Browsing by Author "Uddin, MD. Zia"

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    Identification of COVID-19, Pneumonia, Lung Cancer & TB From Chest X-Ray Images: A Deep Transfer Learning Approach
    (Daffodil International University, 23-01-29) Datta, Gourab; Uddin, MD. Zia
    to Lung Cancer, Pneumonia, Tuberculosis, and COVID-19, which have a very terrible effect on the body, the patient may die and COVID-19 spreads very easily. It is possible to get rid of these diseases if they are detected as soon as possible. With the help of Artificial Intelligence detection of these types of diseases will be very easy and quick. AI can detect these types of diseases automatically and accurately. The use of this type of automated and accurate process in the medical sciences will be very beneficial in the modern era of science and technology. In our study, we use Deep Learning to detect 4 major types of lung diseases including- Lung Cancer, Pneumonia, Tuberculosis and COVID-19. We have used a total of 7,255 patients’ chest X-ray images as the dataset. The dataset is divided into 5 classes including- Pneumonia, COVID-19, Tuberculosis, Lung Cancer and Normal. Classify those diseases we used 6 very popular deep transfer learning models including- ResNet50, VGG16, EfficientNet, VGG19, MobileNet, and InceptionV3. Among them, the best accuracy has been gained in ResNet50. Based on chest X-ray images, we obtained a test accuracy of 97.65% and a training accuracy of 99.67% from ResNet50. We also obtained trained accuracy of 97.44%, 97.23%, 96.82%, 94.33% and 90.73% respectively in VGG16, Efficient Net, VGG19, Mobile Net and Inception-V3.