Use of Deep Learning for Disease Detection and Diagnosis

A dynamic research area which is progressively growing its importance in the field of medical diagnosis and detection is computer-aided diagnosis and detection. Suppose we are in a locality which happens to be very far away from healthcare centre or we are not financially strong enough to pay our hospital bill or we lack time to take sick leaves from our workplaces. In such scenario, the diagnosis of diseases by the help of high-end sophisticated tools can be very fruitful. A lot of AI algorithms have been proposed and developed by computer science scientists for the detection and diagnosis of diseases such as cancer, diseases of lung, rheumatoid arthritis, diabetic retinopathy, diseases of heart, Alzheimer’s disease, hepatitis, dengue, liver disease and Parkinson’s disease. Improved perception accuracy and disease diagnosis are some of the points put up by the recent machine learning researchers. Compared to the traditional computation algorithms, deep learning algorithms are way more effective in disease detection and diagnosis. Deep learning involves the usage of large neural networks that have neurons connected to each other that have the ability to modify their hyper-parameters whenever updated new data comes in. It is that technology which makes the computer systems able to learn things themselves without explicit programming from human side. In this study, we have mentioned the recent developments and trends in the deep learning field which can make a great impact for efficient detection and diagnosis of several types of diseases. Our chapter deals with exploring deep learning usage in efficient diagnosis of certain disease risk factors thereby assisting medical experts in precise decision-making. Also, two different case studies are discussed in detail to highlight the contribution of deep learning techniques in disease diagnosis.