ background indexing with its comprehensive segmentation process while introducing a unique morphological operation along with graph-believe network for ensuring approximately 99% of accuracy of proposed system in contrast to existing learning scheme.
Statistical Feature-based Neural Network Approach for the Detection of Lung C...CSCJournals
Lung cancer, if successfully detected at early stages, enables many treatment options, reduced risk of invasive surgery and increased survival rate. This paper presents a novel approach to detect lung cancer from raw chest X-ray images. At the first stage, we use a pipeline of image processing routines to remove noise and segment the lung from other anatomical structures in the chest X-ray and extract regions that exhibit shape characteristics of lung nodules. Subsequently, first and second order statistical texture features are considered as the inputs to train a neural network to verify whether a region extracted in the first stage is a nodule or not . The proposed approach detected nodules in the diseased area of the lung with an accuracy of 96% using the pixel-based technique while the feature-based technique produced an accuracy of 88%.
Survey on Segmentation Techniques for Spinal Cord ImagesIIRindia
Medical imaging is a technique which is used to expose the interior part of the body, to diagnose the diseases and to treat them as well. Different modalities are used to process the medical images. It helps the human specialists to make diagnosis ailments. In this paper, we surveyed segmentation on the spinal cord images using different techniques such as Data mining, Support vector machine, Neural Networks and Genetic Algorithm which are applied to find the disorders and syndromes affected in the spinal cord system. As a result, we have gained knowledge in an identified disarrays and ailments affected in lumbar vertebra, thoracolumbar vertebra and spinal canal. Finally how the Disc Similarity Index values are generated in each method is also analysed.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
BREAST CANCER DIAGNOSIS USING MACHINE LEARNING ALGORITHMS –A SURVEYijdpsjournal
Breast cancer has become a common factor now-a-days. Despite the fact, not all general hospitals
have the facilities to diagnose breast cancer through mammograms. Waiting for diagnosing a breast
cancer for a long time may increase the possibility of the cancer spreading. Therefore a computerized
breast cancer diagnosis has been developed to reduce the time taken to diagnose the breast cancer and
reduce the death rate. This paper summarizes the survey on breast cancer diagnosis using various machine
learning algorithms and methods, which are used to improve the accuracy of predicting cancer. This survey
can also help us to know about number of papers that are implemented to diagnose the breast cancer.
Artificial neural network for cervical abnormalities detection on computed to...IAESIJAI
Cervical cancer is the second deadliest after breast cancer in Indonesia.
Sundry diagnostic imaging modalities had been used to decide the location
and severity of cervical cancer, one among those is computed tomography
(CT) Scan. This study handles a CT image dataset consisting of two
categories, abnormal cervical images of cervical cancer patients and normal
cervix images of patients with other diseases. It focuses on the ability of
segmentation and classification programs to localize cervical cancer areas
and classify images into normal and abnormal categories based on the
features contained in them. We conferred a novel methodology for the
contour detection round the cervical organ classified with artificial neural
network (ANN) which was employed to categorize the image data. The
segmentation algorithm used was a region-based snake model. The texture
features of the cervical image area were arranged in the form of gray level
co-occurrence matrix (GLCM). Support vector machine (SVM) had been
added to determine which algorithm was better for comparison.
Experimental results show that ANN model has better receiver operating
characteristic (ROC) parameter values than SVM model’s and existing
approach’s regarding 96.2% of sensitivity, 95.32% of specificity, and
95.75% of accuracy.
This document presents a model to detect and classify brain tumors using watershed algorithm for image segmentation and convolutional neural networks (CNN). The model takes MRI images as input, pre-processes the images by converting them to grayscale and removing noise, then uses watershed algorithm for image segmentation and CNN for tumor classification. The CNN architecture achieves classification of three tumor types. Previous related works that also used deep learning methods for brain tumor detection and classification are discussed. The proposed system methodology involves inputting MRI images, pre-processing, segmentation using watershed algorithm, and classification of tumorous vs non-tumorous cells using CNN.
At the 35th AICC-RCOG Annual Conference in association with FOGSI and MOGS, Dr. Niranjan Chavan, President of MOGS, gave an address on Artificial Intelligence in Gynaecologic Oncology at Taj Lands' End, Bandra, Mumbai on the 6th November 2022
Artificial neural networks (ANN) consider classification as one of the most dynamic research and
application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by
back propagation algorithm. The different combinations of functions and its effect while using ANN as a
classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The
back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification
with suitable combination of training, learning and transfer functions. When the maximum likelihood
method was compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible.
Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to
be more effective than other classification algorithms.
detection and classification of knee osteoarthritis.pptxAleenaJamil4
The document presents a research proposal for detecting knee osteoarthritis in X-ray images using computer vision techniques. It discusses knee osteoarthritis as a motivation, sets the objective to classify X-ray images into severity categories using deep learning models. It reviews several related works that used CNNs for osteoarthritis detection and their limitations. The methodology section outlines data collection, preprocessing, model selection of CNNs, model training and evaluation. Evaluation metrics and a tentative timetable are also provided.
