Abstract:
Accurate diagnosis of kidney abnormalities, such as tumors, stones and cysts
is essential for the effective treatment of these diseases. Deep Learning has shown
great potential in improving the diagnosis of medical images. In this paper, we
present an advanced method for the classification of kidney abnormalities by using
Convolutional Neural Networks (CNN) integrated with Spatial Attention
mechanisms. Our approach focuses on improving the performance of the
classification model by identifying and focusing attention on the important areas of
the images.
Our model is trained and tested on a dataset of renal scan images. It contains
different categories of abnormalities. Experimental results show that the integration
of Spatial Attention mechanisms in CNN significantly improves the classification
performance compared to traditional methods. This approach provides a powerful
tool. It can be used by healthcare professionals to efficiently diagnose kidney
abnormalities.
