5,002 research outputs found
Deep Learning Models for Classification of COVID-19 Cases by Medical Images
In recent times, the use of chest Computed Tomography (CT) images for
detecting coronavirus infections has gained significant attention, owing to
their ability to reveal bilateral changes in affected individuals. However,
classifying patients from medical images presents a formidable challenge,
particularly in identifying such bilateral changes. To tackle this challenge,
our study harnesses the power of deep learning models for the precise
classification of infected patients. Our research involves a comparative
analysis of deep transfer learning-based classification models, including
DenseNet201, GoogleNet, and AlexNet, against carefully chosen supervised
learning models. Additionally, our work encompasses Covid-19 classification,
which involves the identification and differentiation of medical images, such
as X-rays and electrocardiograms, that exhibit telltale signs of Covid-19
infection. This comprehensive approach ensures that our models can handle a
wide range of medical image types and effectively identify characteristic
patterns indicative of Covid-19. By conducting meticulous research and
employing advanced deep learning techniques, we have made significant strides
in enhancing the accuracy and speed of Covid-19 diagnosis. Our results
demonstrate the effectiveness of these models and their potential to make
substantial contributions to the global effort to combat COVID-19.Comment: Master's thesi
Empowering COVID-19 Detection: Optimizing Performance Through Fine-Tuned EfficientNet Deep Learning Architecture
The worldwide COVID-19 pandemic has profoundly influenced the health and
everyday experiences of individuals across the planet. It is a highly
contagious respiratory disease requiring early and accurate detection to curb
its rapid transmission. Initial testing methods primarily revolved around
identifying the genetic composition of the coronavirus, exhibiting a relatively
low detection rate and requiring a time-intensive procedure. To address this
challenge, experts have suggested using radiological imagery, particularly
chest X-rays, as a valuable approach within the diagnostic protocol. This study
investigates the potential of leveraging radiographic imaging (X-rays) with
deep learning algorithms to swiftly and precisely identify COVID-19 patients.
The proposed approach elevates the detection accuracy by fine-tuning with
appropriate layers on various established transfer learning models. The
experimentation was conducted on a COVID-19 X-ray dataset containing 2000
images. The accuracy rates achieved were impressive of 100% for EfficientNetB4
model. The fine-tuned EfficientNetB4 achieved an excellent accuracy score,
showcasing its potential as a robust COVID-19 detection model. Furthermore,
EfficientNetB4 excelled in identifying Lung disease using Chest X-ray dataset
containing 4,350 Images, achieving remarkable performance with an accuracy of
99.17%, precision of 99.13%, recall of 99.16%, and f1-score of 99.14%. These
results highlight the promise of fine-tuned transfer learning for efficient
lung detection through medical imaging, especially with X-ray images. This
research offers radiologists an effective means of aiding rapid and precise
COVID-19 diagnosis and contributes valuable assistance for healthcare
professionals in accurately identifying affected patients.Comment: Computers in Biology and Medicine [Q1, IF: 7.7, CS: 9.2
Enhancing COVID-19 Diagnosis: A Multi-Modal Approach Utilizing the CNN Algorithm in Automated Applications
Rapidly identifying COVID-19 patients is essential for effective disease control and management. To address this need, we have developed an automated application that utilizes multi-modal data, including Chest X-ray, Electrocardiogram (ECG), and CT scan images, for precise COVID-19 patient identification. This application comprises a two-stage process, starting with a web-based questionnaire and then the submission of medical images for verification. Leveraging various ML and DL techniques, including CNN, KNN, Logistic Regression, Decision Tree, and NaiveBayes, We conducted extensive model training and validation for LSTM, InceptionV3, SVM, Resnet, and MobileNet. The CNN algorithm emerged as the top-performing method across all modalities, demonstrating exceptional accuracy, precision, recall, F-score, and a minimal false prediction rate. Confusion matrices were employed for comprehensive result evaluation. This study highlights the potential of multi-modal data analysis, particularly the CNN algorithm, for efficiently and accurately identifying COVID-19 patients