A novel framework for efficient identification of brain cancer region from br...IJECEIAES
Diagnosis of brain cancer using existing imaging techniques, e.g., Magnetic Resonance Imaging (MRI) is shrouded with various degrees of challenges. At present, there are very few significant research models focusing on introducing some novel and unique solutions towards such problems of detection. Moreover, existing techniques are found to have lesser accuracy as compared to other detection schemes. Therefore, the proposed paper presents a framework that introduces a series of simple and computationally cost-effective techniques that have assisted in leveraging the accuracy level to a very higher degree. The proposed framework takes the input image and subjects it to non-conventional segmentation mechanism followed by optimizing the performance using directed acyclic graph, Bayesian Network, and neural network. The study outcome of the proposed system shows the significantly higher degree of accuracy in detection performance as compared to frequently existing approaches.
A study on techniques to detect and classify acute lymphoblastic leukemia usi...IRJET Journal
This document summarizes 14 research papers on techniques for detecting and classifying acute lymphoblastic leukemia (ALL) using machine learning and deep learning methods. The papers describe various approaches including using convolutional neural networks to analyze peripheral blood smear images, applying machine learning algorithms to clinical data to identify ALL, and developing mobile apps to assist caregivers of ALL patients. Most papers achieved high accuracy rates of over 95% for detecting and classifying ALL. The document concludes that preprocessing images is an important first step, and that pre-trained CNN models with modifications are effective for detecting and classifying ALL in images.
Framework for progressive segmentation of chest radiograph for efficient diag...IJECEIAES
Segmentation is one of the most essential steps required to identify the inert object in the chest x-ray. A review with the existing segmentation techniques towards chest x-ray as well as other vital organs was performed. The main objective was to find whether existing system offers accuracy at the cost of recursive and complex operations. The proposed system contributes to introduce a framework that can offer a good balance between computational performance and segmentation performance. Given an input of chest x-ray, the system offers progressive search for similar image on the basis of similarity score with queried image. Region-based shape descriptor is applied for extracting the feature exclusively for identifying the lung region from the thoracic region followed by contour adjustment. The final segmentation outcome shows accurate identification followed by segmentation of apical and costophrenic region of lung. Comparative analysis proved that proposed system offers better segmentation performance in contrast to existing system.
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.
Optimizing Problem of Brain Tumor Detection using Image ProcessingIRJET Journal
This document summarizes several existing methods for detecting brain tumors using magnetic resonance imaging (MRI). It discusses techniques such as image preprocessing, segmentation, feature extraction, and classification methods. Specifically, it reviews 10 different papers that propose various approaches for brain tumor detection, segmentation, and classification. These include using k-means clustering, fuzzy c-means, probabilistic neural networks, support vector machines, genetic algorithms, and sparse representation classification. The goal is to evaluate and compare different existing methods for automated brain tumor detection and analysis using MRI images.
View classification of medical x ray images using pnn classifier, decision tr...eSAT Journals
Abstract: In this era of electronic advancements in the field of medical image processing, the quantum of medical X-ray images so produced exorbitantly can be effectively addressed by means of automated indexing, comparing, analysing and annotating that will really be pivotal to the radiologists in interpreting and diagnosing the diseases. In order to envisage such an objective, it has been humbly endeavoured in this paper by proposing an efficient methodology that takes care of the view classification of the X-ray images for the automated annotation from their vast database, with which the decision making for the physicians and radiologists becomes simpler despite an immeasurable and ever-growing trends of the X-ray images. In this paper, X-ray images of six different classes namely chest, head, foot, palm, spine and neck have been collected. The framework proposed in this paper involves the following: The images are pre-processed using M3 filter and segmentation by Expectation Maximization (EM) algorithm, followed by feature extraction through Discrete Wavelet Transform. The orientation of X-ray images has been performed in this work by comparing among the Probabilistic Neural Network (PNN), Decision Tree algorithm and Support Vector Machine (SVM), while the PNN yields an accuracy of 75%, the Decision Tree with 92.77% and the SVM of 93.33%. Key Words: M3 filter, Expectation Maximaization, Discrete Wavelet Transformation, Probabilistic Neural Network, Decision Tree Algorithm and Support Vector Machine.
A Survey On Identification Of Multiple Sclerosis Lesions From Brain MRIJanelle Martinez
This document summarizes several papers on using MRI and image analysis techniques to identify multiple sclerosis (MS) lesions in the brain. It discusses using metrics like fractional anisotropy and apparent diffusion coefficient from diffusion tensor imaging to analyze changes in normal-appearing white matter. It also reviews methods like texture analysis using gray-level co-occurrence matrices, convolutional neural networks, and phase and orientation analysis to segment and identify MS lesions in MRI scans. The goal is to characterize subtle microscopic changes in nerve fibers caused by MS through analysis of MRI textures and alignments to improve diagnosis and understanding of the disease.
IRJET- A Survey on Categorization of Breast Cancer in Histopathological ImagesIRJET Journal
This document summarizes various methods for categorizing breast cancer in histopathological images. It discusses machine learning and image processing techniques that have been used to build computer-aided diagnosis (CAD) systems to help pathologists diagnose breast cancer more objectively and consistently. The document reviews different classification methods that have been proposed, including those using fuzzy logic, level set methods, convolutional neural networks, texture features and ensemble methods. It concludes that accurately classifying histopathological images remains challenging due to limited publicly available datasets and variability in tissue appearance, but that machine learning and advanced image analysis offer promising approaches to improve cancer detection and diagnosis.
