306 research outputs found
Detection of COVID-19 in X-Ray images using Neural Networks
Pandemie způsobena nemocí COVID-19 je velmi naléhavým problémem, který nadále ovlivňuje životy lidí po celém světě. K překonání této nemoci je nutné včas identifikovat a izolovat infikované pacienty, aby se zabránilo šíření viru. Tradiční detekční techniky založené na molekulární diagnostice, jako například RT-PCR, jsou nákladné, časově náročné a studie ukazují, že jejich spolehlivost značně kolísá. V této práci jsme zkoumali detekci nemoci COVID-19 v rentgenových snímcích hrudníku pomocí konvolučních neuronových sítí. Poznatky z provedené rešerše dále využíváme k implementaci prototypu pro provádění binární detekce a jeho následnému vyhodnocení na souboru otevřených datových repozitářů dostupných online. Tyto výsledky poté porovnáváme se stávajícími řešeními a modely. Naše navrhovaná jednoduchá architektura s názvem BaseNet dosahuje na zvolené testovací sadě dat přesnosti 95.50 % a senzitivity 93.00 %. Zmíněný BaseNet jsme dále spolu s několika dalšími vyladěnými architekturami spojili do souboru modelů, jejichž kombinovaná klasifikační přesnost je 99.50 % s naměřenou senzitivitou 98.50 %.The COVID-19 pandemic is a very pressing issue that continues to affect the lives of people around the globe. To combat and overcome the disease, it is necessary for infected patients to be quickly identified and isolated to prevent the virus from spreading. The traditional detection techniques based on molecular diagnosis, such as RT-PCR, are expensive, time-consuming, and their reliability has been shown to fluctuate. In this thesis, we research the detection of COVID-19 in chest X-ray images using convolutional neural networks. We use our findings to implement a prototype that performs binary detection of the disease, evaluate its performance on a collection of open data repositories available online, and compare its results to existing models. Our proposed light-weight architecture called the BaseNet achieves an accuracy of 95.50 % on the chosen test set, with a COVID-19 sensitivity of 93.00 %. We further assemble an ensemble of the BaseNet along with several other fine-tuned architectures, whose combined classification accuracy is 99.25 % with a measured sensitivity of 98.50 %
Automatic Endoscopic Ultrasound Station Recognition with Limited Data
Pancreatic cancer is a lethal form of cancer that significantly contributes
to cancer-related deaths worldwide. Early detection is essential to improve
patient prognosis and survival rates. Despite advances in medical imaging
techniques, pancreatic cancer remains a challenging disease to detect.
Endoscopic ultrasound (EUS) is the most effective diagnostic tool for detecting
pancreatic cancer. However, it requires expert interpretation of complex
ultrasound images to complete a reliable patient scan. To obtain complete
imaging of the pancreas, practitioners must learn to guide the endoscope into
multiple "EUS stations" (anatomical locations), which provide different views
of the pancreas. This is a difficult skill to learn, involving over 225
proctored procedures with the support of an experienced doctor. We build an
AI-assisted tool that utilizes deep learning techniques to identify these
stations of the stomach in real time during EUS procedures. This
computer-assisted diagnostic (CAD) will help train doctors more efficiently.
