A Comprehensive Understanding Advances in Deep CNN Model for Specific Medical Applications in Healthcare
DOI:
https://doi.org/10.48047/1cbr4f45Keywords:
Advanced in Deep Convolutional Neural Network (ADVANCED IN DCNN), ReLU, VGG16, ResNet-50, DenseNet-121, LeNet-5, etcAbstract
The application of Deep Convolutional Neural Networks (CNNs) has significantly Advanced the
field of medical image analysis, transforming diagnostic and prognostic methodologies in
healthcare. This paper provides an in-depth exploration of the latest developments in CNN models
for specific medical applications, such as cancer detection, brain tumor segmentation, retinal disease
diagnosis, and COVID-19 imaging. It discusses architectural advancements, challenges in training;
data scarcity, privacy concerns, and the need for explain ability in medical AI systems. The paper
concludes by highlighting emerging trends, including multi-modal approaches and explainable AI
(XAI) models, and their potential impact on healthcare. Our performance analyses demonstrate the
better execution of our strategy looked at than existing CNN architectures in terms of metrics:
accuracy, F1-score, etc.
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