A Novel Aided Diagnosis Schema for COVID 19 Using Convolution Neural Network

Mahdi, Mohammed Salih; Abid, Yaser M.; Omran, Alaa Hamza; Abdul-Majeed, Ghassan H.

doi:10.1088/1757-899x/1051/1/012007

Cited by 8 publications

(6 citation statements)

References 10 publications

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“…Where: p represents the perimeter of the tumor, and A represents the area of tumor in units called pixels. The index of the irregularity equals 1 only for a circular shape, and it is < 1 for all other shapes [14].…”

Section: Geometrical Featuresmentioning

confidence: 99%

Using K-mean Clustering to Classify the Kidney Images

Hassan,

Ali,

Shehab

et al. 2023

Iraqi Journal of Science

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This study has applied digital image processing on three-dimensional C.T. images to detect and diagnose kidney diseases. Medical images of different cases of kidney diseases were compared with those of healthy cases. Four different kidneys disorders, such as stones, tumors (cancer), cysts, and renal fibrosis were considered in additional to healthy tissues. This method helps in differentiating between the healthy and diseased kidney tissues. It can detect tumors in its very early stages, before they grow large enough to be seen by the human eye. The method used for segmentation and texture analysis was the k-means with co-occurrence matrix. The k-means separates the healthy classes and the tumor classes, and the affected parts were isolated from the healthy parts. To isolate the kidney from the other anatomical parts in a CT image, a mask must be generated, which is a binary image (0s or 1s). This mask was also utilized to remove undesired characteristics from the images. Density slicing was utilized to color the image based on its texture density. A slice is considered a band of neighboring gray levels in a gray scale image seen through monocular color. The gray scale band of (0-255) is transformed into a variety of color slices; it is the conversion of a gray scale image to a colored image that efficiently displays symmetric and diverse regions. Density slicing is a property process for segmentation. The unsupervised classification process, the K-Mean clustering, is used the application of K-mean on C.T. images to detect and classify the type of tumor in the kidney. The K-mean clustering separates each class depending on the texture properties and the distance from each class and color. This method of segmentation was used to separate the affected part from the healthy part of the tissue; the K-mean with Co-occurrence matrices gives statistical properties such as energy, homogeneity, contrast, and correlation. These give an indication of the nature of the tissues that are different in density. The standard deviation for the cancer was higher than the stone, so was the mean, the contrast and the correlation. This means that the texture of the cancer was brighter and has a none of grey level more than the stone and this can be seen from the energy value; the texture of the cancer was highly correlated. This method proved to be a good method for the early diagnosis.

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Section: Geometrical Featuresmentioning

confidence: 99%

Using K-mean Clustering to Classify the Kidney Images

Hassan,

Ali,

Shehab

et al. 2023

Iraqi Journal of Science

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“…A low-contrast image has a narrow histogram typically in the middle of the intensity scale. In the high-contrast image, the histogram has a wide range of intensity scales, and the distribution of pixels extends along the scale, with very few vertical lines [18], As shown in figure 11 [18].…”

Section: Histogram Processingmentioning

confidence: 99%

Mask Laws to study Texture Features of the Kidney Infection

Hassan,

Ali,

Shehab

et al. 2023

Iraqi Journal of Science

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This paper aims to early study of detection and diagnosis of kidney tumors and kidney stones using Computed Tomography Scanning CT scan images by digital image processing. Computerized Axial Tomography (CAT) is a special medical imaging technique that provides images with 3D, including much information about the body's construction consisting of bones and organs. A C.T scan uses X-rays to create cross-sectional images of the body and gives the doctor a full explanation of the diagnosis of the situation through the examination. It has been used in five cases of kidney images, including healthy, stones, tumors (cancer), cystic and renal fibrosis. The masking procedure is used to separate the required C.T. images from undesirable ones. The segmentation processes which was used in this study is a thresholding algorithm, applied on C.T. images, used to detect unhealthy tissues (tumor and stone). The results showed that the system is able to distinguish between different tumors of the kidney of cases mentioned above. It's important for the instructor to determine the area of the tumor and stone to avoid damaging the healthy tissue during the treatment. The histogram which was used to study the differences between the healthy and unhealthy depending on the intensity of the image

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“…This led to the development of a robot that is capable of detecting the face of any human being and processing the data depending on the needs. As depicted in the process model, it begins by extracting the input image and its features using 3 × 3 matrices as Convolution (ConV) across a stride of 1 [11]. Featured maps are then created by taking the dot product of the layers that came before them in ConV and combining them into a single map as the result of this method.…”

Section: Related Workmentioning

confidence: 99%

Automatic Face Mask Detection System in Public Transportation in Smart Cities Using IoT and Deep Learning

Tamilarasan¹,

Rajmohan²,

Pavithra³

et al. 2022

Electronics

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The World Health Organization (WHO) has stated that the spread of the coronavirus (COVID-19) is on a global scale and that wearing a face mask at work is the only effective way to avoid becoming infected with the virus. The pandemic made governments worldwide stay under lock-downs to prevent virus transmissions. Reports show that wearing face masks would reduce the risk of transmission. With the rise in population in cities, there is a greater need for efficient city management in today’s world for reducing the impact of COVID-19 disease. For smart cities to prosper, significant improvements to occur in public transportation, roads, businesses, houses, city streets, and other facets of city life will have to be developed. The current public bus transportation system, such as it is, should be expanded with artificial intelligence. The autonomous mask detection and alert system are needed to find whether the person is wearing a face mask or not. This article presents a novel IoT-based face mask detection system in public transportation, especially buses. This system would collect real-time data via facial recognition. The main objective of the paper is to detect the presence of face masks in real-time video stream by utilizing deep learning, machine learning, and image processing techniques. To achieve this objective, a hybrid deep and machine learning model was designed and implemented. The model was evaluated using a new dataset in addition to public datasets. The results showed that the transformation of Convolution Neural Network (CNN) classifier has better performance over the Deep Neural Network (DNN) classifier; it has almost complete face-identification capabilities with respect to people’s presence in the case where they are wearing masks, with an error rate of only 1.1%. Overall, compared with the standard models, AlexNet, Mobinet, and You Only Look Once (YOLO), the proposed model showed a better performance. Moreover, the experiments showed that the proposed model can detect faces and masks accurately with low inference time and memory, thus meeting the IoT limited resources.

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A Novel Aided Diagnosis Schema for COVID 19 Using Convolution Neural Network

Cited by 8 publications

References 10 publications

Using K-mean Clustering to Classify the Kidney Images

Using K-mean Clustering to Classify the Kidney Images

Mask Laws to study Texture Features of the Kidney Infection

Automatic Face Mask Detection System in Public Transportation in Smart Cities Using IoT and Deep Learning

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