Deep learning is evolving in the areas such as knowledge management, gene regulation, genome organization, and mutation effects. It helps in identifying disease symptoms, undiagnosable disease analysis, detecting introgression, estimating historical recombination rates, identifying selective sweeps, and estimating demography of population genetics. Deep learning methods can be used for disease identification, undiagnosable disease analysis, and personalized treatment recommendation datasets which will be in the order of millions. The black boxed deep neural networks can be used to learn from the data sets regarding the disease symptoms, variants, and patient’s health history to develop a deep learning model. Small variations help in identifying patterns for creating deep learning disease models. The size of the input data helps in improving the accuracy of the model. Deep variant method helps in identifying small variations in the patient’s health data. Patient’s health history and knowledge base are used to predict the disease association with symptoms model. Breast Cancer, pneumonia and other diseases can be diagnosed based on the medical images by using CNN algorithms. CNN technique consists of two steps convolution and pooling. These steps help in image reduction to basic features for image classification. Convolution helps in viewing the image in breaking it into small images. A CNN can have multiple convolution and activation layers. Convolution layer acts like a filter by applying dot product of the actual pixel input values and weights assigned. The sum of the output is used for filtering the image pixels. Activation layer which is part of CNN creates a matrix smaller than the actual image. Skin Image Analysis can be done using machine learning and computer vision. The images are analysed for prediction and prevention of the onset of skin disease. Recommendation engines based on AI algorithms are used for personalization of the treatment for skincare problems based on the user skin type.