IRJET- A Study on Brain Tumor Detection Algorithms for MRI ImagesIRJET Journal
This document discusses algorithms for detecting brain tumors in MRI images. It begins with an abstract that outlines the key stages of brain tumor detection using image processing techniques: pre-processing, segmentation, feature extraction, and classification. It then reviews several existing techniques for brain tumor segmentation and classification, noting their advantages and limitations. Specifically, it examines algorithms using Bayesian techniques, neural networks, clustering, and deep learning. The document proposes using a Spearman algorithm for segmentation combined with a convolutional neural network classifier to overcome limitations of other methods and provide more accurate tumor detection.
Trends and Techniques of Medical Image Analysis and Brain Tumor DetectionIRJET Journal
This document reviews various techniques for medical image analysis and brain tumor detection that have been carried out by researchers. It discusses how image processing techniques like thresholding, morphological operations, clustering algorithms and deep learning methods have been applied for tasks like segmentation, feature extraction and classification of brain tumors in MRI images. The document also summarizes several papers that have used different combinations of such techniques for brain tumor detection and segmentation. Overall, the document provides an overview of the progress made in the field of medical image analysis and various algorithms developed for automated brain tumor detection and segmentation.
IRJET- Brain Tumor Detection using Hybrid Model of DCT DWT and ThresholdingIRJET Journal
The document presents a new hybrid model for detecting brain tumors in MRI images. It uses a combination of discrete cosine transform (DCT), discrete wavelet transform (DWT), principal component analysis (PCA), and fuzzy c-means clustering. DCT and DWT are applied to extract features from MRI images. PCA is then used to reduce the dimensions of the extracted features. Finally, fuzzy c-means clustering is used to segment and detect tumors. The proposed hybrid model is evaluated using objective metrics like RMSE, PSNR, correlation, contrast and entropy. Results show the hybrid model achieves better values for these metrics compared to using DCT or DWT alone, indicating it more accurately detects and segments tumors in MRI images.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
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Survey on Segmentation Techniques for Spinal Cord ImagesIIRindia
Medical imaging is a technique which is used to expose the interior part of the body, to diagnose the diseases and to treat them as well. Different modalities are used to process the medical images. It helps the human specialists to make diagnosis ailments. In this paper, we surveyed segmentation on the spinal cord images using different techniques such as Data mining, Support vector machine, Neural Networks and Genetic Algorithm which are applied to find the disorders and syndromes affected in the spinal cord system. As a result, we have gained knowledge in an identified disarrays and ailments affected in lumbar vertebra, thoracolumbar vertebra and spinal canal. Finally how the Disc Similarity Index values are generated in each method is also analysed.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
BREAST CANCER DIAGNOSIS USING MACHINE LEARNING ALGORITHMS –A SURVEYijdpsjournal
Breast cancer has become a common factor now-a-days. Despite the fact, not all general hospitals
have the facilities to diagnose breast cancer through mammograms. Waiting for diagnosing a breast
cancer for a long time may increase the possibility of the cancer spreading. Therefore a computerized
breast cancer diagnosis has been developed to reduce the time taken to diagnose the breast cancer and
reduce the death rate. This paper summarizes the survey on breast cancer diagnosis using various machine
learning algorithms and methods, which are used to improve the accuracy of predicting cancer. This survey
can also help us to know about number of papers that are implemented to diagnose the breast cancer.
Artificial neural network for cervical abnormalities detection on computed to...IAESIJAI
Cervical cancer is the second deadliest after breast cancer in Indonesia.
Sundry diagnostic imaging modalities had been used to decide the location
and severity of cervical cancer, one among those is computed tomography
(CT) Scan. This study handles a CT image dataset consisting of two
categories, abnormal cervical images of cervical cancer patients and normal
cervix images of patients with other diseases. It focuses on the ability of
segmentation and classification programs to localize cervical cancer areas
and classify images into normal and abnormal categories based on the
features contained in them. We conferred a novel methodology for the
contour detection round the cervical organ classified with artificial neural
network (ANN) which was employed to categorize the image data. The
segmentation algorithm used was a region-based snake model. The texture
features of the cervical image area were arranged in the form of gray level
co-occurrence matrix (GLCM). Support vector machine (SVM) had been
added to determine which algorithm was better for comparison.
Experimental results show that ANN model has better receiver operating
characteristic (ROC) parameter values than SVM model’s and existing
approach’s regarding 96.2% of sensitivity, 95.32% of specificity, and
95.75% of accuracy.
This document presents a model to detect and classify brain tumors using watershed algorithm for image segmentation and convolutional neural networks (CNN). The model takes MRI images as input, pre-processes the images by converting them to grayscale and removing noise, then uses watershed algorithm for image segmentation and CNN for tumor classification. The CNN architecture achieves classification of three tumor types. Previous related works that also used deep learning methods for brain tumor detection and classification are discussed. The proposed system methodology involves inputting MRI images, pre-processing, segmentation using watershed algorithm, and classification of tumorous vs non-tumorous cells using CNN.
At the 35th AICC-RCOG Annual Conference in association with FOGSI and MOGS, Dr. Niranjan Chavan, President of MOGS, gave an address on Artificial Intelligence in Gynaecologic Oncology at Taj Lands' End, Bandra, Mumbai on the 6th November 2022
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application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by
back propagation algorithm. The different combinations of functions and its effect while using ANN as a
classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The
back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification
with suitable combination of training, learning and transfer functions. When the maximum likelihood
method was compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible.
Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to
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A novel framework for efficient identification of brain cancer region from br...IJECEIAES
Diagnosis of brain cancer using existing imaging techniques, e.g., Magnetic Resonance Imaging (MRI) is shrouded with various degrees of challenges. At present, there are very few significant research models focusing on introducing some novel and unique solutions towards such problems of detection. Moreover, existing techniques are found to have lesser accuracy as compared to other detection schemes. Therefore, the proposed paper presents a framework that introduces a series of simple and computationally cost-effective techniques that have assisted in leveraging the accuracy level to a very higher degree. The proposed framework takes the input image and subjects it to non-conventional segmentation mechanism followed by optimizing the performance using directed acyclic graph, Bayesian Network, and neural network. The study outcome of the proposed system shows the significantly higher degree of accuracy in detection performance as compared to frequently existing approaches.
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This document summarizes 14 research papers on techniques for detecting and classifying acute lymphoblastic leukemia (ALL) using machine learning and deep learning methods. The papers describe various approaches including using convolutional neural networks to analyze peripheral blood smear images, applying machine learning algorithms to clinical data to identify ALL, and developing mobile apps to assist caregivers of ALL patients. Most papers achieved high accuracy rates of over 95% for detecting and classifying ALL. The document concludes that preprocessing images is an important first step, and that pre-trained CNN models with modifications are effective for detecting and classifying ALL in images.
Framework for progressive segmentation of chest radiograph for efficient diag...IJECEIAES
Segmentation is one of the most essential steps required to identify the inert object in the chest x-ray. A review with the existing segmentation techniques towards chest x-ray as well as other vital organs was performed. The main objective was to find whether existing system offers accuracy at the cost of recursive and complex operations. The proposed system contributes to introduce a framework that can offer a good balance between computational performance and segmentation performance. Given an input of chest x-ray, the system offers progressive search for similar image on the basis of similarity score with queried image. Region-based shape descriptor is applied for extracting the feature exclusively for identifying the lung region from the thoracic region followed by contour adjustment. The final segmentation outcome shows accurate identification followed by segmentation of apical and costophrenic region of lung. Comparative analysis proved that proposed system offers better segmentation performance in contrast to existing system.
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.
Optimizing Problem of Brain Tumor Detection using Image ProcessingIRJET Journal
This document summarizes several existing methods for detecting brain tumors using magnetic resonance imaging (MRI). It discusses techniques such as image preprocessing, segmentation, feature extraction, and classification methods. Specifically, it reviews 10 different papers that propose various approaches for brain tumor detection, segmentation, and classification. These include using k-means clustering, fuzzy c-means, probabilistic neural networks, support vector machines, genetic algorithms, and sparse representation classification. The goal is to evaluate and compare different existing methods for automated brain tumor detection and analysis using MRI images.
View classification of medical x ray images using pnn classifier, decision tr...eSAT Journals
Abstract: In this era of electronic advancements in the field of medical image processing, the quantum of medical X-ray images so produced exorbitantly can be effectively addressed by means of automated indexing, comparing, analysing and annotating that will really be pivotal to the radiologists in interpreting and diagnosing the diseases. In order to envisage such an objective, it has been humbly endeavoured in this paper by proposing an efficient methodology that takes care of the view classification of the X-ray images for the automated annotation from their vast database, with which the decision making for the physicians and radiologists becomes simpler despite an immeasurable and ever-growing trends of the X-ray images. In this paper, X-ray images of six different classes namely chest, head, foot, palm, spine and neck have been collected. The framework proposed in this paper involves the following: The images are pre-processed using M3 filter and segmentation by Expectation Maximization (EM) algorithm, followed by feature extraction through Discrete Wavelet Transform. The orientation of X-ray images has been performed in this work by comparing among the Probabilistic Neural Network (PNN), Decision Tree algorithm and Support Vector Machine (SVM), while the PNN yields an accuracy of 75%, the Decision Tree with 92.77% and the SVM of 93.33%. Key Words: M3 filter, Expectation Maximaization, Discrete Wavelet Transformation, Probabilistic Neural Network, Decision Tree Algorithm and Support Vector Machine.
A Survey On Identification Of Multiple Sclerosis Lesions From Brain MRIJanelle Martinez
This document summarizes several papers on using MRI and image analysis techniques to identify multiple sclerosis (MS) lesions in the brain. It discusses using metrics like fractional anisotropy and apparent diffusion coefficient from diffusion tensor imaging to analyze changes in normal-appearing white matter. It also reviews methods like texture analysis using gray-level co-occurrence matrices, convolutional neural networks, and phase and orientation analysis to segment and identify MS lesions in MRI scans. The goal is to characterize subtle microscopic changes in nerve fibers caused by MS through analysis of MRI textures and alignments to improve diagnosis and understanding of the disease.
IRJET- A Survey on Categorization of Breast Cancer in Histopathological ImagesIRJET Journal
This document summarizes various methods for categorizing breast cancer in histopathological images. It discusses machine learning and image processing techniques that have been used to build computer-aided diagnosis (CAD) systems to help pathologists diagnose breast cancer more objectively and consistently. The document reviews different classification methods that have been proposed, including those using fuzzy logic, level set methods, convolutional neural networks, texture features and ensemble methods. It concludes that accurately classifying histopathological images remains challenging due to limited publicly available datasets and variability in tissue appearance, but that machine learning and advanced image analysis offer promising approaches to improve cancer detection and diagnosis.
