Advanced technology such as microrobots and nanorobots have the potential to completely transform the healthcare industry. These tiny robotic devices provide fine control for a range of biological applications since t...
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Dysgraphia is a handwriting problem that impairs a person’s ability to write. Even the diagnosis of this condition is challenging, and there is currently no cure. Researchers from all over the world have studied this...
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Brain tumors are critical life-threatening medical condition that requires timely and accurate diagnosis for effective treatment. Magnetic Resonance Imaging (MRI) is a widely used and non-invasive medical imaging tech...
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ISBN:
(纸本)9798350328042
Brain tumors are critical life-threatening medical condition that requires timely and accurate diagnosis for effective treatment. Magnetic Resonance Imaging (MRI) is a widely used and non-invasive medical imaging technique for the detection and diagnosis of brain tumors. In recent years, deep learning approaches, particularly Convolutional Neural Networks (CNNs), have shown remarkable success in medical image analysis, including brain tumor detection. This paper presents a novel approach for brain tumor detection using a Modified Convolutional Neural Network (MCNN) on MRI images. The proposed solution will utilize a deep learning architecture that employs Convolutional Neural Networks (CNNs) for feature extraction and classification. The MCNN architecture consists of a deep Convolutional Neural Networks with a unique combination of convolutional layers, pooling layers, and fully connected layers. Furthermore, we introduce several modifications to the traditional CNN architecture, including the additional layers to improve feature extraction and spatial attention. These modifications aim to address the challenges associated with the complex and subtle nature of brain tumor images in MRI scans. The developed system will be evaluated using standard metrics such as accuracy, sensitivity, specificity, and F1 score. The results will be compared to existing methods for brain tumor detection to demonstrate the effectiveness and potential clinical utility of the proposed approach. The proposed model's superior performance highlights its potential to assist healthcare professionals in early and accurate brain tumor diagnosis, ultimately contributing to better patient care and outcomes. In the proposed CNN model, we observed the average accuracy value on the training data is 98%, with an average loss value of 0.14181. However, the findings on the test data show a significant difference: the average accuracy value on the test data is 90%, with an average loss value of 0.44037.
The Named Data Networking (NDN) is a novel communication mechanism that may assist in enhancing efficiency of data delivery in the Internet of Things (IoT). In this letter, we propose a data pushing framework in NDN-e...
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Brain–computer interface (BCI) technology has huge potential to transform human–computer interaction across multiple disciplines. Despite efforts, achieving high accuracy in motor imagery (MI) task classification re...
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Fog computing has become the primary computing paradigm for IoT applications as it meets the low-latency needs of the growing number of IoT applications. However, the servers can get overwhelmed due to the high demand...
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Alzheimer's, the most prevalent cause of dementia, leads to a continuous decline in cognitive functions, social skills, and behavior, affecting individuals' independent functioning. As per survey 2010 in India...
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In recent decades, vehicle recognition plays an essential and effective role in the intelligent transportation system and traffic safety. Currently, the deep learning approaches made an effective impact in the fast ve...
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In the era of the Internet of Things (IoT), cloud computing huge amounts of data are generated by machines, humans and it is communicated over the internet. We need a stringent security to protect information as well ...
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Class imbalance a common challenge in machine learning, often results in skewed predictions and misrepresentative model assessments, highlighting the need for effective countermeasures. Our detailed survey dives into ...
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