Skin lesion detection from dermoscopic images using Convolutional Neural Netw...Adrià Romero López
This thesis focuses on automatic skin lesion detection and melanoma classification from dermoscopic images using deep learning. For segmentation, a U-Net convolutional neural network is used to accurately extract the lesion region. For classification, a VGG-Net ConvNet with transfer learning performs binary classification of lesions as benign or malignant. Experimental results on the ISIC Archive dataset show classification accuracy in the top three of previous works, and the segmentation model outperforms other state-of-the-art methods. The thesis represents the completion of the author's studies in audiovisual systems engineering.
The document summarizes a study that uses neural networks to detect breast lesions in medical digital images. The study aims to improve existing neural network architectures for better detection of possible lesions. Medical images are preprocessed and classified by neural networks to detect suspicious areas. The study presents a method using multilayer perceptrons trained through backpropagation to analyze image features and classify tissues as benign or malignant.
Filter technique of medical image on multiple morphological gradient methodTELKOMNIKA JOURNAL
Filter technique is supportive for reducing image noise. This paper presents a study on filtering medical images, i.e., CT-Scan, Chest X-ray and Panoramic X-ray collected from two of the most prominent public hospitals in Padang City, Indonesia. The aim of this study preserved to facilitate in diagnosing objects in x-ray medical images. This study used filter technique, i.e. Blur, Emboss, Gaussian, Laplacian, Roberts, Sharpen, or Sobel techniques as pre-processing step. The filter process performed before edge detection and edge clarification. MMG method used in this study to clarify the edge detection. Thus, this research showed the hesitation decline (confidence increase) of the diagnosis of objects contained in medical images.
Automatic Diagnosis of Abnormal Tumor Region from Brain Computed Tomography I...ijcseit
The research work presented in this paper is to achieve the tissue classification and automatically
diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet
based statistical texture analysis method. Comparative studies of texture analysis method are performed
for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method
(SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii)
Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A
wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm
(GA) is used to select the optimal texture features from the set of extracted texture features. We construct
the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by
comparing the classification results of the SVM based classifier with the Back Propagation Neural network
classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis
methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results
show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system
has been tested with a number of real Computed Tomography brain images and has achieved satisfactory
results.
IRJET- Detection and Classification of Skin Diseases using Different Colo...IRJET Journal
This document discusses methods for detecting and classifying skin diseases using image processing techniques. It first presents an abstract that outlines how image processing has played a major role in identifying skin diseases by techniques like filtering, segmentation, feature extraction and edge detection. It then reviews literature on different skin disease detection systems using these image processing methods. The proposed methodology extracts features from input skin disease images using two color phase models: HSV and LAB. These features are then classified using a k-nearest neighbor algorithm to identify the disease. Results show the HSV model achieved higher accuracy than LAB in detecting and classifying five common diseases.
A Wavelet Based Automatic Segmentation of Brain Tumor in CT Images Using Opti...CSCJournals
This document summarizes a research paper that proposes a new method for automatically segmenting brain tumors in CT images. The method uses a combination of wavelet-based texture features extracted from discrete wavelet transformed sub-bands. These features are optimized using genetic algorithms and used to train probabilistic neural network and feedforward neural network classifiers to segment tumors. The proposed method is evaluated on brain CT images and shown to outperform existing segmentation methods.
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%.
1) The document presents a method for detecting skin lesions using support vector machines (SVM). It involves preprocessing images, segmenting the skin lesion region, extracting features related to shape, color, and texture, and classifying lesions as melanoma or non-melanoma using an SVM classifier.
2) Features extracted include asymmetry, border irregularity, compactness, color ratios in HSV, RGB and LAB color spaces, and texture features from the gray-level co-occurrence matrix.
3) An SVM classifier is used for classification as it can accurately classify data by finding the optimal separating hyperplane that maximizes the margin between the classes. The method achieved efficient classification of lesions.
Skin lesion detection from dermoscopic images using Convolutional Neural Netw...Adrià Romero López
This thesis focuses on automatic skin lesion detection and melanoma classification from dermoscopic images using deep learning. For segmentation, a U-Net convolutional neural network is used to accurately extract the lesion region. For classification, a VGG-Net ConvNet with transfer learning performs binary classification of lesions as benign or malignant. Experimental results on the ISIC Archive dataset show classification accuracy in the top three of previous works, and the segmentation model outperforms other state-of-the-art methods. The thesis represents the completion of the author's studies in audiovisual systems engineering.
The document summarizes a study that uses neural networks to detect breast lesions in medical digital images. The study aims to improve existing neural network architectures for better detection of possible lesions. Medical images are preprocessed and classified by neural networks to detect suspicious areas. The study presents a method using multilayer perceptrons trained through backpropagation to analyze image features and classify tissues as benign or malignant.
Filter technique of medical image on multiple morphological gradient methodTELKOMNIKA JOURNAL
Filter technique is supportive for reducing image noise. This paper presents a study on filtering medical images, i.e., CT-Scan, Chest X-ray and Panoramic X-ray collected from two of the most prominent public hospitals in Padang City, Indonesia. The aim of this study preserved to facilitate in diagnosing objects in x-ray medical images. This study used filter technique, i.e. Blur, Emboss, Gaussian, Laplacian, Roberts, Sharpen, or Sobel techniques as pre-processing step. The filter process performed before edge detection and edge clarification. MMG method used in this study to clarify the edge detection. Thus, this research showed the hesitation decline (confidence increase) of the diagnosis of objects contained in medical images.
Automatic Diagnosis of Abnormal Tumor Region from Brain Computed Tomography I...ijcseit
The research work presented in this paper is to achieve the tissue classification and automatically
diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet
based statistical texture analysis method. Comparative studies of texture analysis method are performed
for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method
(SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii)
Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A
wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm
(GA) is used to select the optimal texture features from the set of extracted texture features. We construct
the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by
comparing the classification results of the SVM based classifier with the Back Propagation Neural network
classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis
methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results
show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system
has been tested with a number of real Computed Tomography brain images and has achieved satisfactory
results.
IRJET- Detection and Classification of Skin Diseases using Different Colo...IRJET Journal
This document discusses methods for detecting and classifying skin diseases using image processing techniques. It first presents an abstract that outlines how image processing has played a major role in identifying skin diseases by techniques like filtering, segmentation, feature extraction and edge detection. It then reviews literature on different skin disease detection systems using these image processing methods. The proposed methodology extracts features from input skin disease images using two color phase models: HSV and LAB. These features are then classified using a k-nearest neighbor algorithm to identify the disease. Results show the HSV model achieved higher accuracy than LAB in detecting and classifying five common diseases.
A Wavelet Based Automatic Segmentation of Brain Tumor in CT Images Using Opti...CSCJournals
This document summarizes a research paper that proposes a new method for automatically segmenting brain tumors in CT images. The method uses a combination of wavelet-based texture features extracted from discrete wavelet transformed sub-bands. These features are optimized using genetic algorithms and used to train probabilistic neural network and feedforward neural network classifiers to segment tumors. The proposed method is evaluated on brain CT images and shown to outperform existing segmentation methods.
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%.
1) The document presents a method for detecting skin lesions using support vector machines (SVM). It involves preprocessing images, segmenting the skin lesion region, extracting features related to shape, color, and texture, and classifying lesions as melanoma or non-melanoma using an SVM classifier.
2) Features extracted include asymmetry, border irregularity, compactness, color ratios in HSV, RGB and LAB color spaces, and texture features from the gray-level co-occurrence matrix.
3) An SVM classifier is used for classification as it can accurately classify data by finding the optimal separating hyperplane that maximizes the margin between the classes. The method achieved efficient classification of lesions.