IRJET- A Study on Brain Tumor Detection Algorithms for MRI ImagesIRJET Journal
This document discusses algorithms for detecting brain tumors in MRI images. It begins with an abstract that outlines the key stages of brain tumor detection using image processing techniques: pre-processing, segmentation, feature extraction, and classification. It then reviews several existing techniques for brain tumor segmentation and classification, noting their advantages and limitations. Specifically, it examines algorithms using Bayesian techniques, neural networks, clustering, and deep learning. The document proposes using a Spearman algorithm for segmentation combined with a convolutional neural network classifier to overcome limitations of other methods and provide more accurate tumor detection.
Trends and Techniques of Medical Image Analysis and Brain Tumor DetectionIRJET Journal
This document reviews various techniques for medical image analysis and brain tumor detection that have been carried out by researchers. It discusses how image processing techniques like thresholding, morphological operations, clustering algorithms and deep learning methods have been applied for tasks like segmentation, feature extraction and classification of brain tumors in MRI images. The document also summarizes several papers that have used different combinations of such techniques for brain tumor detection and segmentation. Overall, the document provides an overview of the progress made in the field of medical image analysis and various algorithms developed for automated brain tumor detection and segmentation.
IRJET- Brain Tumor Detection using Hybrid Model of DCT DWT and ThresholdingIRJET Journal
The document presents a new hybrid model for detecting brain tumors in MRI images. It uses a combination of discrete cosine transform (DCT), discrete wavelet transform (DWT), principal component analysis (PCA), and fuzzy c-means clustering. DCT and DWT are applied to extract features from MRI images. PCA is then used to reduce the dimensions of the extracted features. Finally, fuzzy c-means clustering is used to segment and detect tumors. The proposed hybrid model is evaluated using objective metrics like RMSE, PSNR, correlation, contrast and entropy. Results show the hybrid model achieves better values for these metrics compared to using DCT or DWT alone, indicating it more accurately detects and segments tumors in MRI images.
Similar to progress pregentation 11 dec 2023 old - Copy.pptx (20)
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
1. AUTHOR &YEAR TOPIC OBJECTIVE METHODOLOGY METRICS USED REMARKS
Peiji Chen , Zhangnan
Zhou,Haixia Yu,2 Kun
Chen , and Yun Yang
(2022)
Computerized-Assisted
Scoliosis Diagnosis
Based on Faster RCNN
and ResNet for the
Classification of Spine
X-Ray
The paper proposes a
new algorithm that uses
Faster R-CNN and
ResNet to locate and
classify scoliosis
diseases from X-ray
images, without manual
intervention.
R-CNN and ResNet X-ray data The paper compares the proposed
algorithm with other machine learning
methods that use texture features and
SVM, and shows that the proposed
algorithm has higher precision,
sensitivity, and specificity.
Rizki Tri Prasetio
and Dwiza Riana
(2015)
A Comparison of
Classification Methods
in Vertebral Column
Disorder with the
Application of Genetic
Algorithm and Bagging
The paper compares
three classification
methods (naïve bayes,
neural networks, and k-
nearest neighbour) for
detecting spine
abnormalities using MRI
images. The paper also
proposes a combination
of genetic algorithm and
bagging technique to
improve the accuracy
and handle the class
imbalance problem.
Genetic algorithm,
bagging techniques.
Vertebral column
dataset from the
UCI machine
learning
repository.
The paper also shows that k-nearest
neighbour has the best accuracy among
the three classifiers after applying
genetic algorithm and bagging
technique.
The paper concludes that genetic
algorithm and bagging technique are
effective for solving the class
imbalance problem and improving the
classification of vertebral column
disorders32.
2. AUTHOR &YEAR TOPIC OBJECTIVE METHODOLOGY METRICS USED REMARKS
Sinta Kusuma
Wardani , Riyanto
Sigit ,
Setiawardhana,
Seffiana Manik Syah
Putri , DindaAyu
Yunitasari(2018)
Measurement of
Spinal Curvature for
Scoliosis
Classification
The paper presents a
system that uses image
processing and artificial
neural network to
segment and classify
spinal x-ray images of
scoliosis patients based
on the degree of
curvature.
Artificial neural
network. Watershed
method
X-ray data The system achieves 99.74%
accuracy in segmentation and
curvature calculation. The system can
facilitate the diagnosis and treatment
of scoliosis patients.
Marcelo da Silva
Barreiro, Marcello
H. Nogueira-
Barbosa, Rangaraj
M. Rangayyan,
Rafael de Menezes
Reis1 , Lucas
Calabrez Pereyra1 ,
Paulo M. Azevedo-
Marques(2014)
semiautomatic
classification of
intervertebral disc
degeneration in
magnetic resonance
images of the spine
Develop a quantitative
method for computer-
aided diagnosis (CAD)
of intervertebral disc
degeneration according
to Pfirrmann’s scale, a
semiquantitative scale
with five degrees of
degeneration, in T2-
weighted magnetic
resonance images of the
lumbar spine.
The intervertebral
discs were assigned
Pfirrmann’s grades
based on independent
and blind
classification.
Image of 210 Discs. The study indicates the feasibility of
the proposed approach for
semiautomatic classification of disc
degeneration in T2-weighted MR
images.
The results clearly indicate that
dimensionality reduction has had a
positive effect on the classification
results.
The limitations of the study are the
relatively small dataset, nonuniform
distribution of cases among the levels
of disc degeneration, and the absence
of level V cases in the dataset used.
