scholarly journals Novel Pre-processing Stage for Classification of CT Scan Covid-19 Images

2021 ◽  
Author(s):  
D. Vijayalakshmi ◽  
Malaya Nath ◽  
Madhusudhan Mishra
Keyword(s):  
Ct Scan ◽  
Processing Stage

Novel Pre-processing Stage for Classification of CT Scan Covid-19 Images

2021 ◽  
Author(s):  
D. Vijayalakshmi ◽  
Malaya Nath ◽  
Madhusudhan Mishra
Keyword(s):  
Ct Scan ◽  
Processing Stage

scholarly journals Etiology of epilepsy a prospective study of 210 cases

1991 ◽  
Vol 49 (3) ◽  
pp. 251-254 ◽  
Author(s):  
Walter Oleschko Arruda
Keyword(s):  
Ct Scan ◽  
Neurological Examination ◽  
Epileptic Seizures ◽  
Partial Seizures ◽  
Complex Partial Seizures ◽  
Blood Tests ◽  
Generalized Seizures ◽  
A Prospective Study ◽  
Csf Examination

The objective of this study was to establish the etiology of epilepsy in 210 chronic epileptics (110 female, 100 male), aged 14-82 years (34.2±13.3). Patients less than 10 years-old and alcoholism were excluded. All underwent neurological examination, routine blood tests, EEG and CT-scan. Twenty patients (10.5%) were submitted to spinal tap for CSF examination. Neurological examination was abnormal in 26 (12.4%), the EEG in 68 (45.5%), and CT-scan in 93 (44.3%). According to the International Classification of Epileptic Seizures (1981), 101 (48.1%) have generalized seizures, 66 (31.4%) partial seizures secondarily generalized, 25 (11.8%) simple partial and complex partial seizures, and 14 (6.6%) generalized and partial seizures. Four patients (2.0%) could not be classified. In 125 (59.5%) patients the etiology was unknown. Neurocysticercosis accounted for 57 (27.1%) of cases, followed by cerebrovascular disease 8 (3.8%), perinatal damage 5 (2.4%), familial epilepsy 4 (1.9%), head injury 4 (1.9%), infective 1 (0.5%), and miscelanea 6 (2.8%).

Brain tumor detection & segmented classification of CT-scan and MRI image

2021 ◽  
pp. 290-297
Author(s):  
Sanjay Kumar ◽  
J.N. Singh ◽  
Naresh Kumar
Keyword(s):  
Brain Tumor ◽  
Ct Scan ◽  
Tumor Detection ◽  
Mri Image

Segmentation of Leukemia Cells Using Clustering

2019 ◽  
Vol 10 (2) ◽  
pp. 39-48
Author(s):  
Eman Mostafa ◽  
Heba A. Tag El-Dien
Keyword(s):  
Bone Marrow ◽  
Cancer Cells ◽  
Blood Cells ◽  
White Blood Cells ◽  
Leukemia Cells ◽  
Microscopic Image ◽  
Processing Stage ◽  
Clustering Techniques ◽  
Fuzzy C Means

Leukemia is a blood cancer which is defined as an irregular augment of undeveloped white blood cells called “blasts.” It develops in the bone marrow, which is responsible for blood cell generation including leukocytes and white blood cells. The early diagnosis of leukemia greatly helps in the treatment. Accordingly, researchers are interested in developing advanced and accurate automated techniques for localizing such abnormal blood cells. Subsequently, image segmentation becomes an important image processing stage for successful feature extraction and classification of leukemia in further stages. It aims to separate cancer cells by segmenting the microscopic image into background and cancer cells that are known as the region of interested (ROI). In this article, the cancer blood cells were segmented using two separated clustering techniques, namely the K-means and Fuzzy-c-means techniques. Then, the results of these techniques were compared to in terms of different segmentation metrics, such as the Dice, Jac, specificity, sensitivity, and accuracy. The results proved that the k-means provided better performance in leukemia blood cells segmentation as it achieved an accuracy of 99.8% compared to 99.6% with the fuzzy c-means.

Classification of Lung Cancer Stages from CT Scan Images Using Image Processing and k-Nearest Neighbours

2020 ◽  
Author(s):  
Mohd Firdaus Abdullah ◽  
Siti Noraini Sulaiman ◽  
Muhammad Khusairi Osman ◽  
Noor Khairiah A. Karim ◽  
Ibrahim Lutfi Shuaib ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Ct Scan ◽  
Nearest Neighbours

New CT Scan Classification of Leak After Sleeve Gastrectomy

2013 ◽  
Vol 23 (8) ◽  
pp. 1341-1343 ◽  
Author(s):  
M. Nedelcu ◽  
M. Skalli ◽  
E. Delhom ◽  
J. M. Fabre ◽  
D. Nocca
Keyword(s):  
Sleeve Gastrectomy ◽  
Ct Scan ◽  
Ct Scan Classification

Intensity-based statistical features for classification of lungs CT scan nodules using artificial intelligence techniques

2015 ◽  
Vol 27 (6) ◽  
pp. 737-751 ◽  
Author(s):  
Sheeraz Akram ◽  
Muhammad Younus Javed ◽  
Ayyaz Hussain ◽  
Farhan Riaz ◽  
M. Usman Akram
Keyword(s):  
Artificial Intelligence ◽  
Ct Scan ◽  
Statistical Features ◽  
Artificial Intelligence Techniques
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