Brain Tumor Detection using MRI ImagesYogeshIJTSRD
Brain tumor segmentation is a very important task in medical image processing. Early diagnosis of brain tumors plays a crucial role in improving treatment possibilities and increases the survival rate of the patients. For the study of tumor detection and segmentation, MRI Images are very useful in recent years. One of the foremost crucial tasks in any brain tumor detection system is that the detachment of abnormal tissues from normal brain tissues. Because of MRI Images, we will detect the brain tumor. Detection of unusual growth of tissues and blocks of blood within the system is seen in an MRI Imaging. Brain tumor detection using MRI images may be a challenging task due to the brains complex structure.In this paper, we propose an image segmentation method to detect tumors from MRI images using an interface of GUI in MATLAB. The method of distinguishing brain tumors through MRI images is often sorted into four sections of image processing as pre processing, feature extraction, image segmentation, and image classification. During this paper, weve used various algorithms for the partial fulfillment of the necessities to hit the simplest results that may help us to detect brain tumors within the early stage. Deepa Dangwal | Aditya Nautiyal | Dakshita Adhikari | Kapil Joshi "Brain Tumor Detection using MRI Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advances in Engineering, Science and Technology - 2021 , May 2021, URL: https://www.ijtsrd.com/papers/ijtsrd42456.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/42456/brain-tumor-detection-using-mri-images/deepa-dangwal
SVM Classifiers at it Bests in Brain Tumor Detection using MR Imagesijtsrd
This paper presents some case study frameworks to limelight SVM classifiers as most efficient one compared to existing classifiers like Otsu, k-means and fuzzy c-means. In general, Computed Tomography (CT) and Magnetic Resonance Imaging (MR) are more dominant imaging technique for any brain lesions detection like brain tumor, Alzheimer' disease and so on. MR imaging takes a lead technically for imaging medical images due to its possession of large spatial resolution and provides better contrast for the soft tissues like white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The usual method used for classification of lesions in brain images consists of pre-processing, feature extraction, feature reduction and classification. Early detection of the tumor region without much time lapse in computation can be achieved by using efficient SVM classifier model. Brain tumor grade classifications with the assistance of morphologically selected features are extracted and tumor classification is attained using SVM classifier. The assessment of SVM classifications are evaluated through metrics termed as sensitivity, exactness and accuracy of segmentation. These measures are then compared with existing methods to exhibit the SVM classifier as significant classifier model. Dr. R Manjunatha Prasad | Roopa B S"SVM Classifiers at it Bests in Brain Tumor Detection using MR Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18372.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/18372/svm-classifiers-at-it-bests-in-brain-tumor-detection-using-mr-images/dr-r-manjunatha-prasad
IRJET- Lung Diseases using Deep Learning: A Review PaperIRJET Journal
This document reviews research on using deep learning techniques to detect lung diseases from medical images. It first provides background on lung anatomy and common lung diseases like pneumonia, tuberculosis, and lung cancer. Feature extraction and classification algorithms are important for automated detection. Several studies are summarized that used techniques like convolutional neural networks (CNNs) and support vector machines (SVMs) with shape, texture, and focal features to classify chest x-rays and CT scans as normal or abnormal. Deep learning approaches achieved higher accuracy than traditional "bag of features" methods. Overall, CNNs showed potential for developing high-performance automated lung disease detection systems.
Segmentation and Classification of Skin Lesions Based on Texture FeaturesIJERA Editor
Skin cancer is the most common type of cancer and represents 50% all new cancers detected each year. The deadliest form of skin cancer is melanoma and its incidence has been rising at a rate of 3% per year. Due to the costs for dermatologists to monitor every patient, there is a need for an computerized system to evaluate a patient‘s risk of melanoma using images of their skin lesions captured using a standard digital camera. In Proposed method, a novel texture-based skin lesion segmentation algorithm is used and to classify the stages of skin cancer using probabilistic neural network. Probabilistic neural network will give better performance in this system to detect a lot of stages in skin lesion. To extract the characteristics from various skin lesions and its united features gives better classification with new approached probabilistic neural network. There are five different skin lesions commonly grouped as Actinic Keratosis (AK), Basal Cell Carcinoma (BCC), Melanocytic Nevus / Mole (ML), Squamous Cell Carcinoma (SCC), Seborrhoeic Keratosis (SK). The system will be used to classify the queried images automatically to decide the stages of abnormality. The lesion diagnosis system involves two stages of process such as training and classification. Feature selection is used in the classified framework that chooses the most relevant feature subsets at each node of the hierarchy. An automatic classifier will be used for classification based on learning with some training samples of each stage. The accuracy of the proposed neural scheme is higher in discriminating cancer and pre-malignant lesions from benign skin lesions, and it attains an total classification accuracy is high of skin lesions.
IRJET- Detection & Classification of Melanoma Skin CancerIRJET Journal
This document discusses methods for detecting and classifying melanoma skin cancer. It begins with an introduction to skin cancer and the importance of detecting melanoma early. It then reviews literature on existing techniques for melanoma detection using image processing and machine learning. The proposed system uses image segmentation, feature extraction using the ABCD criteria, principal component analysis to select key features, and support vector machine classification to determine whether images contain cancerous or non-cancerous lesions. The system aims to provide an accurate and fast evaluation of skin lesions to help in melanoma diagnosis.
Extracting Features from the fundus image using Canny edge detection method f...vivatechijri
The document discusses extracting features from fundus images using the Canny edge detection method to enable pre-detection of diabetic retinopathy. It begins by introducing diabetic retinopathy and the importance of early detection. It then discusses applying Canny edge detection to extract features like edges from fundus images. The features extracted can be used for diabetic retinopathy detection using machine learning methods. Results show Canny edge detection successfully extracted features like edges from sample images that could aid diabetic retinopathy diagnosis. The technique presents a potential method for automated pre-detection of the disease.
Automatic Skin Lesion Segmentation and Melanoma Detection: Transfer Learning ...Zabir Al Nazi Nabil
Industrial pollution resulting in ozone layer depletion has influenced
increased UV radiation in recent years which is a major environmental risk factor for invasive skin cancer Melanoma and other keratinocyte cancers. The incidence of deaths from Melanoma has risen worldwide in past two decades.
Deep learning has been employed successfully for dermatologic diagnosis. In
this work, we present a deep learning based scheme to automatically segment
skin lesions and detect melanoma from dermoscopy images. U-Net was used
for segmenting out the lesion from surrounding skin. The limitation of utilizing
deep neural networks with limited medical data was solved with data augmentation and transfer learning. In our experiments, U-Net was used with spatial
dropout to solve the problem of overfitting and different augmentation effects
were applied on the training images to increase data samples. The model was
evaluated on two different datasets. It achieved a mean dice score of 0.87 and a
mean jaccard index of 0.80 on ISIC 2018 dataset. The trained model was assessed on PH² dataset where it achieved a mean dice score of 0.93 and a mean
jaccard index of 0.87 with transfer learning. For classification of malignant
melanoma, a DCNN-SVM model was used where we compared state of the art
deep nets as feature extractors to find the applicability of transfer learning in
dermatologic diagnosis domain. Our best model achieved a mean accuracy of
92% on PH² dataset. The findings of this study is expected to be useful in cancer diagnosis research.
Published at IJCCI 2018. Source code available at https://github.com/zabir-nabil/lesion-segmentation-melanoma-tl
Lung Cancer Detection using Machine Learningijtsrd
Modern three dimensional 3 D medical imaging offers the potential and promise for major advances in science and medicine as higher fidelity images are produced. Due to advances in computer aided diagnosis and continuous progress in the field of computerized medical image visualization, there is need to develop one of the most important fields within scientific imaging. From the early basis report on cancer patients it has been seen that a greater number of people die of lung cancer than from other cancers such as colon, breast and prostate cancers combined. Lung cancer are related to smoking or secondhand smoke , or less often to exposure to radon or other environmental factors that’s why this can be prevented. But still it is not yet clear if these cancers can be prevented or not. In this research work, approach of segmentation, feature extraction and Convolution Neural Network CNN will be applied for locating, characterizing cancer portion. Harpreet Singh | Er. Ravneet Kaur | "Lung Cancer Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33659.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-architecture/33659/lung-cancer-detection-using-machine-learning/harpreet-singh
MALIGNANT AND BENIGN BRAIN TUMOR SEGMENTATION AND CLASSIFICATION USING SVM WI...sipij
In this article a method is proposed for segmentation and classification of benign and malignant tumor slices in brain Computed Tomography (CT) images. In this study image noises are removed using median and wiener filter and brain tumors are segmented using Support Vector Machine (SVM). Then a two-level discrete wavelet decomposition of tumor image is performed and the approximation at the second level is obtained to replace the original image to be used for texture analysis. Here, 17 features are extracted that 6 of them are selected using Student’s t-test. Dominant gray level run length and gray level co-occurrence texture features are used for SVM training. Malignant and benign tumors are classified using SVM with kernel width and Weighted kernel width (WSVM) and k-Nearest Neighbors (k-NN) classifier. Classification accuracy of classifiers are evaluated using 10 fold cross validation method. The segmentation results are
also compared with the experienced radiologist ground truth. The experimental results show that the proposed WSVM classifier is able to achieve high classification accuracy effectiveness as measured by sensitivity and specificity.
IRJET- Color and Texture based Feature Extraction for Classifying Skin Ca...IRJET Journal
This document presents a method to classify skin cancer images as malignant or benign using color and texture feature extraction with support vector machine (SVM) and convolutional neural network (CNN) classifiers. The method segments skin cancer images from the ISIC dataset using active contour modeling. Color features are extracted using histogram analysis in HSV color space. Texture features like mean, variance, skewness and kurtosis are calculated statistically. Both SVM and CNN are used to classify the images based on these features, and CNN achieves higher average accuracy than SVM. The CNN approach is therefore proven more effective for skin cancer classification using color and texture features.
