The world is experiencing an increased life expectancy, which results in a natural increase in the chance of getting a disease. The main concern is that some of the methods to determine an affectation are not so fast and need expert people. Therefore, it is necessary to create new low-cost mechanisms of diagnosis that can give us fast and better results.
Recent studies have been implemented using known architectures getting high scores of accuracies. An experimental classification model was implemented in this work using Python libraries. This is an experimental model with custom neural network architecture. This work intends to contrast the results using a model based on the AlexNet against my experimental architecture.
The 2 main reasons to compare my work versus AlexNet is that during my investigation of the state of the art I did not find researches to solve the DR categorization using this architecture and also if I had chosen other architecture, I would need more powerful computing. In the end, AlexNet was not a good solution.
This solution will help the healthcare industry to have a less expensive and non-invasive way to determine if a person is being affected by diabetic retinopathy, depending on the damage shown on their retinasITESO, A. C