3. AUTHOR &YEAR TOPIC OBJECTIVE METHODOLOGY METRICS USED REMARKS
Pratik Shrestha,
Aachal Singh, Riya
Garg, Ishika Sarraf,
Mahesh T R, Sindhu
Madhuri G(2021)
Early Stage
Detection of
Scoliosis Using
Machine Learning
Algorithms
The paper presents a
five-stage method that
involves input image,
pre-processing, training
and testing,
classification, and
performance metrics.
And also compares the
accuracy and elapsed
time of linear regression
and SVM algorithms.
Linear regression and
SVM algorithm
X-ray Image SVM achieves better results with
85.67% accuracy and 15.438
seconds elapsed time.
The paper concludes that SVM is the
best approach for detecting scoliosis
automatically and provides benefits
such as less time, less cost, and early
stage predictive analysis.
Md. Shariful Islam,
Md. Asaduzzaman,
Mohammad Masudur
Rahman(2019)
Feature Selection and
Classification of
Spinal Abnormalities
to Detect Low Back
Pain Disorder using
Machine Learning
Approaches
The paper uses a setwise
evolutionary based
wrapper paradigm to
identify the most
influential features and
discard the less relevant
ones. It combines
multiple feature selection
algorithms such as LVQ,
RFE, and UST.
Algorithms such as
LVQ, random forest
and UST
310 patient data with
12 features
It finds that Random Forest classifier
achieves the best accuracy of 94%
with feature selection.
The paper also analyses the
contribution of each feature towards
the abnormality of the spine.
4. AUTHOR &YEAR TOPIC OBJECTIVE METHODOLOGY METRICS USED REMARKS
Jiabo He, Wei Liu, Yu
Wang , Xingjun Ma ,
Xian-Sheng Hua
SpineOne: A One-
Stage Detection
Framework for
Degenerative Discs
and Vertebrae
The paper introduces
three key techniques to
improve the detection
performance.
One-channel-per-
class(OCPC)
550 MRI The paper claims that the proposed
method is more efficient and accurate
than two-stage methods, and that the
novel techniques are generic and can
be applied to other medical diagnosis
tasks.
Zhenda Xu, Jiahao
Hu, Qiang Gao,
Donghua Hang,
Qihua Zhou, Song
Guo, Aiqian Gan
Development of
Deep Learning
Algorithms for
Automated Scoliosis
and Abnormal
Posture Screening
Using 2D Back
Image
The system aims to
overcome the limitations
of conventional
screening methods and
provide a cost-free, fast,
accurate, and radiation-
free solution.
Deep learning X-ray image age
between 6-24
The paper reports that the system
achieves an overall classification
accuracy of 88.1% on the test set and
can correctly detect mild scoliosis and
categorize postural abnormalities.
5. AUTHOR &YEAR TOPIC OBJECTIVE METHODOLOGY METRICS USED REMARKS
Audrey Ha, John
Vorhies, Andrew
Campion, Charles
Fang, Michael Fadell
II, Steve Dou,
Safwan Halabi,
David Larson, Emily
Wang, YongJin Lee,
Joanna Langner,
Japsimran Kaur, Bao
Do(2020)
Automatic Extraction
of Skeletal Maturity
from Whole Body
Pediatric Scoliosis X-
rays Using Regional
Proposal and
Compound Scaling
Convolutional Neural
Networks
In this paper, to detect
and classify multiple
skeletal maturity
indicators from scoliosis
x-rays, such as the
humeral head and the
modified Oxford Bone
Score regions.
CNN and Machine
learning
X-ray images In this paper the system achieved an
F1-score of 0.99 for regional detection,
an overall accuracy of 89% and an
intraclass correlation coefficient of
0.84 for staging models, and a
processing time of less than 45
seconds per study.
Anjany Sekuboyina
(2021)
VerSe: A Vertebrae
labelling and
segmentation
benchmark for multi-
detector CT images
A total of 25 algorithms
were benchmarked on
these datasets. In this
work, they present the
results of this evaluation
and further investigate
the performance
variation at the vertebra
level, scan level, and
different fields of view.
U-Net and Deep
learning
374 CT scan The authors summarise the main
contributions and findings of the VerSe
challenges, which are the largest spine
dataset to date, the evaluation and
benchmarking of 25 algorithms for
vertebral labelling and segmentation,
and the in-depth analysis of the
algorithms’ behaviour in terms of
spine region, fields of view, and
manual effort.
6. AUTHOR &YEAR TOPIC OBJECTIVE METHODOLOGY METRICS USED REMARKS
Shu Liao, Yiqiang
Zhan, Zhongxing
Dong, Ruyi Yan,
Liyan Gong, Xiang
Sean Zhou, Marcos
Salganicoff and Jun
Fei (2015)
Automatic Lumbar
Spondylolisthesis
Measurement in CT
Images
The paper uses a
hierarchical learning
approach to detect and
label each lumbar
vertebrae and inter-
vertebral disc in CT
images.
Hierarchical learning 258 CT Scan The paper evaluates the proposed
framework on 258 CT
spondylolisthesis patients, and shows
that the measurement derived by the
method is very similar to the manual
measurement by radiologists and
significantly increases the
measurement efficiency.
Fatih Varçın, Hasan
Erbay, Eyüp Çetin
Diagnosis of Lumbar
Spondylolisthesis via
Convolutional Neural
Networks
In this paper, solution to
the problem of diagnosis
of spondylolisthesis by
using two well known
artificial neural networks
AlexNet and GoogleLen.