Diagnosis of Burn Images using Template Matching, k-Nearest Neighbor and Arti...CSCJournals
The aim of this research is to develop an automated method of determining the severity of skin burn wounds. Towards achieving this aim, a database of skin burn images has been created by collecting images from hospitals, doctors and the Internet. The initial pre-processing involves contrast enhancement in lab color space by taking luminance component. Various pattern analysis or pattern classifier techniques viz. Template Matching (TM), k Nearest Neighbor Classifier (kNN) and Artificial Neural Network (ANN) have been applied on skin burn images and a performance comparison of the three techniques has been made. The help of dermatologists and plastic surgeons has been taken to label the images with skin burn grades and are used to train the classifiers. The algorithms are optimized on pre-labeled images, by fine-tuning the classifier parameters. During the course of research, of the three classifier methods used for classification of burn images it has been observed that the ANN technique reflected the best results. This has been inferred based on the comparative studies of the three methods. In the ANN method the classification of the image of burns has been found to be the nearest to the actual burns. The efficiency of the analysis and classification of the ANN technique has been of the order of 95% for Grade-1 burns, 97.5% for Grade-2 burns and 95% for Grade-3 burns. As compared to 55%, 72.5% and 70% for Grade1, Grade2, and Grade 3 burns respectively for the TM Method and 67.5%, 82.5% and 75% for kNN method. It is therefore felt that the ANN technique could be applied to analyze and classify the severity of burns. This burn analysis technique could be safely used in remote location where specialists’ services are not readily available. The local doctors could use the analyzer and classify the grade of the burn with a good degree of accuracy and certainty. They could start preliminary treatment accordingly, prior to specialists’ services. This would definitely go a long way in mitigating the pain and sufferings of the patients.
Tumor Detection from Brain MRI Image using Neural Network Approach: A ReviewIRJET Journal
This document reviews using neural networks to detect tumors in brain MRIs. It discusses how MRI is commonly used to diagnose soft tissue issues and analyze conditions like trauma and strokes. The paper proposes a methodology for brain tumor detection that includes image acquisition, pre-processing, enhancement, thresholding, and morphological operations using MATLAB. A neural network approach is also presented. The conclusions state that neural networks can help detect, classify, segment, and visualize brain tumors in MRI images with ease and accuracy.
Skin cancer is a term given to the uncontrolled
growth of strange skin cells. It occurs whenever unrepaired
DNA damages to skin cells trigger mutations, or any other
genetic defects, that lead the skin cells to multiply readily
and form malignant tumors. Image processing is a
commonly used method for skin cancer detection from the
appearance of the affected area on the skin. The input to the
system is that the skin lesion image so by applying novel
image process techniques, it analyses it to conclude about
the presence of skin cancer. The Lesion Image analysis tools
checks for the various Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, (ABCD rule), etc. by
texture, size and form analysis for image segmentation and
have stages. The extracted feature parameters are
accustomed classify the image as traditional skin and
malignant melanoma cancerlesion.
Artificial Neural Network (ANN) is one of the
important branches of Artificial Intelligence, which has
been accepted as a brand-new technology in computer
science for image processing. Neural Networks is currently
the area of interest in medicine, particularly in the fields of
radiology, urology, cardiology, oncology, etc. Neural
Network plays a vital role in an exceedingly call network. It
has been used to analyze Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, etc. which are
calculated using MATLAB from skin cancer images
intending to developing diagnostic algorithms that might
improve triage practices in the emergency department.
Using the ABCD rules for melanoma skin cancer, we use
ANN in the classification stage. Initially, we train the
network with known target values. The network is well
trained with 96.9% accuracy, and then the unknown values
are tested for the cancer classification. This classification
method proves to be more efficient for skin cancer
classification
This paper proposes the development of a software that performs the pre-diagnosis of malignant melanoma, spincellular carcinoma and basal-cell carcinoma. The software is divided into five modules, these being: digital imaging, analysis and processing, storage, feature extraction and classification by means of an Artificial Neural Network (ANN). The results shown the performance of the software for two different combination of activation functions in the network. With the use of spectroscopic techniques for the acquisition of images and the combination of non-linear and linear activation functions in the ANN, the software shows an effectiveness greater than 80%, concluding that it can be an effective tool as an aid in the diagnosis of cancer of skin.
New Approach of MA Detection & Grading Using Different ClassifiersIJSTA
This document discusses approaches for detecting microaneurysms (MAs), the earliest sign of diabetic retinopathy, in retinal images using machine learning classifiers. It first provides background on diabetic retinopathy and the importance of early MA detection. It then describes several existing methods for MA detection, including morphological approaches, fractal analysis, and using hue-saturation-value color models with eccentricity. The document goes on to propose a new ensemble-based approach combining different pre-processing techniques, candidate extraction methods, and classifiers to improve MA detection performance.
Blood vessel segmentation in fundus imagesIRJET Journal
This document summarizes a research paper on blood vessel segmentation in fundus images using random forest classification. The proposed method uses six steps: 1) spatial calibration of fundus images, 2) image preprocessing including illumination equalization, denoising and contrast equalization, 3) optic disc removal, 4) candidate lesion extraction, 5) dynamic shape feature extraction, and 6) random forest classification of lesions. The method was tested on public Diaretdb1 fundus image database and achieved accurate segmentation and detection of microaneurysms and hemorrhages for early diagnosis of diabetic retinopathy.
Image Segmentation and Identification of Brain Tumor using FFT Techniques of ...IDES Editor
The image processing tools are extensively used on
the development of new algorithms and mathematical tools
for the advanced processing of medical and biological images.
Given an MRI scan, first segment the tumor region in the
MRI brain image and study the pixel intensity values. A
detailed procedure using Matlab script is written to extract
tumor region in CT scan Brain Image and MRI Scan Brain
Image. MRI Scan has higher resolution and easier
identification compare to CT scan Brain image. Fast Fourier
Transform is used here to study the tumor region of MRI
Brain Image in terms of its pixel intensity. Types of FFT like
Zero padded FFT, Windowed FFT are used to study the signal
converted from the MRI Brain Image. It is found that lesser
spectral leakage for Zero Padded Windowed FFT than other
Types of FFT and hence the tumor cell identification is easier
than other methods. Finally higher pixel intensity values of
the cells gives identification of presence and activeness of
tumor cells.
Artificial neural network based cancer cell classificationAlexander Decker
This document summarizes an artificial neural network (ANN) based system called ANN-C3 for cancer cell classification using medical images. The system performs image pre-processing, segmentation using Harris corner detection and region growing, feature extraction of Tamura texture features, and classification using a neural network ensemble. Segmentation detects threshold points using Harris corner detection and performs region growing from these seed points. Feature extraction converts the image data into numerical form using Tamura texture features that capture variations in illumination and surfaces that human vision and surgeons use to differentiate cancerous and non-cancerous cells. The neural network is trained on a large set of labeled data to accurately classify cells.
IRJET - An Efficient Approach for Multi-Modal Brain Tumor Classification usin...IRJET Journal
This paper proposes an efficient approach for multi-modal brain tumor classification using texture features and machine learning. It uses the MICCAI BraTS 2016 dataset and segments tumors using fuzzy c-means clustering. It then extracts texture features like GLCM and LBP and classifies tumors as benign or malignant using an SVM classifier. The proposed method achieved accurate segmentation and classification of brain tumors in MRI images.
The document outlines the agenda and presentations for a Wireless Building Automation demonstration at Vooruit. The agenda includes general presentations on WBA and demonstrations of managing video surveillance over a wireless mesh network, intelligent video transmission and control, SANET use cases, indoor positioning, and SANET network solutions. The document also provides details on WBA, including its architecture, applications to building management systems, and research partners.
