Detection of Blood-Related Diseases Using Deep Neural Nets
B. K. Rajithkumar, D. R. Shilpa, B. V. Uma · Advances in computational intelligence and robotics book series · 2019
Image processing offers medical diagnosis and it overcomes the shortcomings faced by traditional laboratory methods with the help of intelligent algorithms. It is also useful for remote quality control and consultations. As machine learning is stepping into biomedical engineering, there is a huge demand for devices which are intelligent and accurate enough to target the diseases. The platelet count in a blood sample can be done by extrapolating the number of platelets counted in the blood smear. Deep neural nets use multiple layers of filtering and automated feature extraction and detection and can overcome the hurdle of devising complex algorithms to extract features for each type of disease. So, this chapter deals with the usage of deep neural networks for the image classification and platelets count. The method of using deep neural nets has increased the accuracy of detecting the disease and greater efficiency compared to traditional image processing techniques. The method can be further expanded to other forms of diseases which can be detected through blood samples.