Historically, the challenge faced in developing such a tool has been the amount
of retrospective labeling required by trained clinicians. To solve this, we
developed an open-source user-friendly labeling web app that streamlines the
process of annotating stations during the EUS procedure with minimal effort
from the clinicians. Our research shows that employing only 43 procedures with
no hyperparameter fine-tuning obtained a balanced accuracy of 90%, comparable
to the current state of the art. In addition, we employ Grad-CAM, a
visualization technology that provides clinicians with interpretable and
explainable visualizations
Automated Pneumothorax Diagnosis using Deep Neural Networks
Thoracic ultrasound can provide information leading to rapid diagnosis of pneumothorax with improved accuracy over the standard physical examination and with higher sensitivity than anteroposterior chest radiography. However, the clinical We have Furthermore, remote environments, such as the battlefield or deep-space exploration, may lack expertise for diagnosing developed an automated image interpretation pipeline for the analysis of thoracic ultrasound data and the classification of pneumothorax events to provide decision support in such situations. Our pipeline consists of image preprocessing, data augmentation, and deep learning architectures for medical diagnosis. In this work, we demonstrate that robust, accurate interpretation of chest images and video can be achieved using deep neural networks. A number of novel image processing techniques were employed to achieve this result. Affine transformations were applied for data augmentation. Hyperparameters were optimized for learning rate, dropout regularization, batch size, and epoch iteration by a sequential model-based Bayesian approach. In addition, we utilized pretrained architecturesinterpretation of a patient medical image is highly operator dependent. certain pathologies., applying transfer learning and fine-tuning techniques to fully connected layers. Our pipeline yielded binary classification validation accuracies of 98.3% for M-mode images and 99.8% with B-mode video frames
CONVOLUTIONAL NEURAL NETWORK PADA KLASIFIKASI SIDIK JARI MENGGUNAKAN RESNET-50
Pengenalan sidik jari merupakan bagian dari teknologi biometrik. Klasifikasi sidik jari yang paling popular adalah Henry classification system. Henry membagi sidik jari berdasarkan garis polanya menjadi lima kelas yaitu arch (A), tented arch (T), left loop (L), right loop (R), dan whorl (W). Penelitian ini menggunakan Convolutional Neural Network (CNN) dengan model arsitektur Residual Network-50 (ResNet-50) untuk mengembangkan sistem klasifikasi sidik jari. Dataset yang digunakan diperoleh dari website National Institute of Standards and Technology (NIST) berupa citra sidik jari grayscale 8-bit. Hasil pengujian menunjukkan bahwa pemrosesan awal Contrast Limited Adaptive Histogram Equalization (CLAHE) dalam model CNN dapat meningkatkan performa akurasi dari sistem klasifikasi sidik jari sebesar 11,79%. Pada citra tanpa CLAHE diperoleh akurasi validasi 83,26%, sedangkan citra dengan CLAHE diperoleh akurasi validasi 95,05%
Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis
The purposes of this study were to investigate: 1) the effect of placement of
region-of-interest (ROI) for texture analysis of subchondral bone in knee
radiographs, and 2) the ability of several texture descriptors to distinguish
between the knees with and without radiographic osteoarthritis (OA). Bilateral
posterior-anterior knee radiographs were analyzed from the baseline of OAI and
MOST datasets. A fully automatic method to locate the most informative region
from subchondral bone using adaptive segmentation was developed. We used an
oversegmentation strategy for partitioning knee images into the compact regions
that follow natural texture boundaries. LBP, Fractal Dimension (FD), Haralick
features, Shannon entropy, and HOG methods were computed within the standard
ROI and within the proposed adaptive ROIs. Subsequently, we built logistic
regression models to identify and compare the performances of each texture
descriptor and each ROI placement method using 5-fold cross validation setting.
Importantly, we also investigated the generalizability of our approach by
training the models on OAI and testing them on MOST dataset.We used area under
the receiver operating characteristic (ROC) curve (AUC) and average precision
(AP) obtained from the precision-recall (PR) curve to compare the results. We
found that the adaptive ROI improves the classification performance (OA vs.
non-OA) over the commonly used standard ROI (up to 9% percent increase in AUC).