Presentatie mfconnect Museum Boerhaave en TwitterVera Bartels
Deze presentatie heb ik gegeven tijdens de conferentie Connect van MuseumFuture op 12 november 2010 in Zeist. De presentatie geeft een korte introductie op het twittergebruik van @museumboerhaave en een workshop gedeelte
Brain Tumor Detection using MRI ImagesYogeshIJTSRD
Brain tumor segmentation is a very important task in medical image processing. Early diagnosis of brain tumors plays a crucial role in improving treatment possibilities and increases the survival rate of the patients. For the study of tumor detection and segmentation, MRI Images are very useful in recent years. One of the foremost crucial tasks in any brain tumor detection system is that the detachment of abnormal tissues from normal brain tissues. Because of MRI Images, we will detect the brain tumor. Detection of unusual growth of tissues and blocks of blood within the system is seen in an MRI Imaging. Brain tumor detection using MRI images may be a challenging task due to the brains complex structure.In this paper, we propose an image segmentation method to detect tumors from MRI images using an interface of GUI in MATLAB. The method of distinguishing brain tumors through MRI images is often sorted into four sections of image processing as pre processing, feature extraction, image segmentation, and image classification. During this paper, weve used various algorithms for the partial fulfillment of the necessities to hit the simplest results that may help us to detect brain tumors within the early stage. Deepa Dangwal | Aditya Nautiyal | Dakshita Adhikari | Kapil Joshi "Brain Tumor Detection using MRI Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advances in Engineering, Science and Technology - 2021 , May 2021, URL: https://www.ijtsrd.com/papers/ijtsrd42456.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/42456/brain-tumor-detection-using-mri-images/deepa-dangwal
SVM Classifiers at it Bests in Brain Tumor Detection using MR Imagesijtsrd
This paper presents some case study frameworks to limelight SVM classifiers as most efficient one compared to existing classifiers like Otsu, k-means and fuzzy c-means. In general, Computed Tomography (CT) and Magnetic Resonance Imaging (MR) are more dominant imaging technique for any brain lesions detection like brain tumor, Alzheimer' disease and so on. MR imaging takes a lead technically for imaging medical images due to its possession of large spatial resolution and provides better contrast for the soft tissues like white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The usual method used for classification of lesions in brain images consists of pre-processing, feature extraction, feature reduction and classification. Early detection of the tumor region without much time lapse in computation can be achieved by using efficient SVM classifier model. Brain tumor grade classifications with the assistance of morphologically selected features are extracted and tumor classification is attained using SVM classifier. The assessment of SVM classifications are evaluated through metrics termed as sensitivity, exactness and accuracy of segmentation. These measures are then compared with existing methods to exhibit the SVM classifier as significant classifier model. Dr. R Manjunatha Prasad | Roopa B S"SVM Classifiers at it Bests in Brain Tumor Detection using MR Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18372.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/18372/svm-classifiers-at-it-bests-in-brain-tumor-detection-using-mr-images/dr-r-manjunatha-prasad
IRJET- Lung Diseases using Deep Learning: A Review PaperIRJET Journal
This document reviews research on using deep learning techniques to detect lung diseases from medical images. It first provides background on lung anatomy and common lung diseases like pneumonia, tuberculosis, and lung cancer. Feature extraction and classification algorithms are important for automated detection. Several studies are summarized that used techniques like convolutional neural networks (CNNs) and support vector machines (SVMs) with shape, texture, and focal features to classify chest x-rays and CT scans as normal or abnormal. Deep learning approaches achieved higher accuracy than traditional "bag of features" methods. Overall, CNNs showed potential for developing high-performance automated lung disease detection systems.
Segmentation and Classification of Skin Lesions Based on Texture FeaturesIJERA Editor
Skin cancer is the most common type of cancer and represents 50% all new cancers detected each year. The deadliest form of skin cancer is melanoma and its incidence has been rising at a rate of 3% per year. Due to the costs for dermatologists to monitor every patient, there is a need for an computerized system to evaluate a patient‘s risk of melanoma using images of their skin lesions captured using a standard digital camera. In Proposed method, a novel texture-based skin lesion segmentation algorithm is used and to classify the stages of skin cancer using probabilistic neural network. Probabilistic neural network will give better performance in this system to detect a lot of stages in skin lesion. To extract the characteristics from various skin lesions and its united features gives better classification with new approached probabilistic neural network. There are five different skin lesions commonly grouped as Actinic Keratosis (AK), Basal Cell Carcinoma (BCC), Melanocytic Nevus / Mole (ML), Squamous Cell Carcinoma (SCC), Seborrhoeic Keratosis (SK). The system will be used to classify the queried images automatically to decide the stages of abnormality. The lesion diagnosis system involves two stages of process such as training and classification. Feature selection is used in the classified framework that chooses the most relevant feature subsets at each node of the hierarchy. An automatic classifier will be used for classification based on learning with some training samples of each stage. The accuracy of the proposed neural scheme is higher in discriminating cancer and pre-malignant lesions from benign skin lesions, and it attains an total classification accuracy is high of skin lesions.
IRJET- Detection & Classification of Melanoma Skin CancerIRJET Journal
This document discusses methods for detecting and classifying melanoma skin cancer. It begins with an introduction to skin cancer and the importance of detecting melanoma early. It then reviews literature on existing techniques for melanoma detection using image processing and machine learning. The proposed system uses image segmentation, feature extraction using the ABCD criteria, principal component analysis to select key features, and support vector machine classification to determine whether images contain cancerous or non-cancerous lesions. The system aims to provide an accurate and fast evaluation of skin lesions to help in melanoma diagnosis.
Extracting Features from the fundus image using Canny edge detection method f...vivatechijri
The document discusses extracting features from fundus images using the Canny edge detection method to enable pre-detection of diabetic retinopathy. It begins by introducing diabetic retinopathy and the importance of early detection. It then discusses applying Canny edge detection to extract features like edges from fundus images. The features extracted can be used for diabetic retinopathy detection using machine learning methods. Results show Canny edge detection successfully extracted features like edges from sample images that could aid diabetic retinopathy diagnosis. The technique presents a potential method for automated pre-detection of the disease.
Automatic Skin Lesion Segmentation and Melanoma Detection: Transfer Learning ...Zabir Al Nazi Nabil
Industrial pollution resulting in ozone layer depletion has influenced
increased UV radiation in recent years which is a major environmental risk factor for invasive skin cancer Melanoma and other keratinocyte cancers. The incidence of deaths from Melanoma has risen worldwide in past two decades.
Deep learning has been employed successfully for dermatologic diagnosis. In
this work, we present a deep learning based scheme to automatically segment
skin lesions and detect melanoma from dermoscopy images. U-Net was used
for segmenting out the lesion from surrounding skin. The limitation of utilizing
deep neural networks with limited medical data was solved with data augmentation and transfer learning. In our experiments, U-Net was used with spatial
dropout to solve the problem of overfitting and different augmentation effects
were applied on the training images to increase data samples. The model was
evaluated on two different datasets. It achieved a mean dice score of 0.87 and a
mean jaccard index of 0.80 on ISIC 2018 dataset. The trained model was assessed on PH² dataset where it achieved a mean dice score of 0.93 and a mean
jaccard index of 0.87 with transfer learning. For classification of malignant
melanoma, a DCNN-SVM model was used where we compared state of the art
deep nets as feature extractors to find the applicability of transfer learning in
dermatologic diagnosis domain. Our best model achieved a mean accuracy of
92% on PH² dataset. The findings of this study is expected to be useful in cancer diagnosis research.
Published at IJCCI 2018. Source code available at https://github.com/zabir-nabil/lesion-segmentation-melanoma-tl
Lung Cancer Detection using Machine Learningijtsrd
Modern three dimensional 3 D medical imaging offers the potential and promise for major advances in science and medicine as higher fidelity images are produced. Due to advances in computer aided diagnosis and continuous progress in the field of computerized medical image visualization, there is need to develop one of the most important fields within scientific imaging. From the early basis report on cancer patients it has been seen that a greater number of people die of lung cancer than from other cancers such as colon, breast and prostate cancers combined. Lung cancer are related to smoking or secondhand smoke , or less often to exposure to radon or other environmental factors that’s why this can be prevented. But still it is not yet clear if these cancers can be prevented or not. In this research work, approach of segmentation, feature extraction and Convolution Neural Network CNN will be applied for locating, characterizing cancer portion. Harpreet Singh | Er. Ravneet Kaur | "Lung Cancer Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33659.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-architecture/33659/lung-cancer-detection-using-machine-learning/harpreet-singh
MALIGNANT AND BENIGN BRAIN TUMOR SEGMENTATION AND CLASSIFICATION USING SVM WI...sipij
In this article a method is proposed for segmentation and classification of benign and malignant tumor slices in brain Computed Tomography (CT) images. In this study image noises are removed using median and wiener filter and brain tumors are segmented using Support Vector Machine (SVM). Then a two-level discrete wavelet decomposition of tumor image is performed and the approximation at the second level is obtained to replace the original image to be used for texture analysis. Here, 17 features are extracted that 6 of them are selected using Student’s t-test. Dominant gray level run length and gray level co-occurrence texture features are used for SVM training. Malignant and benign tumors are classified using SVM with kernel width and Weighted kernel width (WSVM) and k-Nearest Neighbors (k-NN) classifier. Classification accuracy of classifiers are evaluated using 10 fold cross validation method. The segmentation results are
also compared with the experienced radiologist ground truth. The experimental results show that the proposed WSVM classifier is able to achieve high classification accuracy effectiveness as measured by sensitivity and specificity.
IRJET- Color and Texture based Feature Extraction for Classifying Skin Ca...IRJET Journal
This document presents a method to classify skin cancer images as malignant or benign using color and texture feature extraction with support vector machine (SVM) and convolutional neural network (CNN) classifiers. The method segments skin cancer images from the ISIC dataset using active contour modeling. Color features are extracted using histogram analysis in HSV color space. Texture features like mean, variance, skewness and kurtosis are calculated statistically. Both SVM and CNN are used to classify the images based on these features, and CNN achieves higher average accuracy than SVM. The CNN approach is therefore proven more effective for skin cancer classification using color and texture features.
