CNN googlenet and alexnet architecture deep learning for diabetic retinopathy image processing and classification

The aim of this study is to analyze the performance of two widely used convolutional neural network (CNN) architectures, namely AlexNet and GoogLeNet, to determine their relative advantages and disadvantages regarding classification of photographic images. The purpose of this test is to assess how effectively children can recognize and prepare for various types of pictures. It covers a wide range of topics such as experiments and analysis, implementation of algorithms, various preprocessing methods, continuation of algorithms, and various fine-tuning strategies. Examinations are made in various educational period environments and the findings and graphics obtained from these reviews are compared. The findings show that both AlexNet and GoogLeNet have the ability to be used in the photo classification process. AlexNet already has a performance advantage over its competitors after only six rounds of training. Initially, GoogleNet wasn't quite as accurate as Caffe, but it soon caught on, dealing with massive training repetitions. Overall, the study's findings imply that choosing the appropriate CNN architecture should be driven by necessity rather than personal taste. AlexNet offers high accuracy with less epochs, while GoogLeNet shows potential for higher performance with more training. This research not only helps academics and practitioners make more educated decisions when choosing models for image classification tasks, it also increases our understanding of CNN architectures.