VGG16-Based Deep Learning Framework for Accurate Kidney Disease Classification Using CT Scan Images
Keywords:
Kidney Disease Classification, CT Imaging, Deep Learning, Transfer Learning, VGG16, Convolutional Neural Network (CNN), Medical Image Analysis, Automated DiagnosisAbstract
However, kidney disorders such as cysts, stones, and tumours look very similar to each other in a CT scan, making diagnosis a challenge. This research proposes a transfer learning-based deep learning framework for diagnosing multi-class kidney diseases using VGG16 architecture. The dataset included 12,446 CT images, of which 8,710 was for training, 1,865 for validation and 1,871 for testing, under the 4 classes, namely: Normal, Cyst, Stone and Tumour. To generalize further and prevent overfitting, dense and dropout layers were added on top of the pre-trained VGG16 model. The experimental results manifest better classification performance with the training accuracy of 98.86%, validation accuracy of 99.46% and a test accuracy of 99.63%. The highly robust and discriminative nature of the proposed framework was illustrated by accurate, low false positive classification of renal pathologies. This is the first study to show the promise of deep learning based convolutional neural networks in automating kidney disease diagnosis and augmenting clinical decision making in radiology workflows.
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