Diagnosis of Burn Images using Template Matching, k-Nearest Neighbor and Arti...CSCJournals
The aim of this research is to develop an automated method of determining the severity of skin burn wounds. Towards achieving this aim, a database of skin burn images has been created by collecting images from hospitals, doctors and the Internet. The initial pre-processing involves contrast enhancement in lab color space by taking luminance component. Various pattern analysis or pattern classifier techniques viz. Template Matching (TM), k Nearest Neighbor Classifier (kNN) and Artificial Neural Network (ANN) have been applied on skin burn images and a performance comparison of the three techniques has been made. The help of dermatologists and plastic surgeons has been taken to label the images with skin burn grades and are used to train the classifiers. The algorithms are optimized on pre-labeled images, by fine-tuning the classifier parameters. During the course of research, of the three classifier methods used for classification of burn images it has been observed that the ANN technique reflected the best results. This has been inferred based on the comparative studies of the three methods. In the ANN method the classification of the image of burns has been found to be the nearest to the actual burns. The efficiency of the analysis and classification of the ANN technique has been of the order of 95% for Grade-1 burns, 97.5% for Grade-2 burns and 95% for Grade-3 burns. As compared to 55%, 72.5% and 70% for Grade1, Grade2, and Grade 3 burns respectively for the TM Method and 67.5%, 82.5% and 75% for kNN method. It is therefore felt that the ANN technique could be applied to analyze and classify the severity of burns. This burn analysis technique could be safely used in remote location where specialists’ services are not readily available. The local doctors could use the analyzer and classify the grade of the burn with a good degree of accuracy and certainty. They could start preliminary treatment accordingly, prior to specialists’ services. This would definitely go a long way in mitigating the pain and sufferings of the patients.
Tumor Detection from Brain MRI Image using Neural Network Approach: A ReviewIRJET Journal
This document reviews using neural networks to detect tumors in brain MRIs. It discusses how MRI is commonly used to diagnose soft tissue issues and analyze conditions like trauma and strokes. The paper proposes a methodology for brain tumor detection that includes image acquisition, pre-processing, enhancement, thresholding, and morphological operations using MATLAB. A neural network approach is also presented. The conclusions state that neural networks can help detect, classify, segment, and visualize brain tumors in MRI images with ease and accuracy.
Skin cancer is a term given to the uncontrolled
growth of strange skin cells. It occurs whenever unrepaired
DNA damages to skin cells trigger mutations, or any other
genetic defects, that lead the skin cells to multiply readily
and form malignant tumors. Image processing is a
commonly used method for skin cancer detection from the
appearance of the affected area on the skin. The input to the
system is that the skin lesion image so by applying novel
image process techniques, it analyses it to conclude about
the presence of skin cancer. The Lesion Image analysis tools
checks for the various Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, (ABCD rule), etc. by
texture, size and form analysis for image segmentation and
have stages. The extracted feature parameters are
accustomed classify the image as traditional skin and
malignant melanoma cancerlesion.
Artificial Neural Network (ANN) is one of the
important branches of Artificial Intelligence, which has
been accepted as a brand-new technology in computer
science for image processing. Neural Networks is currently
the area of interest in medicine, particularly in the fields of
radiology, urology, cardiology, oncology, etc. Neural
Network plays a vital role in an exceedingly call network. It
has been used to analyze Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, etc. which are
calculated using MATLAB from skin cancer images
intending to developing diagnostic algorithms that might
improve triage practices in the emergency department.
Using the ABCD rules for melanoma skin cancer, we use
ANN in the classification stage. Initially, we train the
network with known target values. The network is well
trained with 96.9% accuracy, and then the unknown values
are tested for the cancer classification. This classification
method proves to be more efficient for skin cancer
classification
This paper proposes the development of a software that performs the pre-diagnosis of malignant melanoma, spincellular carcinoma and basal-cell carcinoma. The software is divided into five modules, these being: digital imaging, analysis and processing, storage, feature extraction and classification by means of an Artificial Neural Network (ANN). The results shown the performance of the software for two different combination of activation functions in the network. With the use of spectroscopic techniques for the acquisition of images and the combination of non-linear and linear activation functions in the ANN, the software shows an effectiveness greater than 80%, concluding that it can be an effective tool as an aid in the diagnosis of cancer of skin.
New Approach of MA Detection & Grading Using Different ClassifiersIJSTA
This document discusses approaches for detecting microaneurysms (MAs), the earliest sign of diabetic retinopathy, in retinal images using machine learning classifiers. It first provides background on diabetic retinopathy and the importance of early MA detection. It then describes several existing methods for MA detection, including morphological approaches, fractal analysis, and using hue-saturation-value color models with eccentricity. The document goes on to propose a new ensemble-based approach combining different pre-processing techniques, candidate extraction methods, and classifiers to improve MA detection performance.
Blood vessel segmentation in fundus imagesIRJET Journal
This document summarizes a research paper on blood vessel segmentation in fundus images using random forest classification. The proposed method uses six steps: 1) spatial calibration of fundus images, 2) image preprocessing including illumination equalization, denoising and contrast equalization, 3) optic disc removal, 4) candidate lesion extraction, 5) dynamic shape feature extraction, and 6) random forest classification of lesions. The method was tested on public Diaretdb1 fundus image database and achieved accurate segmentation and detection of microaneurysms and hemorrhages for early diagnosis of diabetic retinopathy.
Image Segmentation and Identification of Brain Tumor using FFT Techniques of ...IDES Editor
The image processing tools are extensively used on
the development of new algorithms and mathematical tools
for the advanced processing of medical and biological images.
Given an MRI scan, first segment the tumor region in the
MRI brain image and study the pixel intensity values. A
detailed procedure using Matlab script is written to extract
tumor region in CT scan Brain Image and MRI Scan Brain
Image. MRI Scan has higher resolution and easier
identification compare to CT scan Brain image. Fast Fourier
Transform is used here to study the tumor region of MRI
Brain Image in terms of its pixel intensity. Types of FFT like
Zero padded FFT, Windowed FFT are used to study the signal
converted from the MRI Brain Image. It is found that lesser
spectral leakage for Zero Padded Windowed FFT than other
Types of FFT and hence the tumor cell identification is easier
than other methods. Finally higher pixel intensity values of
the cells gives identification of presence and activeness of
tumor cells.
Artificial neural network based cancer cell classificationAlexander Decker
This document summarizes an artificial neural network (ANN) based system called ANN-C3 for cancer cell classification using medical images. The system performs image pre-processing, segmentation using Harris corner detection and region growing, feature extraction of Tamura texture features, and classification using a neural network ensemble. Segmentation detects threshold points using Harris corner detection and performs region growing from these seed points. Feature extraction converts the image data into numerical form using Tamura texture features that capture variations in illumination and surfaces that human vision and surgeons use to differentiate cancerous and non-cancerous cells. The neural network is trained on a large set of labeled data to accurately classify cells.
IRJET - An Efficient Approach for Multi-Modal Brain Tumor Classification usin...IRJET Journal
This paper proposes an efficient approach for multi-modal brain tumor classification using texture features and machine learning. It uses the MICCAI BraTS 2016 dataset and segments tumors using fuzzy c-means clustering. It then extracts texture features like GLCM and LBP and classifies tumors as benign or malignant using an SVM classifier. The proposed method achieved accurate segmentation and classification of brain tumors in MRI images.
The document outlines the agenda and presentations for a Wireless Building Automation demonstration at Vooruit. The agenda includes general presentations on WBA and demonstrations of managing video surveillance over a wireless mesh network, intelligent video transmission and control, SANET use cases, indoor positioning, and SANET network solutions. The document also provides details on WBA, including its architecture, applications to building management systems, and research partners.
Presentatie mfconnect Museum Boerhaave en TwitterVera Bartels
Deze presentatie heb ik gegeven tijdens de conferentie Connect van MuseumFuture op 12 november 2010 in Zeist. De presentatie geeft een korte introductie op het twittergebruik van @museumboerhaave en een workshop gedeelte
Este documento presenta la información de dos plataformas e-learning, Edmodo y Chamilo, que serán utilizadas por un profesor y estudiante para el aprendizaje en línea. Se incluyen los perfiles creados, las URLs de acceso a cada plataforma, y los detalles de contacto del estudiante.
Break out: Project Communication and Dissemination - Koen De Vosimec.archive
This document discusses best practices for disseminating project information to target audiences. It advises selecting the right dissemination tools and channels based on the audience, such as using social media, websites, newsletters, and publishing tools. A case study of the Apollon project shows what dissemination methods worked well, such as an open living labs website and newsletter, and what did not, like LinkedIn and Twitter. The document stresses publishing results at the right time, to the right audience, using existing channels, with simple explanations and an enticing manner.
