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.
Blood-related diseases are one of the most widespread and rampant vector-borne diseases in tropical countries like India. With an ever-increasing population and enormous stress on resources like land and water, new avenues open for insects like mosquitoes to breed and propagate the virus. The traditional lab method for the detection of diseases in a human's anatomy involves extracting the blood and subjecting it to various tests to count and detect the number of blood cells. An abnormally low platelet count would indicate the presence of the virus in the body. The usual method undertaken by labs all over the world is the use of the conventional chemical procedures, which may take a few hours to produce the result. The proposed system for the low cost estimating of RBC and WBC is developed using image processing techniques and canny edge detection algorithm. The obtained results are analysed and compared with the conventional methods, and results are obtained with an accuracy of 91.2.
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