We also observed that, from all texture parameters, LBP yielded the best
performance in all settings with the best AUC of 0.840 [0.825, 0.852] and
associated AP of 0.804 [0.786, 0.820]. Compared to the current state-of-the-art
approaches, our results suggest that the proposed adaptive ROI approach in
texture analysis of subchondral bone can increase the diagnostic performance
for detecting the presence of radiographic OA
A Comparative Study of Existing and New Deep Learning Methods for Detecting Knee Injuries using the MRNet Dataset
This work presents a comparative study of existing and new techniques to
detect knee injuries by leveraging Stanford's MRNet Dataset. All approaches are
based on deep learning and we explore the comparative performances of transfer
learning and a deep residual network trained from scratch. We also exploit some
characteristics of Magnetic Resonance Imaging (MRI) data by, for example, using
a fixed number of slices or 2D images from each of the axial, coronal and
sagittal planes as well as combining the three planes into one multi-plane
network. Overall we achieved a performance of 93.4% AUC on the validation data
by using the more recent deep learning architectures and data augmentation
strategies. More flexible architectures are also proposed that might help with
the development and training of models that process MRIs. We found that
transfer learning and a carefully tuned data augmentation strategy were the
crucial factors in determining best performance
Multidimensional Contrast Limited Adaptive Histogram Equalization
Contrast enhancement is an important preprocessing technique for improving the performance of downstream tasks in image processing and computer vision. Among the existing approaches based on nonlinear histogram transformations, contrast limited adaptive histogram equalization (CLAHE) is a popular choice when dealing with 2D images obtained in natural and scientific settings. The recent hardware upgrade in data acquisition systems results in significant increase in data complexity, including their sizes and dimensions. Measurements of densely sampled data higher than three dimensions, usually composed of 3D data as a function of external parameters, are becoming commonplace in various applications in the natural sciences and engineering. The initial understanding of these complex multidimensional datasets often requires human intervention through visual examination, which may be hampered by the varying levels of contrast permeating through the dimensions. We show both qualitatively and quantitatively that using our multidimensional extension of CLAHE (MCLAHE) acting simultaneously on all dimensions of the datasets allows better visualization and discernment of multidimensional image features, as are demonstrated using cases from 4D photoemission spectroscopy and fluorescence microscopy. Our implementation of multidimensional CLAHE in Tensorflow is publicly accessible and supports parallelization with multiple CPUs and various other hardware accelerators, including GPUs
Towards a more accurate characterization of granular media 2.0: Involving AI in the process
publishedVersio
A survey, review, and future trends of skin lesion segmentation and classification
The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a user-friendly tool to dermatologists to reduce the challenges encountered or associated with manual inspection. This article aims to provide a comprehensive literature survey and review of a total of 594 publications (356 for skin lesion segmentation and 238 for skin lesion classification) published between 2011 and 2022. These articles are analyzed and summarized in a number of different ways to contribute vital information regarding the methods for the development of CAD systems. These ways include: relevant and essential definitions and theories, input data (dataset utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria. We intend to investigate a variety of performance-enhancing approaches, including ensemble and post-processing. We also discuss these dimensions to reveal their current trends based on utilization frequencies. In addition, we highlight the primary difficulties associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these difficulties. Findings, recommendations, and trends are disclosed to inform future research on developing an automated and robust CAD system for skin lesion analysis
Applying supervised contrastive learning for the detection of diabetic retinopathy and its severity levels from fundus images
Diabetic Retinopathy (DR) is a major complication in human eyes among the diabetic patients. Early detection of the DR can save many patients from permanent blindness. Various artificial intelligent based systems have been proposed and they outperform human analysis in accurate detection of the DR. In most of the traditional deep learning models, the cross-entropy is used as a common loss function in a single stage end-to-end training method. However, it has been recently identified that this loss function has some limitations such as poor margin leading to false results, sensitive to noisy data and hyperparameter variations. To overcome these issues, supervised contrastive learning (SCL) has been introduced. In this study, SCL method, a two-stage training method with supervised contrastive loss function was proposed for the first time to the best of authors' knowledge to identify the DR and its severity stages from fundus images (FIs) using “APTOS 2019 Blindness Detection” dataset. “Messidor-2” dataset was also used to conduct experiments for further validating the model's performance. Contrast Limited Adaptive Histogram Equalization (CLAHE) was applied for enhancing the image quality and the pre-trained Xception CNN model was deployed as the encoder with transfer learning. To interpret the SCL of the model, t-SNE method was used to visualize the embedding space (unit hyper sphere) composed of 128 D space into a 2 D space. The proposed model achieved a test accuracy of 98.36%, and AUC score of 98.50% to identify the DR (Binary classification) and a test accuracy of 84.364%, and AUC score of 93.819% for five stages grading with the APTOS 2019 dataset. Other evaluation metrics (precision, recall, F1-score) were also determined with APTOS 2019 as well as with Messidor-2 for analyzing the performance of the proposed model. It was also concluded that the proposed method achieved better performance in detecting the DR compared to the conventional CNN without SCL and other state-of-the-art methods
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