Break out: Incubation and Venturing - Felix Van Maeleimec.archive
The document discusses entrepreneurship and provides details about the speaker's background as the co-founder and CEO of Collibra, a data governance software company. It outlines Collibra's growth over time from preparing with an idea and seed funding to currently proving themselves with international customers and market recognition. The document also offers tips for international sales, noting the importance of finding sales channels to target markets beyond one's home country. It closes with a brief discussion of open source business models.
1) The document discusses the need for a new way of perceiving, being, and doing in the world as the old ways no longer work due to current crises.
2) It proposes moving from an industrial-market era focused on GDP to a socio-ecological era with new metrics like the ISEW that consider social and environmental welfare over time.
3) Social innovation is presented as a process where new ideas come from those directly involved in problems to find solutions through collaboration rather than top-down approaches. This distributes complexity and innovation more widely.
Vânia goncalves isbo ng wi nets - accounting interferenceimec.archive
This document discusses the impact of interference on revenue from spectrum sharing. It first provides background on spectrum allocation and sensing. It then describes models for estimating how allowing secondary usage impacts primary users and owners' revenue. Case studies examine interference between technologies like 802.15.4 and Wi-Fi, UMTS and UWB, and WiMAX and fixed wireless access. The document concludes that interference effects depend on the technologies, applications, and contexts involved.
The document describes the structure and facilities of the IBBT iLab, which includes research groups, a technical test center for evaluating platforms and networking equipment, an open innovation center for multi-stakeholder collaboration on policy issues, and a usability lab for testing products and services. The technical test center is equipped to evaluate various networking technologies and conduct performance testing and analysis. The usability lab allows for usability testing in controlled laboratory environments or portable and mobile setups in natural contexts.
El documento habla sobre el tema de la drogadicción. Explica sus causas como problemas familiares e influencia social, sus consecuencias como depresión y enfermedades, y posibles soluciones como aceptar el problema y buscar tratamiento de desintoxicación y apoyo familiar. Concluye que la drogadicción afecta negativamente a la persona y la sociedad.
This document discusses cyber exclusion and the digital divide in South-East European countries. It notes methodological challenges in measuring key indicators due to non-standardized definitions and limited data. It examines different types of cyber exclusion, both external like access to the internet and internal like differences by age, gender, and residence. Several charts show differences in internet users, hosts, age and gender distributions between countries in the region and Europe. The document argues that income, infrastructure, and literacy are important determinants of cyber exclusion and that access alone is not enough - usage and available content are also important to reach social inclusion.
This document discusses the iterative process of co-creating an ontology with stakeholders. Researchers conducted contextual inquiries through documentation analysis, observations, and interviews across multiple healthcare sites. Scenarios were developed and used in workshops with various stakeholders including medical professionals, engineers, and social scientists. The workshops introduced ontologies and involved role playing, decision making, and concept evaluation. A proof of concept was developed using a personal electronic device to demonstrate the ontology. The document reflects on further refining the process and developing the research.
This document provides recommendations for using MyBBT, a collaboration tool, based on practical experiences. It outlines best practices for proposal writing, project startups, mailing lists, and directories. It also discusses challenges with user skills and engagement, especially among industry partners. Formal reporting and follow up procedures need clarification around details like project versus calendar year, approvals processes, and what gets transferred to official reporting. Users should not overestimate others' ICT skills and need to respect partners' tools and policies around meetings, bandwidth limits, and security.
The document describes the Scavenger2000, an environmental restoration vessel developed through a collaboration between Picture SAI Water Technologies India and Water Management Technologies USA. The Scavenger2000 is designed to treat water bodies by removing debris, disinfecting water to kill bacteria and viruses, and increasing oxygen levels. It can handle large volumes of water treatment and is intended to provide an effective yet affordable solution for water quality issues.
Break out: Project Communication and Dissemination - Karen Boersimec.archive
The document discusses communication and dissemination strategies for research projects. It provides testimonials from researchers about communicating their work. Key points are made about creating economic, societal, and scientific value through communication. Guidance is given on determining the main message, target audience, appropriate format, and making the communication appealing. The document outlines communication services and templates available through IBBT, such as website pages, flyers, videos, and events. It encourages researchers to contact IBBT for assistance with project communication.
I Minds2009 Health Decision Support Prof Bart De Moor (Ibbt Esat Ku Leuven)imec.archive
This document discusses trends and opportunities in health decision support systems. It notes the exponential growth of data from technologies like genomics and imaging. This data tsunami creates opportunities for advanced decision support through integration of heterogeneous data sources. Multimodal imaging data and gene prioritization are examples given. The document also discusses building clinical decision support systems, policy decision support, and embedded decision support systems. It outlines several areas for further research and development like information security, population data mining, home health monitoring, and advanced signal processing.
This short document appears to be notes about a family trip mentioning Mrs. and Mr. Arvind near Mussorie, a stop at the Golden Temple, and notes about the author's father, daughter, sister, and brother-in-law.
Quantitative Image Analysis for Cancer Diagnosis and Radiation TherapyWookjin Choi
1.Lung Cancer Screening
1.1.Deep learning (feasible but not interpretable)
1.2.Radiomics (concise model)
1.3.Spiculation quantification (interpretable feature)
2.PET/CT Tumor Response
2.1.Aggressive Lung ADC subtype prediction (helpful for surgeons)
2.2.Pathologic response prediction (accurate but not concise)
2.3.Local tumor morphological changes (accurate and interpretable)
Normal & abnormal radiology of brain part iiMohammed Fathy
This document provides an overview of radiology techniques used to image the central nervous system (CNS), with a focus on brain tumors. It discusses the roles of various imaging modalities like CT, MRI, X-rays and their sequences. CT provides density-based images and is useful for detecting calcification. MRI has better soft tissue contrast and avoids radiation. Key MRI sequences like T1WI, T2WI, FLAIR, DWI, PWI, and post-contrast T1WI are explained. These sequences help characterize lesions and determine tumor type and grade. Perfusion imaging can indicate malignancy by showing blood flow and volume within a tumor. Together modern imaging allows diagnosis, staging, treatment planning and monitoring
The document provides an introduction to medical imaging modalities, with a focus on computed tomography (CT). It discusses the history and evolution of CT, from first generation scanners with one source and detector requiring 25 minutes per scan, to current multi-slice scanners with up to 2000 detectors allowing scans in just a few seconds. Scatter correction and iterative reconstruction techniques are also covered, which help reduce dose and improve image quality compared to traditional filtered back projection. The principles of the Radon transform and how it is used to reconstruct CT images from projections are briefly explained. Overall, the document gives a high-level overview of CT technology and image reconstruction methods.
This work was presented at the first Annual IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems (BioWireleSS) held as part of the IEEE Radio and Wireless Symposium 2011, in Phoenix, AZ.
This document discusses computer aided detection (CAD) of abnormalities in medical images. It begins by outlining CAD and some of the key machine learning challenges, including correlated training data, non-standard evaluation metrics, runtime constraints, lack of objective ground truths, and data shortages. It then describes solutions like multiple instance learning, batch classification, cascaded classifiers, crowdsourcing algorithms, and multi-task learning. The document concludes by reviewing the clinical impact of CAD systems through several independent studies, which demonstrated improved radiologist performance and sensitivity in detecting diseases.
Bridging the STEM gender gap through cultural inclusion and educational opportunity, this opportunity was granted to a selected set of women from UB to showcase their research.
Interpretable Spiculation Quantification for Lung Cancer ScreeningWookjin Choi
Spiculations are spikes on the surface of pulmonary nodule and are important predictors of malignancy in lung cancer. In this work, we introduced an interpretable, parameter-free technique for quantifying this critical feature using the area distortion metric from the spherical conformal (angle-preserving) parameterization. The conformal factor in the spherical mapping formulation provides a direct measure of spiculation which can be used to detect spikes and compute spike heights for geometrically-complex spiculations. The use of the area distortion metric from conformal mapping has never been exploited before in this context. Based on the area distortion metric and the spiculation height, we introduced a novel spiculation score. A combination of our spiculation measures was found to be highly correlated (Spearman's rank correlation coefficient ρ = 0.48) with the radiologist's spiculation score. These measures were also used in the radiomics framework to achieve state-of-the-art malignancy prediction accuracy of 88.9% on a publicly available dataset.
This document provides an introduction to a lecture series on diagnostic imaging research. It discusses the history and basic principles of various medical imaging modalities, including x-rays, computed tomography (CT), nuclear medicine, and ultrasound. The course objectives are to learn fundamentals of multidimensional signal processing and physics underlying modalities like x-rays, CT, MRI, PET, and ultrasound. Prerequisites include courses in systems theory and statistics, while related courses on microscopy and MRI are also mentioned.
