scholarly journals Comparative Analysis of White Blood Cell Cancer Detection Using Image Processing and IoT

2021 ◽  
pp. 198-201
Author(s):  
Dr. T. Loganayagi ◽  
Sindhu. K ◽  
Sripriyadharshini. S
Keyword(s):  
Image Processing ◽  
Comparative Analysis ◽  
Blood Cell ◽  
Cancer Detection ◽  
Blood Cells ◽  
Cell Cancer ◽  
White Blood Cells ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
New Applications

Computerized analysis of white blood cells tumor such as Leukemia and Myeloma is an essential testing biomedical investigate point. Herein, a comparative analysis of image processing algorithms to detect the cancer is made and patient’s health is monitored using IOT also analyzed. The work could be useful for developing and exploring the new applications of image processing in IOT based systems.

scholarly journals Comparative Analysis of Deep Learning Image Detection Algorithms

2020 ◽  
Author(s):  
Shrey Srivast ◽  
Amit Vishvas Divekar ◽  
Chandu Anilkumar ◽  
Ishika Naik ◽  
Ved Kulkarni ◽  
...  
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Visual Media ◽  
Image Detection ◽  
Detection Algorithms ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
The Moment

Abstract As humans, we do not have to strain ourselves when we interpret our surroundings through our visual senses. From the moment we begin to observe, we unconsciously train ourselves with the same set of images. Hence, distinguishing entities is not a difficult task for us. On the contrary, computer views all kinds of visual media as an array of numerical values. Due to this contrast in approach, they require image processing algorithms to examine the contents of images. This project presents a comparative analysis of 3 major image processing algorithms: SSD, Faster R-CNN, and YOLO. In this analysis, we have chosen the COCO dataset. With the help of the COCO dataset, we have evaluated the performance and accuracy of the three algorithms and analysed their strengths and weaknesses. Using the results obtained from our implementations, we determine the differences between how each algorithm runs and suitable applications for each. The parameters for evaluation are accuracy, precision, F1 score.

scholarly journals Microview: a medical application focusing on the diagnosis of diseases

2015 ◽  
Author(s):  
Alexandros Michopoulos ◽  
George Ntakakis ◽  
Stamoulis Zamanis ◽  
Efthimia Petinaki
Keyword(s):  
Mobile Technology ◽  
Blood Cells ◽  
Biological Sample ◽  
Biological Fluids ◽  
White Blood Cells ◽  
Medical Application ◽  
Diagnostic Tools ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Diagnosis Of Diseases

MicroView was made to make the connection between medicine and mobile technology reality, in order to achieve that we transform the widely used mobile devices (smartphones, tablets) into diagnostic tools. MicroView is a medical application that focuses on the diagnosis of diseases whose causes or results can be detected in biological fluids such as CSF, urine and blood. The diagnosis is achieved by innovative image processing algorithms that detect the presence and the number of the white blood cells in the biological sample. The app has been tested in 37 urine and 23 CSF samples. The accuracy of the app was 92%. In conclusion the app offers a quicker, easier and friendlier diagnosis to young scientists but also experienced professionals can be benefit from it, since all images are in electronic form and can provide a way of result confirmation.

IMAGE SEGMENTATION TECHNIQUES FOR RED BLOOD CELL : ON OVERVIEW

2015 ◽  
Vol 77 (6) ◽  
Author(s):  
Laghouiter Oussama ◽  
M. Mahadi Abdul Jamil ◽  
Wan Mahani Hafiza Bt. Wan Mahmud
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Blood Cell ◽  
Blood Cells ◽  
Medical Image Analysis ◽  
White Blood Cells ◽  
Critical Factor ◽  
Processing Technique ◽  
Image Processing Technique ◽  
The Common

Image processing technique applies in different domains, such as medical, remote sensing and security. This techniques Aims to get a simple image called -image processed- should retain maximum useful information. The sensitive step in image processing is segmentation of image. Segmentation is first stage in medical image analysis seeded to two categories supervised and unsupervised technique. Accuracy of this stage affects the whole system performance. This paper present some methods applied for blood cell image segmentation and compares previous studies of overlapping cell division method. The common goal about this area is accuracy of counting the number of red blood cells (RBC) or white blood cells (WBC), which decrease with effect of some diseases such as anemia and leukemia. And makes it a critical factor in patient treatments.

scholarly journals An Exploration of the Image Processing Techniques for the Detection of Leukemia

2018 ◽  
Vol 7 (2) ◽  
pp. 96-99
Author(s):  
A. Premnath ◽  
V. S. Meenakshi
Keyword(s):  
Image Processing ◽  
Bone Marrow ◽  
Blood Cell ◽  
Diagnostic Method ◽  
Cell Classification ◽  
Image Processing Techniques ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Processing Techniques