CNN and transfer
learning
272 x-ray The paper evaluates the performance
of the models using metrics such as
accuracy, sensitivity, specificity, and
F1 score3. The results show that
GoogLeNet performs slightly better
than AlexNet on all metrics45. The
paper concludes that the proposed
method is an encouraging start for
diagnosing lumbar pathologies.
7. AUTHOR &YEAR TOPIC OBJECTIVE METHODOLOGY METRICS USED REMARKS
Deepika Saravagi ,
Shweta Agrawal,
Manisha
Saravagi,Jyotir Moy
Chatterjee ,and Mohit
Agarwal
Diagnosis of Lumbar
Spondylolisthesis
Using Optimized
Pretrained CNN
Models
CNN model, VGG16
model prepared.
Deep learning model 299 x-ray Data augmentation is used to increase
the sample size. VGG16 model has
achieved 98% accuracy rate.
Models may be used as a substitute for
manual radiological analysis and can
help clinicians to diagnose
spondylolisthesis from spine X-ray
data automatically, further study is
needed for grading spondylolisthesis
through X-ray images.
Syed furqan qadri,
linlin shen, mubashir
ahmad, salman qadri ,
syeda shamaila
zareen5 , and salabat
khan (2021)
OP-convNet: A Patch
Classification-Based
Framework for CT
Vertebrae
Segmentation
The paper proposes an
OP-convNet model that
divides the CT image
slices into equal-sized
square overlapping
patches and applies a
random under-sampling
function(RUS-Function)
for class balancing.
Deep learning and
CNN
CT scan The model outputs a binary label for
each patch, indicating whether it
belongs to the vertebrae or the
background. The paper also describes
the preprocessing, data augmentation,
and post-processing steps of the
proposed method.
OP-convNet has precision(PRE) of
90.1%,specificity (SPE) of 99.4%,
accuracy (ACC) of 98.8%, F-score of
90.1% in terms of the patch-based
classification accuracy, and BF-score
of 90.2%, sensitivity (SEN) of 90.3%,
Jaccard index (JAC) of 82.3%.
8. AUTHOR
&YEAR
TOPIC OBJECTIVE METHODOLOG
Y
METRICS USED REMARKS
SK. Hasane
Ahammad,
V.Rajesh, Md.zia
Ur Rahman
(2020)
A Hybrid CNN
Based
Segmentation And
Boosting Classifier
For Real Time
Sensor Spinal Cord
injury Data
The proposed model is
a novel CNN-deep
segmentation based
boosting classifier that
uses a real-time
wearable sensor to
capture the SCI data
and performs
segmentation and
classification using a
hybrid CNN-SVM and
CNN-RF approach.
Deep Learning,
Embedded sensor,
random Forest,
SVM
Spinal Cord Injury
Data
This method deep learning
framework optimizes nearly 10%
improvement on the classification
rate and segmentation quality
compared to other models.
Experimental results proved that the
present model has better
performance than the existing spinal
cord injury detection models in
terms of true positive rate;
TP=0.9859, Accuracy=0.9894, and
Error rate=0.019 are concerned.
.
Ala s. al-kafri , Sud
sudirman , Abir
hussain , dhiya al-
jumeily , friska
natalia , hira
meidia , nunik
afriliana , wasfi al-
rashdan ,
mohammad
bashtawi, and
mohammed al-
jumaily (2018)
Boundary
Delineation of MRI
Images for Lumbar
Spinal Stenosis
Detection Through
Semantic
Segmentation Using
Deep Neural
Networks
In this paper, lumbar
spinal stenosis
detection through
semantic segmentation
and delineation of
magnetic resonance
imaging (MRI) scans
of the lumbar spine
using deep learning.
Deep learning. MRI scan with
symptomatic back
pain patients data
set used.
The model’s performance is within
the range of manual labelling
performance .
The ground-truth dataset has an
excellent inter-rater agreement score.
The mean accuracy in segmentation
is consistently lower in the
unregistered class.
9. AUTHOR &YEAR TOPIC OBJECTIVE METHODOLOGY METRICS USED REMARKS
Malaika Mushtaq ,
Muhammad Usman
Akram , Norah Saleh
Alghamdi , Joddat
Fatima ,and Rao Farhat
Masood (2022)
Localization and
Edge-Based
Segmentation of
Lumbar Spine
Vertebrae to
Identify the
Deformities Using
Deep Learning
Models
In this paper, the
localization and
segmentation of
the lumbar spine,
which aid in the
analysis of
lumbar spine
abnormalities.
Deep learning; Sensors localization; lumbar
lordortic angle;
lumbosacral angle;
lumbar spine; edge-
based segmentation
They have high computational
complexity.
This work can be extended to diagnose
cervical, thoracic spine, and pelvic
region deformities. Other may be used
to investigate and develop a fully
automated machine learning toolkit for
spinal deformities to prevent invasive
surgery methods.
Dong-sik Chaea , Thong
Phi Nguyenb, Sung-Jun
Parkc , Kyung-Yil Kang
d, Chanhee Wonb ,
Jonghun Yoone, (2020)
Decentralized
convolutional
neural network for
evaluating spinal
deformity with
spinopelvic
parameters
This paper
presents an
automated
method for
precisely
measuring
spinopelvic
parameters using
a decentralized
convolutional
neural network as
an efficient
replacement for
current manual
process.