This document provides an introduction to a lecture series on diagnostic imaging research. It discusses the history and basic principles of various medical imaging modalities, including x-rays, computed tomography (CT), nuclear medicine, and ultrasound. The course objectives are to learn fundamentals of multidimensional signal processing and physics underlying modalities like x-rays, CT, MRI, PET, and ultrasound. Prerequisites include courses in systems theory and statistics, while related courses on microscopy and MRI are also mentioned.
MediaEval 2017 - Medical Multimedia Task: Multimedia for Medicine: The Medico...multimediaeval
Presenter: Konstantin Pogorelov, Simula Research Laboratory, University of Oslo, Norway
Paper: http://ceur-ws.org/Vol-1984/Mediaeval_2017_paper_3.pdf
Video: https://youtu.be/V2vFNXKSFrM
Authors: Michael Riegler, Konstantin Pogorelov, Pål Halvorsen, Carsten Griwodz, Thomas de Lange, Kristin Ranheim Randel, Sigrun Losada Eskeland, Duc-Tien Dang-Nguyen, Mathias Lux, Concetto Spampinato
Abstract: The Multimedia for Medicine Medico Task, running for the first time as part of MediaEval 2017, focuses on detecting abnormalities, diseases and anatomical landmarks in images captured by medical devices in the gastrointestinal tract. The task characteristics are described, including the use case and its challenges, the dataset with ground truth, the required participant runs and the evaluation metrics.
I reviewed 3 papers at 'SNU TF Study Group' in Korea.
3 papers tried to solve segmentation problems in medical images with Deep Learning.
Deep Learning 을 이용하여 의료 영상에서 Segmentation 문제를 풀고자 한 3가지 논문을 리뷰하였습니다. :)
Computed tomography (CT) uses x-rays to produce cross-sectional images of the body. CT scanners rotate around the patient to obtain multiple x-ray images from different angles, which are reconstructed using filtered back projection or iterative reconstruction techniques to produce detailed images. CT provides accurate diagnostic information for conditions like cancer and heart disease. While CT has superior accuracy compared to other imaging methods, the increased radiation exposure raises cancer risk, so techniques aim to reduce dose while preserving image quality.
This document discusses the principles and utility of 3D conformal radiation therapy (3DCRT). It begins by explaining the goals of radiotherapy to maximize dose to the tumor while minimizing dose to normal tissues. It then describes some disadvantages of conventional 2D planning, including lack of 3D visualization and irradiation of large normal tissue volumes. The document goes on to define 3DCRT as radiotherapy that closely conforms the high dose volume to the target while sparing critical tissues. It discusses the history and development of 3DCRT and provides details on target volume definition, treatment planning workflow including imaging, contouring, planning and evaluation.
The document contains questions and answers about various topics related to CT scans. It includes definitions and explanations of ring artifacts, HRCT techniques, image reconstruction methods, CT numbers, scintillation detectors, pixels, radiation profile width in CT collimators, CT number, resolution types, mass attenuation coefficient, parallel multi-hole collimators, low dose CT scans, CT guided biopsies, and CT artifacts. The document consists of questions from several students on technical aspects of computed tomography imaging.
A summary of recent innovations in radiation oncology focussing on the priniciples of different techniques and their application. An overview of clinical results has also been given
Beam directed radiotherapy aims to deliver a homogenous tumor dose while minimizing radiation to normal tissues. It involves careful patient positioning, immobilization, tumor localization, field selection, dose calculations, and verification. Key steps include using positioning aids and molds to reproducibly position the patient, imaging such as CT to delineate the tumor volume, contouring to define external body outlines, and dose calculations and verification to ensure accurate delivery.
11.texture feature based analysis of segmenting soft tissues from brain ct im...Alexander Decker
This document describes a study that used texture feature analysis and a bidirectional associative memory (BAM) type artificial neural network to segment normal and tumor tissues from brain CT images. Gray level co-occurrence matrix features were extracted from 80 CT images of normal, benign and malignant tumors. The most discriminative features were selected using t-tests and used to train the BAM network classifier to segment tissues in the images. The proposed method provided accurate segmentation of normal and tumor regions, especially small tumors, in an efficient and fast manner with less computational time compared to other methods.
Cancerous lung nodule detection in computed tomography imagesTELKOMNIKA JOURNAL
Diagnosis the computed tomography images (CT-images) is one of the images that may take a lot of time in diagnosis by the radiologist and may miss some of cancerous nodules in these images. Therefore, in this paper a new novel enhancement and detection cancerous nodule algorithm is proposed to diagnose a CT-images. The novel algorithm is divided into three main stages. In first stage, suspicious regions are enhanced using modified LoG algorithm. Then in stage two, a potential cancerous nodule was detected based on visual appearance in lung. Finally, five texture features analysis algorithm is implemented to reduce number of detected FP regions. This algorithm is evaluated using 60 cases (normal and cancerous cases), and it shows a high sensitivity in detecting the cancerous lung nodules with TP ration 97% and with FP ratio 25 cluster/image.
Microcalcification Enhancement in Digital MammogramNashid Alam
The document discusses early detection of breast cancer through computer-aided detection of microcalcifications in digital mammograms. It describes microcalcifications and how mammography is used to detect them as early signs of cancer. The problem is the difficulty for radiologists to accurately detect microcalcifications. The goal is to develop a computer model to better detect microcalcification clusters and determine cancer likelihood from mammogram images.
A novel CAD system to automatically detect cancerous lung nodules using wav...IJECEIAES
A novel cancerous nodules detection algorithm for computed tomography images (CT-images) is presented in this paper. CT-images are large size images with high resolution. In some cases, number of cancerous lung nodule lesions may missed by the radiologist due to fatigue. A CAD system that is proposed in this paper can help the radiologist in detecting cancerous nodules in CT- images. The proposed algorithm is divided to four stages. In the first stage, an enhancement algorithm is implement to highlight the suspicious regions. Then in the second stage, the region of interest will be detected. The adaptive SVM and wavelet transform techniques are used to reduce the detected false positive regions. This algorithm is evaluated using 60 cases (normal and cancerous cases), and it shows a high sensitivity in detecting the cancerous lung nodules with TP ration 94.5% and with FP ratio 7 cluster/image.
The document discusses a living lab for small and medium enterprises (SMEs) to involve users in the product development process from an early stage. Some key benefits mentioned include detecting unintended problems or opportunities through active user involvement, conducting multi-method research to enrich products academically, and allowing technologies to be domesticated by users rather than just consumed. The living lab offers services to SMEs to help fast track the process from ideation to demonstration through co-creation with various user types and extra funding and support opportunities.
PRoF is a living lab that builds very life-like environments using state-of-the-art products to enable early testing and concept validation. It provides an ecosystem for innovation and business across companies, academia, users, and care actors. PRoF has a long history of collaboration and has had a big impact on innovation in healthcare.
Results of the Apollon pilot in homecare and independent livingimec.archive
The document summarizes the results of the Apollon pilot project evaluating the use of living lab networks for testing homecare and independent living services across borders. The pilot involved transferring three such services between four living labs in different countries. A key finding was that a common cross-border ecosystem model for living labs in healthcare was not feasible due to differences between countries in areas like value networks, organization of healthcare, regulations, and infrastructure. However, living labs could still effectively serve as brokers and matchmakers to enable cross-border collaboration by addressing issues around stakeholders, access to users, liability, ethics, rules, and safety. Based on this pilot, the document advocates for a domain-specific network of smart care living labs to facilitate knowledge
Delivery of feedback on Health, Home Security and Home Energy in Aware Homes ...imec.archive
This document discusses the CASALA Living Lab, which conducts research on delivering feedback to users about their health, home security, and energy usage using sensors in ambient assisted living homes. The CASALA Living Lab has multiple stages, including virtual environments, a facility called Great Northern Haven with over 2,000 sensors collecting data from 16 apartments, and community deployments. The lab aims to understand user behavior from real-world data and provide feedback to empower users. Challenges include lack of market awareness for ambient assisted living and siloed funding, while successes involve end-user involvement and driving education and adoption of these technologies.
The document describes the Emmanuel Haven Living Lab located in Motherwell, South Africa. The Living Lab was established to provide prevention, treatment, care and support to communities impacted by HIV/AIDS, tuberculosis, and diabetes. It aims to mitigate the health, psychological and socio-economic effects of these diseases through the use of information and communication technologies (ICT) and community programs. Some of its initiatives include using mobile technologies to enable home-based care, nutritional education, and skills development for disabled community members. The Living Lab faces challenges such as lack of infrastructure, connectivity and access issues, as well as social challenges like poverty and low literacy levels in the community.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Health-Lab Amsterdam is a living lab platform focused on testing and improving ICT and healthcare solutions together with users. It has three dimensions: 1) a platform where people can meet and discuss new care solutions, 2) living labs where solutions can be tested with users, and 3) new educational programs focused on implementing solutions. The living lab has apartments equipped with sensors to study user needs, concepts, and acceptance of new solutions. Students from various fields participate in minors to learn about digital health and intelligent environments.