Automated COVID-19 CT Image Classification using Multi-head Channel Attention in Deep CNN
The rapid spread of COVID-19 has necessitated efficient and accurate
diagnostic methods. Computed Tomography (CT) scan images have emerged as a
valuable tool for detecting the disease. In this article, we present a novel
deep learning approach for automated COVID-19 CT scan classification where a
modified Xception model is proposed which incorporates a newly designed channel
attention mechanism and weighted global average pooling to enhance feature
extraction thereby improving classification accuracy. The channel attention
module selectively focuses on informative regions within each channel, enabling
the model to learn discriminative features for COVID-19 detection. Experiments
on a widely used COVID-19 CT scan dataset demonstrate a very good accuracy of
96.99% and show its superiority to other state-of-the-art techniques. This
research can contribute to the ongoing efforts in using artificial intelligence
to combat current and future pandemics and can offer promising and timely
solutions for efficient medical image analysis tasks
Comparative Analysis of MFO, GWO and GSO for Classification of Covid-19 Chest X-Ray Images
تلعب الصور الطبية دورًا حاسمًا في تصنيف الأمراض والحالات المختلفة. إحدى طرق التصوير هي الأشعة السينية التي توفر معلومات بصرية قيمة تساعد في تحديد وتوصيف مختلف الحالات الطبية. لطالما استخدمت الصور الشعاعية للصدر (CXR) لفحص ومراقبة العديد من اضطرابات الرئة، مثل السل والالتهاب الرئوي وانخماص الرئة والفتق. يمكن الكشف عن COVID-19 باستخدام صور CXR أيضًا. تم اكتشاف COVID-19، وهو فيروس يسبب التهابات في الرئتين والممرات الهوائية في الجهاز التنفسي العلوي، لأول مرة في عام 2019 في مقاطعة ووهان بالصين، ومنذ ذلك الحين يُعتقد أنه يتسبب في تلف كبير في مجرى الهواء، مما يؤثر بشدة على رئة الأشخاص المصابين. انتشر الفيروس بسرعة في جميع أنحاء العالم، وتم تسجيل الكثير من الوفيات والحالات المتزايدة بشكل يومي. يمكن استخدام CXR لمراقبة آثار COVID-19 على أنسجة الرئة. تبحث هذه الدراسة في تحليل مقارنة لأقرب جيران k (KNN)، و Extreme Gradient Boosting (XGboost)، و Support-Vector Machine (SVM)، وهي بعض مناهج التصنيف لاختيار الميزات في هذا المجال باستخدام خوارزمية Moth-Flame Optimization (MFO)، وخوارزمية Gray Wolf Optimizer (GWO)، وخوارزمية Glowworm Swarm Optimization (GSO). في هذه الدراسة، استخدم الباحثون مجموعة بيانات تتكون من مجموعتين على النحو التالي: 9544 صورة بالأشعة السينية ثنائية الأبعاد، والتي تم تصنيفها إلى مجموعتين باستخدام اختبارات التحقق من صحتها: 5500 صورة لرئتين سليمتين و4044 صورة للرئتين مع COVID-19. تتضمن المجموعة الثانية 800 صورة و400 صورة لرئتين سليمتين و400 رئة مصابة بـ COVID-19. تم تغيير حجم كل صورة إلى 200 × 200 بكسل. كانت الدقة والاستدعاء ودرجة F1 من بين معايير التقييم الكمي المستخدمة في هذه الدراسة.Medical images play a crucial role in the classification of various diseases and conditions. One of the imaging modalities is X-rays which provide valuable visual information that helps in the identification and characterization of various medical conditions. Chest radiograph (CXR) images have long been used to examine and monitor numerous lung disorders, such as tuberculosis, pneumonia, atelectasis, and hernia. COVID-19 detection can be accomplished using CXR images as well. COVID-19, a virus that causes infections in the lungs and the airways of the upper respiratory tract, was first discovered in 2019 in Wuhan Province, China, and has since been thought to cause substantial airway damage, badly impacting the lungs of affected persons. The virus was swiftly gone viral around the world and a lot of fatalities and cases growing were recorded on a daily basis. CXR can be used to monitor the effects of COVID-19 on lung tissue. This study examines a comparison analysis of k-nearest neighbors (KNN), Extreme Gradient Boosting (XGboost), and Support-Vector Machine (SVM) are some classification approaches for feature selection in this domain using The Moth-Flame Optimization algorithm (MFO), The Grey Wolf Optimizer algorithm (GWO), and The Glowworm Swarm Optimization algorithm (GSO). For this study, researchers employed a data set consisting of two sets as follows: 9,544 2D X-ray images, which were classified into two sets utilizing validated tests: 5,500 images of healthy lungs and 4,044 images of lungs with COVID-19. The second set includes 800 images, 400 of healthy lungs and 400 of lungs affected with COVID-19. Each image has been resized to 200x200 pixels. Precision, recall, and the F1-score were among the quantitative evaluation criteria used in this study
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