In the pathological diagnostic method, categorization of blood cell has more essential to detect and analyze the disease. The complications that are connected with blood can be distributed only after the blood cell classification. The illness that begins with the bone marrow is the Leukemia. Therefore, it must be handled at the beginning step and proceeds to death if continuing untreated. This present research elucidates an investigation of diagnosing leukemia from microscopic blood image exhausting various image processing algorithms.

scholarly journals Comparative analysis of deep learning image detection algorithms

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Shrey Srivastava ◽  
Amit Vishvas Divekar ◽  
Chandu Anilkumar ◽  
Ishika Naik ◽  
Ved Kulkarni ◽  
...  
Keyword(s):  
Image Processing ◽  
Comparative Analysis ◽  
Visual Media ◽  
Single Shot ◽  
Image Detection ◽  
Testing Environment ◽  
Detection Algorithms ◽  
Shot Detection ◽  
Image Processing Algorithms ◽  
Processing Algorithms

AbstractA computer views all kinds of visual media as an array of numerical values. As a consequence of this approach, they require image processing algorithms to inspect contents of images. This project compares 3 major image processing algorithms: Single Shot Detection (SSD), Faster Region based Convolutional Neural Networks (Faster R-CNN), and You Only Look Once (YOLO) to find the fastest and most efficient of three. In this comparative analysis, using the Microsoft COCO (Common Object in Context) dataset, the performance of these three algorithms is evaluated and their strengths and limitations are analysed based on parameters such as accuracy, precision and F1 score. From the results of the analysis, it can be concluded that the suitability of any of the algorithms over the other two is dictated to a great extent by the use cases they are applied in. In an identical testing environment, YOLO-v3 outperforms SSD and Faster R-CNN, making it the best of the three algorithms.

scholarly journals Microview: a medical application focusing on the diagnosis of diseases

2015 ◽  
Author(s):  
Alexandros Michopoulos ◽  
George Ntakakis ◽  
Stamoulis Zamanis ◽  
Efthimia Petinaki
Keyword(s):  
Mobile Technology ◽  
Blood Cells ◽  
Biological Sample ◽  
Biological Fluids ◽  
White Blood Cells ◽  
Medical Application ◽  
Diagnostic Tools ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Diagnosis Of Diseases

MicroView was made to make the connection between medicine and mobile technology reality, in order to achieve that we transform the widely used mobile devices (smartphones, tablets) into diagnostic tools. MicroView is a medical application that focuses on the diagnosis of diseases whose causes or results can be detected in biological fluids such as CSF, urine and blood. The diagnosis is achieved by innovative image processing algorithms that detect the presence and the number of the white blood cells in the biological sample. The app has been tested in 37 urine and 23 CSF samples. The accuracy of the app was 92%. In conclusion the app offers a quicker, easier and friendlier diagnosis to young scientists but also experienced professionals can be benefit from it, since all images are in electronic form and can provide a way of result confirmation.

Otoconia perfect/imperfect crystals

1994 ◽  
Vol 52 ◽  
pp. 42-43
Author(s):  
César D. Fermin ◽  
Dale Martin
Keyword(s):  
Image Processing ◽  
Room Temperature ◽  
Phosphotungstic Acid ◽  
Gallus Domesticus ◽  
Crystal Matrix ◽  
Organic Templates ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Diffraction Patterns

Otoconia of higher vertebrates are interesting biological crystals that display the diffraction patterns of perfect crystals (e.g., calcite for birds and mammal) when intact, but fail to produce a regular crystallographic pattern when fixed. Image processing of the fixed crystal matrix, which resembles the organic templates of teeth and bone, failed to clarify a paradox of biomineralization described by Mann. Recently, we suggested that inner ear otoconia crystals contain growth plates that run in different directions, and that the arrangement of the plates may contribute to the turning angles seen at the hexagonal faces of the crystals.Using image processing algorithms described earlier, and Fourier Transform function (2FFT) of BioScan Optimas®, we evaluated the patterns in the packing of the otoconia fibrils of newly hatched chicks (Gallus domesticus) inner ears. Animals were fixed in situ by perfusion of 1% phosphotungstic acid (PTA) at room temperature through the left ventricle, after intraperitoneal Nembutal (35mg/Kg) deep anesthesia. Negatives were made with a Hitachi H-7100 TEM at 50K-400K magnifications. The negatives were then placed on a light box, where images were filtered and transferred to a 35 mm camera as described.

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