scholarly journals Computer-Aided Diagnosis of COVID-19 CT Scans Based on Spatiotemporal Information Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Tianyi Li ◽  
Wei Wei ◽  
Lidan Cheng ◽  
Shengjie Zhao ◽  
Chuanjun Xu ◽  
...  
Keyword(s):  
Information Fusion ◽  
Large Scale ◽  
Computer Aided Diagnosis ◽  
Ct Scans ◽  
Diagnostic Screening ◽  
Spatiotemporal Information ◽  
Average Accuracy ◽  
Computer Aided ◽  
Aided Diagnosis

Coronavirus disease (COVID-19) is highly contagious and pathogenic. Currently, the diagnosis of COVID-19 is based on nucleic acid testing, but it has false negatives and hysteresis. The use of lung CT scans can help screen and effectively monitor diagnosed cases. The application of computer-aided diagnosis technology can reduce the burden on doctors, which is conducive to rapid and large-scale diagnostic screening. In this paper, we proposed an automatic detection method for COVID-19 based on spatiotemporal information fusion. Using the segmentation network in the deep learning method to segment the lung area and the lesion area, the spatiotemporal information features of multiple CT scans are extracted to perform auxiliary diagnosis analysis. The performance of this method was verified on the collected dataset. We achieved the classification of COVID-19 CT scans and non-COVID-19 CT scans and analyzed the development of the patients’ condition through the CT scans. The average accuracy rate is 96.7%, sensitivity is 95.2%, and F1 score is 95.9%. Each scan takes about 30 seconds for detection.

scholarly journals A Rapid Artificial Intelligence-Based Computer-Aided Diagnosis System for COVID-19 Classification from CT Images

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Hassaan Haider Syed ◽  
Muhammad Attique Khan ◽  
Usman Tariq ◽  
Ammar Armghan ◽  
Fayadh Alenezi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Aided Diagnosis ◽  
High Rate ◽  
Ct Scans ◽  
Computational Time ◽  
False Alarms ◽  
High Resolution Computed Tomography ◽  
Computer Aided ◽  
Aided Diagnosis

The excessive number of COVID-19 cases reported worldwide so far, supplemented by a high rate of false alarms in its diagnosis using the conventional polymerase chain reaction method, has led to an increased number of high-resolution computed tomography (CT) examinations conducted. The manual inspection of the latter, besides being slow, is susceptible to human errors, especially because of an uncanny resemblance between the CT scans of COVID-19 and those of pneumonia, and therefore demands a proportional increase in the number of expert radiologists. Artificial intelligence-based computer-aided diagnosis of COVID-19 using the CT scans has been recently coined, which has proven its effectiveness in terms of accuracy and computation time. In this work, a similar framework for classification of COVID-19 using CT scans is proposed. The proposed method includes four core steps: (i) preparing a database of three different classes such as COVID-19, pneumonia, and normal; (ii) modifying three pretrained deep learning models such as VGG16, ResNet50, and ResNet101 for the classification of COVID-19-positive scans; (iii) proposing an activation function and improving the firefly algorithm for feature selection; and (iv) fusing optimal selected features using descending order serial approach and classifying using multiclass supervised learning algorithms. We demonstrate that once this method is performed on a publicly available dataset, this system attains an improved accuracy of 97.9% and the computational time is almost 34 (sec).

Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Saliha Zahoor ◽  
Ikram Ullah Lali ◽  
Muhammad Attique Khan ◽  
Kashif Javed ◽  
Waqar Mehmood
Keyword(s):  
Breast Cancer ◽  
Computer Aided Diagnosis ◽  
Diagnose Breast Cancer ◽  
Initial Stage ◽  
Computer Aided ◽  
Diagnosis And Classification ◽  
And Performance ◽  
Tools And Techniques ◽  
Aided Diagnosis

: Breast Cancer is a common dangerous disease for women. In the world, many women died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues there are several techniques and methods. The image processing, machine learning and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to survive the women's life. To detect the breast masses, microcalcifications, malignant cells the different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have been reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for the survival of women’s life it is essential to improve the methods or techniques to diagnose breast cancer at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

Fr612 INFLUENCE OF COMPUTER AIDED DIAGNOSIS WITH AUGMENTED VISUALIZATION ON ENDOSCOPIC CLASSIFICATION OF COLORECTAL POLYPS

2021 ◽  
Vol 160 (6) ◽  
pp. S-376
Author(s):  
Eladio Rodriguez-Diaz ◽  
Gyorgy Baffy Wai-Kit Lo ◽  
Hiroshi Mashimo ◽  
Aparna Repaka ◽  
Alexander Goldowsky ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Colorectal Polyps ◽  
Computer Aided ◽  
Endoscopic Classification ◽  
Aided Diagnosis

scholarly journals Segmentation and Classification of Breast Cancer Tumour

2018 ◽  
Vol 2 (1) ◽  
pp. 14-18
Author(s):  
Gokalp Cinarer ◽  
Bulent Gursel Emiroglu ◽  
Ahmet Hasim Yurttakal
Keyword(s):  
Breast Cancer ◽  
Texture Features ◽  
Computer Aided Diagnosis ◽  
Wavelet Approximation ◽  
Proposed Model ◽  
Svm Algorithm ◽  
Computer Aided ◽  
Magnetic Resonance Imaging Mri ◽  
Aided Diagnosis

Breast cancer is cancer that forms in the cells of the breasts. Breast cancer is the most common cancer diagnosed in women in the world. Breast cancer can occur in both men and women, but it's far more common in women. Early detection of breast cancer tumours is crucial in the treatment. In this study, we presented a computer aided diagnosis expectation maximization segmentation and co-occurrence texture features from wavelet approximation tumour image of each slice and evaluated the performance of SVM Algorithm. We tested the model on 50 patients, among them, 25 are benign and 25 malign. The 80% of the images are allocated for training and 20% of images reserved for testing. The proposed model classified 2 patients correctly with success rate of 80% in case of 5 Fold Cross-Validation  Keywords: Breast Cancer, Computer-Aided Diagnosis (CAD), Magnetic Resonance Imaging (MRI);

Computer-aided Diagnosis for the Detection and Classification of Lung Cancers on Chest Radiographs

2006 ◽  
Vol 13 (8) ◽  
pp. 995-1003 ◽  
Author(s):  
Junji Shiraishi ◽  
Hiroyuki Abe ◽  
Feng Li ◽  
Roger Engelmann ◽  
Heber MacMahon ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Chest Radiographs ◽  
Lung Cancers ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms

2018 ◽  
Vol 46 (9) ◽  
pp. 1419-1431 ◽  
Author(s):  
Gopichandh Danala ◽  
Bhavika Patel ◽  
Faranak Aghaei ◽  
Morteza Heidari ◽  
Jing Li ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Breast Masses ◽  
Contrast Enhanced ◽  
Computer Aided ◽  
Aided Diagnosis
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