ABSTRACT
Deep learning is a subfield of machine learning (ML) that offers flexibility and understanding of concepts by learning to represent the universe as a nested hierarchy of concepts. These concepts are connected to each other by means of elements called neurons or neural network. In human brains, high-level information is connected by means of connections called synapses or elements called neurons. Nowadays, huge volume of data is being processed in hospital and clinics, wherein the handling of these data gives a tough time in predicting diseases and symptoms to the patients. The electronic system records and the information is collected in these records are mixed up. So with the help of a standard analytic method it is impossible to analyze each and every value and data that we obtain. In order to overcome these problems, conventional techniques of ML are used. In this, the computer systems are trained to predict and prescribe the medicines to the patients. In drug discovery and predicting the drugs to every individual their day-to-day activities are noted and with the help of the genomic activity and the data available for each person will be prescribed with a different dosage of drug according to the genomes and their activities. The algorithm used for the prediction of diseases is deep convolution neural network that trains the system and provides a test report and pattern recognition is used to 2distinguish the images obtained from the CT, X-ray, and MRI techniques and provide a report for certain types of tumors.
