Brain tumors are abnormal growths of brain cells that can be benign (non-tumor) or malignant (tumor). These tumors can arise from different types of brain cells and occur in various brain regions. Timely detection is ...
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Brain tumors are abnormal growths of brain cells that can be benign (non-tumor) or malignant (tumor). These tumors can arise from different types of brain cells and occur in various brain regions. Timely detection is crucial for reducing the severity and improving prognosis. However, the traditional human examination suffers in early tumor detection due to the irregular patterns in MRI scans. Additionally, Machine learning and deep learning- based frameworks detect brain tumors more accurately than human analysis. This work introduces an efficient diagnostic approach with improved accuracy to classify the benign and malignant from MRI scans. This diagnostic approach consists of three levels. In the first level, the majority and minority samples are increased to train the framework with more subjects using imagedatagenerator with real-time data augmentation. In the second level, a pre-trained Convolution Neural Network (CNN), namely the Xception framework, is utilized to learn comprehensive information about images. The hyperparameter tuning process improves the multi-class classification accuracy in the third level. The proposed framework classifies brain tumors into multiple such as glioma, meningioma, no tumor, and pituitary. The experimental dataset is obtained from the Kaggle repository to train the framework. The outcomes attained by the proposed framework deliberate higher accuracy compared with other CNN frameworks. The proposed framework proves its efficiency in the fine-grained classification of brain tumors with a validation accuracy of 99.87%. Thus, this framework may be employed in clinical services to diagnose brain MRI tumors.
The novel coronavirus disease has produced destructive effects on human life, taking away millions of lives. The biggest bottleneck in detecting the COVID-19-affected patient is the limited availability and time-consu...
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ISBN:
(纸本)9781665463188
The novel coronavirus disease has produced destructive effects on human life, taking away millions of lives. The biggest bottleneck in detecting the COVID-19-affected patient is the limited availability and time-consuming features of conventional RT-PCR tests and the lack of specialized sample extraction laboratories. Early detection of this virus may help in the advancement of a medication approach and disease control strategies. In this research, we have developed an Android smartphone application that can detect pneumonia and COVID-19 from chest X-ray photographs using convolutional neural network deep learning algorithms (VGG16 and VGG19). The COVID-19, pneumonia, and healthy chest X-ray images are collected from various repositories of a public database, Kaggle. After applying the data augmentation technique, 9,000 chest X-ray photographs were used for training, including 3,000 images for COVID-19, pneumonia, and normal cases. For testing, 3,000 chest X-ray photographs were collected, with 1,000 images for all three cases. VGG16 model achieved better performance than the VGG19 with a training accuracy of 98.31% and validation accuracy of 95.03%. Next, the deep learning-based automatic classification framework is deployed into a smartphone application. Finally, the application has been tested and assessed by a focused group, and analytical results have been presented.
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