A Deep Learning Model for Multiple Disease Prediction using VGG-16

A Deep Learning Model for Multiple Disease Prediction using VGG-16

Nemani Anil

                 ISE department

Acharya Institute of Technology Bangalore

nemanin.19.beis@acharya.ac.in

Harsha Vardhana

ISE department

Acharya Institute of Technology Bangalore

harshal.19.beis@acharya.ac.in

Preethi L

ISE department

Acharya Institute of Technology Bangalore

preethil.19.beis@acharya.ac.in

Kiran Raju L

ISE department

Acharya Institute of Technology Bangalore

kirank.19.beis@acharya.ac.in

Abstract— All health-related problems must be precisely and professionally examined if it is to be prohibited and examined. In the case of a captious illness, the common technique of diagnosis might not be adequate. Early diagnosis of any health issue is crucial, as earlier disease discovery may result in more successful treatments or longer survival times.

We may have serious illness, and if we do not address these symptoms right away, it may be too late to reverse the condition. The establishment of a Deep Learning (DL)-based medical analysis systems for disease forecast can lead to a prognosis that is more accurate than one made using historical techniques. Currently, the suggested approach can forecast Covid-19, Brain tumor, Breast Cancer, Alzheimer, Diabetes, Pneumonia. We can employ, a NN (Neural Network) known as CNN (Convolution Neural Network), which VGG 16 architectures for deep learning (visual geometry group) and random forest. For the early detection of a sickness, our diagnostic model can serve as a doctor, enabling timely treatment and the possibility of saving lives.

Keywords- CNN (Convolutional neural networks), VGG-16, Deep Learning, Machine Learning and Random Forest.