Medical Image Classification for Coronavirus Disease (COVID-19) Using Convolutional Neural Networks

Al-Zubaidi, Ehsan Ali; Mijwil, Maad M.

doi:10.24996/ijs.2021.62.8.27

Cited by 15 publications

(11 citation statements)

References 20 publications

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“…In 2021, the authors of [ 57 ] employed convolutional neural networks (ConvNets) to accurately identify COVID-19 in computed tomography (CT) images, enabling the early classification of chest CT images of COVID-19 by hospital staff. ConvNets automatically learned and extracted features from medical image datasets, including the COVID-CT dataset used in this study.…”

Section: Related Workmentioning

confidence: 99%

Robust Classification and Detection of Big Medical Data Using Advanced Parallel K-Means Clustering, YOLOv4, and Logistic Regression

Awad

Hamad

Alzubaidi

2023

Life

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Big-medical-data classification and image detection are crucial tasks in the field of healthcare, as they can assist with diagnosis, treatment planning, and disease monitoring. Logistic regression and YOLOv4 are popular algorithms that can be used for these tasks. However, these techniques have limitations and performance issue with big medical data. In this study, we presented a robust approach for big-medical-data classification and image detection using logistic regression and YOLOv4, respectively. To improve the performance of these algorithms, we proposed the use of advanced parallel k-means pre-processing, a clustering technique that identified patterns and structures in the data. Additionally, we leveraged the acceleration capabilities of a neural engine processor to further enhance the speed and efficiency of our approach. We evaluated our approach on several large medical datasets and showed that it could accurately classify large amounts of medical data and detect medical images. Our results demonstrated that the combination of advanced parallel k-means pre-processing, and the neural engine processor resulted in a significant improvement in the performance of logistic regression and YOLOv4, making them more reliable for use in medical applications. This new approach offers a promising solution for medical data classification and image detection and may have significant implications for the field of healthcare.

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Section: Related Workmentioning

confidence: 99%

Robust Classification and Detection of Big Medical Data Using Advanced Parallel K-Means Clustering, YOLOv4, and Logistic Regression

Awad

Hamad

Alzubaidi

2023

Life

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“…They applied hand-crafted feature extraction to make the data more convenient and optimized the features by using stacked auto-encoder and principal component analysis techniques. Al-Zubaidi et al ( 37 ) applied CNNs for the classification of COVID-19 images. They used Google-Net for training and extracting automated features from the images.…”

Section: Related Studymentioning

confidence: 99%

COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization

Hamza

Khan

Wang

et al. 2022

Front. Public Health

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Coronavirus disease 2019 (COVID-19) is a highly contagious disease that has claimed the lives of millions of people worldwide in the last 2 years. Because of the disease's rapid spread, it is critical to diagnose it at an early stage in order to reduce the rate of spread. The images of the lungs are used to diagnose this infection. In the last 2 years, many studies have been introduced to help with the diagnosis of COVID-19 from chest X-Ray images. Because all researchers are looking for a quick method to diagnose this virus, deep learning-based computer controlled techniques are more suitable as a second opinion for radiologists. In this article, we look at the issue of multisource fusion and redundant features. We proposed a CNN-LSTM and improved max value features optimization framework for COVID-19 classification to address these issues. The original images are acquired and the contrast is increased using a combination of filtering algorithms in the proposed architecture. The dataset is then augmented to increase its size, which is then used to train two deep learning networks called Modified EfficientNet B0 and CNN-LSTM. Both networks are built from scratch and extract information from the deep layers. Following the extraction of features, the serial based maximum value fusion technique is proposed to combine the best information of both deep models. However, a few redundant information is also noted; therefore, an improved max value based moth flame optimization algorithm is proposed. Through this algorithm, the best features are selected and finally classified through machine learning classifiers. The experimental process was conducted on three publically available datasets and achieved improved accuracy than the existing techniques. Moreover, the classifiers based comparison is also conducted and the cubic support vector machine gives better accuracy.

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“…Artificial intelligence techniques play a significant and influential role in the medical domain by developing complex algorithms that can automatically analyze and interpret large amounts of medical data [11][12][13][14][15][16]. This data includes various types of medical imaging such as MRI [17], CT-scan [18], X-ray [19], and ultrasound [20], as well as other types of medical data, such as genomics and electronic health records. The most famous of these techniques is deep learning, which is part of machine learning.…”

Section: Introductionmentioning

confidence: 99%

MobileNetV1-Based Deep Learning Model for Accurate Brain Tumor Classification

Mijwil

Doshi

Hiran

et al. 2023

Mesopotamian Journal of Computer Science

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Brain tumors are among the most dangerous diseases that lead to mortality after a period of time from injury. Therefore, physicians and healthcare professionals are advised to make an early diagnosis of brain tumors and follow their instructions. Magnetic resonance imaging (MRI) is operated to provide sufficient and practical data in detecting brain tumors. Applications based on artificial intelligence contribute a very large role in disease detection, provide incredible accuracy and assist in creating the right decisions. In particular, deep learning models, which are a significant part of artificial intelligence, have the ability to diagnose and process medical image datasets. In this concern, one of the deep learning techniques (MobileNetV1model) is utilized to detect brain disease from 1265 images gathered from the Kaggle platform. The behavior of this model is studied through four main metrics. This article deduced that this model has a significant effect in diagnosing these images from the most important metric, which is accuracy, as it gained an accuracy result of more than 97%, which is an excellent effect.

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Medical Image Classification for Coronavirus Disease (COVID-19) Using Convolutional Neural Networks

Cited by 15 publications

References 20 publications

Robust Classification and Detection of Big Medical Data Using Advanced Parallel K-Means Clustering, YOLOv4, and Logistic Regression

Robust Classification and Detection of Big Medical Data Using Advanced Parallel K-Means Clustering, YOLOv4, and Logistic Regression

COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization

MobileNetV1-Based Deep Learning Model for Accurate Brain Tumor Classification

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