Classification of Kidney Diseases Using Transfer Learning
Sachin Kumar Saxena, Jitendra Nath Shrivastava, Gaurav Agarwal, Sanjay Kumar · Advances in computational intelligence and robotics book series · 2023
A urologist confirms high risks of kidney stones just because of diabetic mellitus; however, other factors also exist, but a major cause is type 2 diabetes. Renal cyst and diabetes clinical features show 58% of affected subjects as the same. Research findings prove the high risk of renal cancer among diabetes patients. All these patients underwent abdominal MRI or CT scan to extract kidney high-definition 3D images. The dataset was gathered from two hospitals: the first is the SRMS IMS, and the second is the Bareilly MRI & CT Scan Centre, both located in the city of Bareilly in the state of Uttar Pradesh of India. Research has been analyzed to note the classification among four classes using seven transfer learning methods. Results have been compared with seven transfer learning methods. The methods are EfficientNetB0, Xception, VGG16, ResNet50, MobileNet, InceptionV3, DenseNet121. Out of these deep learning-based algorithms, EfficientNetB0 shows the best accuracy of 96.02%.