Artificial intelligent,
Orthopaedic,
Convolutional neural
network
According to key
points obtained,
parameters
representing the
spinal deformity are
calculated, which
consistency with
manual measurement
was validated by 40
test cases.
The improvement in accuracy caused
by an increase in number of orders was
also verified by comparing the results
in cases of 3 and 4 orders, which
suggests directions for application in
multiple fields requiring precise
measurement using a limited dataset.
They have also an serror detection.
10. AUTHOR &YEAR TOPIC OBJECTIVE METHODOLO
GY
METRICS
USED
REMARKS
SK. HASANE
AHAMMAD, V.
RAJESH, AND
MD. ZIA UR
RAHMAN
(2019)
Fast and Accurate
Feature Extraction-
Based Segmentation
Framework for
Spinal Cord Injury
Severity
Classification
In this paper a
novel segment-
based classification
model which
determine the
extent of the
damage and
forecast the illness
patterns on the
excessively
segmented regions
and features.
Machine learning,
spinal cord image,
support vector
machine,
segmentation.
MRI image. In the future work, a novel multi-
level segmentation-based
classification approach will be
implemented on the gender wise
spinal cord images to improve the
error rate and accuracy.
Also, the noises in the T1-weighted
and T2-weighted regions are
optimized in order to improve the
classification accuracy in the older
age SCI images.
Faisal rehman ,
Syed irtiza ali shah ,
Naveed riaz , and
Syed omer gilani
(2019)
A Robust Scheme of
Vertebrae
Segmentation for
Medical Diagnosis
In this paper, they
proposed a novel
and efficient
framework to
address the subject
problem by
integrating a
parametric level set
approach in deep
convolutional
neural networks.
Deep Neural
network
MRI image and
CT scan image
Segmentation performance degrades
with substantial topological shape
variability.
In future, they can extend this work
to multimodality images or datasets
from different scanners in order to
build a robust system.
And also developed a simultaneous
segmentation scheme that can
perform cervical, thoracic and
lumbar vertebrae segmentation over
a single platform.
11. AUTHOR &YEAR TOPIC OBJECTIVE METHODOLOGY METRICS USED REMARKS
Jiajun Zhanga,b , Ka-
yee Cheuka,b , Leilei
Xub,c , Yujia Wanga,b ,
Zhenhua Fengb,c , Tony
Sitd , Ka-lo Chenga,b ,
Evguenia
Nepotchatykhe , Tsz-
ping Lama,b , Zhen
Liub,c , Alec L.H.
Hunga,b , Zezhang
Zhub,c , Alain
Moreaue,f,g , Jack C.Y.
Chenga,b, Yong Qiub,c,
Wayne Y.W.
Leea,b,(2020)
A validated
composite model
to predict risk of
curve progression
in adolescent
idiopathic
scoliosis
In this paper, create a
composite model for
prediction, patients
with AIS were
tracked for a
minimum of six
years.
Scoliosis Adolescent
Clinical study
In this paper, a two
phase study with an
exploration group of
120 Adolescent
idiopathic
scoliosis(AIS) and a
validation cohort.
In this paper, they provide very less
information for clinical decision making.
Ben Glocker, Darko
Zikic, Ender
Konukoglu, David R.
Haynor, and Antonio
Criminisi
Vertebrae
Localization in
Pathological Spine
CT via Dense
Classification
from Sparse
Annotations
In this paper they
proposed a robust
localization and
identification
algorithm which
builds upon
supervised
classification forests
and avoids an
explicit parametric
model of appearance.
Supervised
classification forests
CT scans image, MRI
image
In this paper, they improve the centroid
estimation.
12. AUTHOR &YEAR TOPIC OBJECTIVE METHODOLOGY METRICS USED REMARKS
Ben Glocker,J. Feulner,
Antonio Criminisi1, D.R.
Haynor, and E. Konukoglu
Automatic
Localization and
Identification of
Vertebrae in
Arbitrary Field-of-
View CT Scan
In this paper they have
presented a new method
for automatic
localization and
identification of
vertebrae in arbitrary
field-of-view CT scans.
Regression forests and
probabilistic graphical
model
CT scan In this paper, they will
be increase the amount
of training data, in
particular, for the
cervical region
Bizhan Aarabi,Chen
Chixiang, J. Marc
Simard,Timothy
Chryssikos, Jesse A.
Stokum, Charles A. Sansur,
Kenneth M. Crandall,Joshua
Olexa,Jeffrey Oliver,
Melissa R. Meister,Gregory
Cannarsa, Ashish Sharma,
Cara Lomangino, Maureen
Scarboro,Abdul-Kareem
Ahmed, Nathan
Han,Riccardo Serra, Phelan
Shea, Carla Aresco, and
Gary T. Schwartzbauer,
Proposal of a
Management
Algorithm to Predict
the Need for
Expansion
Duraplasty in
American Spinal
Injury Association
Impairment Scale
Grades A–C
Traumatic Cervical
Spinal Cord Injury
Patients
In this paper to identify
patient for expansion
duraplasty, based on the
absence of cerebrospinal
fluid (CSF) interface
around the spinal cord
on magnetic resonance
imaging (MRI), in the
setting of otherwise
adequate bony
decompression.
Decompression,
duraplasty,
neuroprotection
MRI scan In this paper, they have
implement the CNN for such
a TSCI data set.