The European Network of Living Labs (ENoLL) is a non-profit international association representing over 300 certified Living Labs across Europe. Living Labs are real-life test environments where users and producers co-create innovations. ENoLL was launched in 2006 and supports various EU initiatives related to aging well, smart cities, and future internet technologies by facilitating partnerships between its member Living Labs. ENoLL is committed to the EU Active and Assisted Living Program and plans workshops and projects to promote interoperability and gather evidence on independent living solutions.
This document summarizes the process and outcomes of the 6th Wave of the European Network of Living Labs (ENoLL). It describes how 72 proposals were submitted and evaluated by 6 teams against 20 criteria on a scale of 0-5. 46 Living Labs were ultimately selected, including 31 from EU countries and 15 non-EU members. The document provides details on the evaluation phases and typical weaknesses seen in applications. It concludes by welcoming the new members and thanking those involved in the evaluation process.
The Connected Smart Cities Network and Living Labs - Towards Horizon 2020 - K...imec.archive
The document discusses how EU Cohesion Policy supports innovation, particularly through the European Regional Development Fund (ERDF). It provides an overview of how over €86 billion was spent on research and innovation during 2007-2013 to build research capacity and infrastructure in all regions. For 2014-2020, there will be a thematic focus on research and innovation, ICT, and SME competitiveness to maximize impact. Regions will develop research and innovation strategies for smart specialisation to concentrate resources on competitive advantages. Synergies between Cohesion Policy and Horizon 2020 are aimed at supporting research and innovation from the idea stage to market.
Apollon-23/05/2012-9u30- Parallell session: Living Labs added value imec.archive
1) Living labs provide meeting places for research, development, and innovation where companies, researchers, specialists, teachers, students, and product users collaborate.
2) Demola is an innovation platform that combines student ideas with needs and support from project partners and customers, turning ideas into product and service demos.
3) Benefits of Demola include real market potential for projects, valuable experience for students, opportunity for students to start their own businesses, and license agreements or partnerships between students and project partners.
Apollon - 22/5/12 - 11:30 - Local SME's - Innovating Across bordersimec.archive
This document outlines a methodology for setting up and operating cross-border networks of living labs to support small and medium enterprises (SMEs) with innovation. It describes a multi-phase process including connecting partners, planning projects, supporting experimentation, and evaluating results. A variety of methods and tools were developed and validated through pilot projects in different domains like healthcare, energy efficiency, and manufacturing. These methods and tools are accessible through an online knowledge center to facilitate cross-border collaboration between living labs.
Apollon - 22/5/12 - 16:00 - Smart Open Cities and the Future Internetimec.archive
The document discusses Lisbon's efforts to become a smarter city through open innovation and citizen participation. It outlines challenges like economic issues but also opportunities from new technologies. Lisbon is promoting spaces and tools for public involvement, including participatory budgeting, living labs, open data, and co-working areas. It also supports entrepreneurship through initiatives like Lx Startup, Fab Lab, and Lx Academy. The city is investing in sustainable mobility and renewable energy programs. Overall, the goal is to engage citizens in developing solutions and make Lisbon a center for creativity, business, and green technology.
Apollon - 22/5/12 - 16:00 - Smart Open Cities and the Future Internetimec.archive
The document summarizes a presentation on smart cities as innovation ecosystems sustained by the future internet. Some key points:
1) Smart cities are not yet a reality, but rather an urban development strategy and vision focused on empowering citizens and creating an "urban innovation ecology."
2) The FIREBALL project aims to bring together cities, living labs, and future internet stakeholders to explore how open innovation and user participation can support experimentation and adoption of future internet technologies.
3) Case studies of smarter cities show examples of technology districts, living lab initiatives, infrastructure development, and efforts to engage citizens. However, challenges remain around skills gaps, funding, and measuring impact.
Apollon - 22/5/12 - 16:00 - Smart Open Cities and the Future Internetimec.archive
The document describes an open data app challenge organized by Open Cities. It invites developers to create apps using European open data sources that solve citizen issues. The challenge runs from February to November 2012, with a submission period in August-September and finals at the Smart City Expo in November. Top prizes include €5,000 for first place. The goal is to promote open data apps and make city living easier through collaboration across Europe.
Apollon - 22/5/12 - 16:00 - Smart Open Cities and the Future Internetimec.archive
The document describes open data platforms and sensor network platforms created by the Open Cities project. It discusses how the platforms provide open data and sensor data from multiple cities through common interfaces and tools. This allows developers to more easily access and build applications using the urban data. The platforms have seen increasing use, with thousands of data sets accessed from cities across Europe. Support is provided to developers through tutorials, code samples and documentation to help them create innovative apps using the open data.
Apollon - 22/5/12 - 11:30 - Local SME's - Innovating Across bordersimec.archive
This document discusses the transition of a large living lab called i-City in Flanders into a spin-off MVNO business. It summarizes that i-City started as a wireless city project with over 500 hotspots and 2000 test users. Some of the alfa community members who received support went on to work for the founding companies. The spin-off took the community-focused approach of i-City and applies it to their MVNO business, which has grown to over 120,000 users through testing with focus groups and an open API. The plans are to expand the business model to other European countries using the same approach of building, testing, and rebuilding with community input.
Apollon - 22/5/12 - 09:00 - User-driven Open Innovation Ecosystemsimec.archive
The document discusses the European Network of Living Labs (ENoLL), which connects over 320 Living Labs across Europe and globally. Living Labs are open innovation ecosystems that engage stakeholders to address societal challenges through user-driven collaboration. ENoLL supports its members through events, projects and services. It also works to expand globally through partnerships and regional networks. The Connected Smart Cities Network was launched to facilitate collaboration between cities on developing smart city solutions using Living Labs approaches.
Apollon - 22/5/12 - 09:00 - User-driven Open Innovation Ecosystemsimec.archive
1) The FIREBALL project coordinates and aligns approaches between future internet research, experimentation testbeds, and user-driven open innovation to promote innovation in smart cities.
2) Smart cities require three components: cities/communities to define challenges, living labs as generators of solutions developed with citizen involvement, and internet technologies as facilitators of communication and information processing.
3) Key FIREBALL activities include developing a smart city vision and cases, building smart city innovation ecosystems and networks, and coordinating medium to long term future internet research with short to medium term applied research and large scale experimentation.
Apollon - 22/5/12 - 09:00 - User-driven Open Innovation Ecosystemsimec.archive
This document summarizes a keynote about user-driven open innovation ecosystems across borders, and the Future Internet Public-Private Partnership (FI PPP) program. The FI PPP aims to make applications research drive technology development, make Europe a leader in future internet technologies, and accelerate sustainable innovation. It involves three phases: technology development, networked pilots and trials across Europe, and expansion of testbeds and pilots. The program is implemented through a series of calls for proposals totaling over 300 million Euros. It represents an effort to reinvent how the European Commission approaches internet-related research and innovation.
Apollon - 22/5/12 - 09:00 - User-driven Open Innovation Ecosystems
20080125 Friday Food
1. Quantitative analysis of ultrasound images of the preterm brain Ewout Vansteenkiste IBBT-Medisip/IPI-UGENT Friday Food 25/01/2008
2. Outline [Source: William Lawson, A new Orchard and Garden , 1648, Londen ] quantitative image analysis medical ultrasound speckle-reduction in ultrasound 2D echo/3D MRI registration white matter classification 1/24 texture-classification psycho-physics segmentation registration white matter segmentation ventricle segmentation segmentation carotid
3. Quantitative image analysis 2/24 tumor = “white dot” in the image size = “small”, “average” Qualitative analysis : in words 2.25 cm² Both experts measure the tumor Using the same segmentation algorithm Quantitative analysis: through measuring
4. Medical ultrasound 3/24 SPECKLE probe electric current Pi ëzo-electric cristal pulsing Tissue structures/transitions skin
12. Outline [Source: William Lawson, A new Orchard and Garden , 1648, Londen ] Quantitative image analysis medical ultrasound Texture classification white matter classification 11/24
13. Flare segmentation and area estimation 12/24 sensitivity 98% Validation? Initial texture- Basd segmen- Tation map -Morfological closing -Gradient -Opening by Reconstruction expert existing new Expert delineation: subjective? 2D US 3D MRI registration
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15. Multimodal 2D ultrasound to 3D MRI registration initialization Mutuel Information Metric Regular Step Gradient Descent Rigid Transformation Trilinear Interpolation result 14/24